dataesr/scientific-paragraphs-categorization huggingface dataset:
A multivariate linear regression model was used for assessment of independent variables correlated with both surface and total CaSR expression. The correlation between CaSR expression and laboratory parameters (Ca, Ph, creatinine clearance, hsCRP, 25 OH vitamin D, iPTH, total cholesterol, LDL cholesterol, HDL cholesterol, HIL6, and HTNFα) was studied with the Spearman's rank correlation and a correlation was considered only when the coefficient was between 0. 41 and 1. 00 or between -0. 41 and -1. 00, with a significant P value. Twosided P values <0. 05 were considered significant in multivariate analysis. SAS software version 9. 2 (SAS Institute, Cary, NC, USA) software was used for all analyses.
There are two key challenges hindering effective use of quantitative assessment of imaging in cancer response assessment: 1) Radiologists usually describe the cancer lesions in imaging studies subjectively and sometimes ambiguously, and 2) it is difficult to repurpose imaging data, because lesion measurements are not recorded in a format that permits machine interpretation and interoperability. We have developed a freely available software platform on the basis of open standards, the electronic Physician Annotation Device (ePAD), to tackle these challenges in two ways. First, ePAD facilitates the radiologist in carrying out cancer lesion measurements as part of routine clinical trial image interpretation workflow. Second, ePAD records all image measurements and annotations in a data format that permits repurposing image data for analyses of alternative imaging biomarkers of treatment response.
A second challenge is that there is poor coordination and communication between oncologists and radiologists with respect to target lesions and their assessments (Figure  1 ). Oncologists or data managers record the target lesions and measurements in flow sheets that are usually not communicated to radiologists, who interpret each case as part of their routine workflow. Thus, the radiologist who happens to interpret the scans from a patient enrolled in a clinical trial may not necessarily describe and make quantitative measurements on all the lesions being tracked in the trial. Moreover, radiology results are recorded in a text report and in graphical annotations on the images, which are an inefficient (and sometimes unclear or ambiguous) way in which to communicate the quantitative imaging information.
Annotation and Image Markup A primary design consideration in creating ePAD was the storage of image annotations and markups. Annotations describe the results of image interpretation. For cancer studies, these annotations include the imaging time point, the name, type, anatomic location, and measurement of target lesions, the coordinates of the line drawn to make the measurements, and the identifier of the image measured. Image markups are the visual representation of the annotations (e. g. , a line drawn on the image indicating a lesion measurement) (Figure  2 ). We previously developed the AIM data model as part of an effort by the cancer Biomedical Informatics Grid project of the National Cancer Institute (Bethesda, MD)  [34, 35, [38] [39] [40] [41]. AIM is an information model for storing and sharing image metadata  [34, 40, 41].
ePAD Platform The ePAD platform comprises five main components (Figure  3 ): 1) the ePAD viewer, a Web-based image viewer and AIM annotation editor that provides a graphical user interface for viewing and recording image interpretations, measurements, and other metadata (Figure  4 ), 2) the ePAD Web Services application, which provides a programming interface to the ePAD platform resources, 3) an image database, 4) an AIM annotation database, and 5) analytical plugin modules for processing image annotations. ePAD viewer. The ePAD viewer is implemented as a "rich Web application," a software program that runs in a Web browser and provides the core features to permit users to view images and to collect and summarize lesion measurements and other aspects of the results of imaging studies. The ePAD viewer was written using HTML5  [63] , Java, JavaScript, and the Google Web Toolkit (http://www.
1 is shown in Figure  4. In addition to the preceding minimal necessary information, this template captures (automatically without user intervention) the coordinates of lines drawn on the image to measure lesions and lesion size. To provide error-checking capability, the ePAD viewer leverages the ability of AIM templates to specify constrained choices of controlled terms  [40]. All answer choices in ePAD templates are controlled terminology lists provided by the RadLex ontology  [65]. The ePAD viewer prompts the user if certain values in the templates are inconsistent or incomplete  [40]. Text entered into ePAD's templates is matched to controlled terminologies such as RadLex to prevent spelling errors and to ensure that legal terms are recorded in AIM documents. This ensures compliance with data standards and interoperability with other image metadata collected in cancer research.
studies to become aware of the lesions previously measured and that need to be measured on the current study. The ePAD viewer queries the AIM annotation database (described below) to summarize, for the radiologist, all the lesions from the prior exams in a summary table (Figure  5 ). In essence, this comprises a radiology worklist of the measurements needed to be made in each imaging study. It also links each measurement to the image from which it was obtained. When the radiologist clicks on a measurement, the corresponding image is retrieved, and the measurement is displayed as a graphical overlay. ePAD Web Services. The ePAD viewer uses a set of Web services to provide functionalities requiring persistence of data or more heavy-duty processing than can be handled by a Web application, such as authenticating user credentials, retrieving image data from the image database (see below), storing and retrieving annotations, and invoking image calculation methods that are too time consuming to be performed within the client Web-based application.
The ePAD Web Services were written using standard open-source Java Web application technologies. The ePAD Web Services provide programmatic access to the image database and AIM annotation database (described below). The ePAD Web Services are hosted on a CentOS VMware virtual machine. A typical configuration has 100 GB of disk storage and 16 GB of random access memory (RAM), though more storage or RAM can be exploited if needed. The ePAD Web Services are typically hosted on a server that resides within an institution's firewall so that all traffic between the ePAD viewer and the ePAD Web Services reside within the institution's Intranet. Thus, users can use ePAD to evaluate image data containing protected health information, provided the network on which ePAD is hosted is secure. Another model for hosting ePAD is a centralized hosted version, which could provide publicly available images (e.
g. , those disseminated publicly in national databases) to build national databases of image annotations. The ePad platform supports both environments. The ePAD Web Services are implemented as a set of RESTful Web services  [66]  that other applications use to access and modify the resources of the ePAD platform. Developers can use the ePAD Web Services to access annotations and images in their own applications or to provide extensions to the ePAD platform.
Image database. Medical images in Digital Image Communications in Medicine (DICOM) format are managed by an open-source Picture Archiving and Communication System (PACS) called dcm4chee  [67]. This PACS contains a DICOM image receiver and a programming interface that permits the ePAD Web Services to manage imaging studies within ePAD (Figure  3 ). The advantages of using dcm4chee are interoperability with other open-source projects and its robust support for open standards, such as the Integrating Healthcare Enterprise  [68]. The ePAD platform can alternatively use other types of PACS. The DICOM image database supports the following two mechanisms for importing images into ePAD: direct upload (from the local computer workstation) and the "DICOM send" method from any DICOM-compliant image workstation (e.
g. , a commercial PACS). The concept of the DICOM image database is that of an "image cache," a temporary storage depot for images, which are kept sufficiently long for image display and annotation. Because AIM annotations reference DICOM identifiers and the latter are universally unique and persistent, deleting images from the DICOM image database does not inviolate the corresponding AIM files (which are stored in ePAD's AIM database). The AIM annotations can be displayed once again if the associated DICOM images are loaded into ePAD or some other AIM-compliant workstation. A challenge with creating a Web-based image viewer such as ePAD is that DICOM images are typically large. CT studies typically have hundreds of DICOM images, and transmitting all of them from the dcm4chee database to the ePAD viewer would hamper performance significantly.
We thus designed a lossless compressed Portable Networks Graphics (PNG) image object ("packed PNG") to convey the DICOM data in compressed format. This compression mechanism takes each 16-bit pixel in a DICOM image and packs it into a PNG color channel before returning it to the ePAD viewer, where it is unpacked. The packed PNGs provide a Web-friendly, lossless approach to compressing the DICOM data and significantly reduce the volume of data provided by the server. Using the PNG format is advantageous for Web display of DICOM images, because it allows for faster upload and browser caching of image data; if the image has already been loaded in the Web browser, it is retrieved from the browser's cache and not reloaded. The ePAD Web Services preprocess the DICOM files so that the packed PNG files are available when the user opens the ePAD viewer.
The DICOM image database thus maintains two parallel image stores, the original DICOM images and the packed PNGs. The ePAD Web Services manage the packed PNGs, creating them when the DICOM database receives new images and deleting them when a DICOM study is deleted from the DICOM database. To further speed image display performance, ePAD makes use of a feature of dcm4chee called the Web-Accessible DICOM Objects  [69]  protocol to retrieve lossy Joint Photographic Experts Group ( JPG) images while the lossless packed PNGs are initially loading. Using this strategy, the image slices are scrollable quickly after the user first opens a study. If the user stops at a JPG image to start inspecting it in detail, that image is prioritized by the ePAD Web Services, which load its packed PNG and the corresponding packed.
AIM annotation database. As the user makes annotations on images, ePAD creates AIM files appropriate to the data entry template selected. All AIM annotations are stored in an XML database (eXist  [70] ). The ePAD Web Services manage annotations in the AIM annotation database using a programming interface called the AIM API (http://www. stanford. edu/group/qil/cgi-bin/mediawiki/index. php/AIM_API). The latter has functions for creating, storing, and retrieving AIM files from the AIM annotation database. As described below, the AIM annotation database is the key resource that ePAD queries for lesion tracking and summarizing longitudinal changes in cancer treatment response (Figure  3 ). Analytical modules for processing image annotations. Using the ePAD Web services, we built a query engine to retrieve AIM annotations and their associated images to enable analyses of image measurements for tracking target lesions and for evaluating cancer treatment response.
The query engine interrogates the AIM annotation database to retrieve annotations on a given patient for one or more imaging studies, processes the annotations, and displays the results (Figure  3 ). This query engine also accesses the DICOM image database to retrieve images associated with the corresponding annotations to permit displaying the images and their annotations as overlays in the ePAD viewer (Figure  4 ). Because the AIM files contain the DICOM unique identifiers of the source images from which they were derived, queries to the AIM annotation database can retrieve the image(s) associated with any AIM annotation. We created the following two application modules: 1) summary of lesion measurements across longitudinal imaging studies, to help radiologists to review the measurements made in prior imaging studies when interpreting a follow-up imaging study, and 2) calculation and analysis of aggregate imaging data, to help oncologists to understand patient response assessment and cohort evaluation.
Summary of lesion measurements. When the user opens a patient study, the ePAD Web Services query the AIM annotation database to populate a panel in the ePAD viewer that shows the user all of the lesions from the prior imaging studies for the patient in a Patient Lesion Summary Table  (Figure   6 ). It also links each measurement to the image from which it was obtained. When the user clicks on a measurement in the ePAD viewer, the corresponding image is retrieved, and the measurement is displayed as a graphical overlay. In addition to summarizing each lesion and its measurement across all imaging time points, the ePAD Web Services also perform automated calculations and inferences in accordance to response criteria. For example, for RECIST, it sums the long axis of the lesions for which the lesion type is "target lesion" (producing the SLD), as well as an image-based response rate (the percentage of change in the SLD compared with baseline).
It then applies the RECIST rules to classify the response rate to determine the response category (i. e. , stable disease, partial response, complete response, and progressive disease). This information is displayed with the lesion measurements in the ePAD viewer (Figure  6 ). Calculation and analysis of imaging data for response assessment. We created a module that uses the ePAD Web Services to query the AIM annotation database to produce automated summaries of lesion measurements at each study time point  [71]. These summaries comprise a quantitative imaging report, which can help the oncologist quickly amass the measurements on target lesions and the changes in tumor burden in a patient across longitudinal imaging studies. This application, implemented as a separate Web application in the ePAD platform, was designed to address the needs that oncologists have in assessing treatment response, which we previously described  [22].
1 criteria]. The radiologist was able to bring up any of previously annotated target lesions by consulting the Patient Findings Table in the ePAD viewer (Figure  6 ). When doing the read during the session without ePAD, they had to bring up the images and locate the previous measurements by hand to determine which target lesions were measured previously. The radiologist's reading time for the assisted and unassisted reading of the imaging studies for each patient, as well as the mean, median, and SD of the reading times for all patients, was calculated for assisted and unassisted reading sessions. Significance of the differences in the Figure  7. ePAD quantitative imaging report. This report is generated automatically by the ePAD Web Services, which query its AIM annotation database to produce a tabular and graphical summary of patient response.
g. , a relational database format. All aspects of the ePAD functionality are based on querying AIM annotations, which contain the image data that comprise the original measurement [the region of interest (ROI) drawn to make the measurement, the value of the measurement, the designation of the measurement as being a target lesion, and other information]. Thus, all of ePAD's functionality is directly driven from the primary image data and image assessments-the AIM annotations acquired from the user who originally evaluated the images-rather than from a transcription of image-derived results onto flow sheets or case report forms (the current practice). This is a substantial advantage, because the source documentation for image measurements when using AIM is the actual measurement, rather than indirect information-the radiology report and image annotations-neither of which may consistently document all of the information about lesions required for response assessment  [22].
It can also be seen as a "meta-model checker". The Alloy language is used to state constraints that describe a set of models, or structures. It is also used to define properties that are verified with the Alloy Analyzer. For a given property (predicate or assertion), this tool finds models, in the provided set, for which this property holds; or states that there is no model, corresponding to the meta-model provided, for which the property holds. The approach followed by this feasibility draft is to provide a framework that defines a "running" product architecture as a set of components, defined as a function in a given state, connected by connectors (List. 1, lines 1 to 4). Each connector is associated to a symbolic value. The state of a component is either nominal or in failure (lines 7 to 8).
Fusion detection from RNA-seq A modified version of VirusSeq that implements a greedy algorithm with a robust statistical model was implemented and used in gene fusion discovery for RNA-seq data  (21). The TRCC cases were confirmed by another bioinformatic pipeline called Fusionseq  (22). Specifically, Mosaik was used to align paired-end reads to human genome reference (hg19). A given paired-end read alignment was then quantified in terms of the genomic location (L) of the aligned read pair, the distance (D) between the aligned read pair of the fragment (insert) and the orientation (O) of the read pair, and the confidence that each read is uniquely aligned. The specific pattern in (L, D, O) space was used as a constraint to define the discordant read pair. For example, a discordant read pair may have an exceptionally long D spanning a region in the reference genome.
Validation of fusion transcripts Validation of identified fusion transcripts was performed by PCR using custom primers for amplification. Sanger sequencing was performed on a 3730xl DNA Analyzer (Applied Biosystems) using BigDye ™ Terminator v3 chemistry (Applied Biosystems). Fusion sequences were verified using BLAST (U. S. National Institutes of Health) and SeqScape Software v2. 5 (Applied Biosystems).
Unsupervised classification of gene expression The variance stabilizing transformations implemented in DESeq package were performed on the count data in order to conduct sensible distance calculation. For each gene, the dispersion was calculated to measure its variance among samples, and thus the 2000 genes with highest dispersions were selected for clustering analysis. To remove the systematic difference between the MD Anderson and TCGA samples, the median expression values of each batch per gene were scaled to the same level. Hierarchical clustering analysis was performed using the Pearson correlation coefficient as the distance metric and Ward's linkage rule. Principal component analysis was also applied to investigate the multivariate pattern. The consensus clustering algorithm with the hierarchical clustering method was used to perform clustering analysis for 460 TCGA ccRCC samples.
Identification of MITF/TFE translocation in ccRCC of TCGA To characterize the functional translocations in ccRCC and identify fusion transcripts in a large dataset of kidney cancer, we analyzed paired-end transcriptome sequencing of 460 ccRCC samples profiled by TCGA. Using a threshold detection of a minimum of four tags per fusion junction, we identified fusion transcripts in 87 tumors (18. 7%) with a median translocation number of one per tumor (range: 1-12). Aiming to select the translocations that are most likely oncogenic, we used stringent criteria of a minimum of six tags identified per fusion junction, with at least one tag spanning the fusion junction. These criteria were based on the assumption that oncogenic fusion transcripts should be overexpressed. Using the criteria of six or more tags per fusion junction, we identified 123 translocations, including 84 (68.
3%) intra-chromosomal and 39 (31. 7%) extra-chromosomal translocations (Tables  S3  and S4 ). These were related to 81 tumors (17. 6%) (Figure  2 ), with a median of one translocation per tumor (range: 1-10) (Figure  3A ). To analyze whether the fusion genes have functional relevance, we used Database for Annotation Visualization and Integrated Discovery (DAVID). Functional annotations revealed exclusive enrichment of genes involved in chromosomal rearrangements (P-value =4x10 -4 , false discovery rate [FDR] <0. 04), which were related to 13 fusion genes and had occurred in 19 patients (~23. 5% of all patients with translocation) (Table  S5 ). These genes were: TFE3 (n=6), TFEB (n=1), SFPQ (n=5), FHIT (n=2), SLC9A9 (n=2), AFF1 (n=1), MKL1 (n=1), LHFP (n=1), ELL (n=1), JAK2 (n=1), DCX (n=1), EP300 (n=1), and LNP1 (n=1).
Six of these genes are known to be proto-oncogenes (TFEB, TFE3, MKL1, ELL, JAK2, and AFF1). Thus, TRCC represents the most frequently occurring common translocation found in TCGA's RCC dataset and represents 1. 5% (7 of 460) of all ccRCC cases (Figure  2 ). Interestingly, tumors bearing fusion transcripts with a partner previously reported to be involved in translocation have an overall higher rate of translocation as compared to those with newly identified translocations, with translocations per tumor having mean values of 2. 8 versus 1. 2, respectively (P-value<0. 0003; Figure  3B ). After excluding the six tumors with a translocation rate of four or more per tumor, DAVID analysis found that samples with fusion genes were enriched for fusions occurring within chromosome 3 (n=20; P-value =4.
2. In addition to the TFEB-KHDBRS2 fusion transcript, this sample of TRCC showed one of the highest numbers of tags spanning the fusion junction in the entire TCGA dataset (Table  S3 ). In particular, this tumor bears nine additional fusion transcripts, consistent with rearrangements occurring within 28. 9 megabases (chr6: 33,748,810-62,638,252) (Figure  4C-D ). We termed this process a "translocator phenotype". Likewise, the RCC sample with KHSRP-TFE3 fusion transcript had nine additional translocations occurring within a region spanning ~17. 3 megabases on chromosome 21 within a region spanning approximately 54. 8 megabases on chromosome X and consistent with a "translocator phenotype". To rule out the possibility of aberrant splicing that may explain the "translocator phenotype", we downloaded whole exome sequencing data from TCGA for both KHSRP-TFE3 and TFEB-KHDRBS2 cases (there is no whole genome sequencing data for both cases), and performed translocation detection.
Pathway analysis of MITF/TFE translocation renal cell carcinoma To assess whether TRCC displays a specific transcriptional profile, we analyzed our seven TRCC samples, and seven cases (six TFE3-related and one TFEB-related) which we identified in the dataset from TCGA, and compared them to 17 ccRCC samples (1 from MDACC and 16 from TCGA), 19 papillary RCC samples (3 from MDACC and 16 from TCGA), and 16 normal kidney tissue samples (2 from MDACC and 14 from TCGA). After removing the batch effect, unsupervised clustering revealed that TRCC displays a unique gene expression signature, as compared to those of ccRCC and papillary RCCs, except for two TFE3-related RCCs that were found to be minor clones (Figure  5A ). These data were confirmed by principal component analysis (not shown). Thus, MITF/TFE family translocations identified in the RCC cohort from TCGA have transcriptional profiles that are similar to those of the pediatric and young adult patients profiled independently through our sample collection, and share the same active and distinct pathways as those of other RCC subtypes.