Articles de revues sur le sujet « Table annotation »

Semantic web mining is getting more attention in intelligent web applications. Many web sites, especially those dynamically generate HTML pages to display the results of user queries, present information in the form of lists or tables. It is very useful to extract concept instances from these tables for many web applications such as intelligent agent systems for on-line product recommendations. This paper describes a technique for extracting data from tables in two steps, namely table detection and table analysis. The table detection step identifies the existence of a table and extracts its contents, and the table analysis step discovers the semantic meanings embedded in the table and associates them with the concepts described in the domain ontology that are used for semantic annotation on these tables. Our algorithm has been tested based on real-life web documents and the experimental results are encouraging.

Given a large amount of table data, how can we find the tables that contain the contents we want? A naive search fails when the column names are ambiguous, such as if columns containing stock price information are named “Close” in one table and named “P” in another table.One way of dealing with this problem that has been gaining attention is the semantic annotation of table data columns by using canonical knowledge. While previous studies successfully dealt with this problem for specific types of table data such as web tables, it still remains for various other types of table data: (1) most approaches do not handle table data with numerical values, and (2) their predictive performance is not satisfactory.This paper presents a novel approach for table data annotation that combines a latent probabilistic model with multilabel classifiers. It features three advantages over previous approaches due to using highly predictive multi-label classifiers in the probabilistic computation of semantic annotation. (1) It is more versatile due to using multi-label classifiers in the probabilistic model, which enables various types of data such as numerical values to be supported. (2) It is more accurate due to the multi-label classifiers and probabilistic model working together to improve predictive performance. (3) It is more efficient due to potential functions based on multi-label classifiers reducing the computational cost for annotation.Extensive experiments demonstrated the superiority of the proposed approach over state-of-the-art approaches for semantic annotation of real data (183 human-annotated tables obtained from the UCI Machine Learning Repository).

Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables. Current methods rely on either table metadata like column name or entity correspondences of cells in the KB, and may fail to deal with growing web tables with incomplete meta information. In this paper we propose a neural network based column type annotation framework named ColNet which is able to integrate KB reasoning and lookup with machine learning and can automatically train Convolutional Neural Networks for prediction. The prediction model not only considers the contextual semantics within a cell using word representation, but also embeds the semantics of a column by learning locality features from multiple cells. The method is evaluated with DBPedia and two different web table datasets, T2Dv2 from the general Web and Limaye from Wikipedia pages, and achieves higher performance than the state-of-the-art approaches.

We developed a Gene Annotation Easy Viewer (GAEV) that integrates the gene annotation data from the KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server. GAEV generates an easy-to-read table that summarizes the query gene name, the KO (KEGG Orthology) number, name of gene orthologs, functional definition of the ortholog, and the functional pathways that query gene has been mapped to. Via links to KEGG pathway maps, users can directly examine the interaction between gene products involved in the same molecular pathway. We provide a usage example by annotating the newly published freshwater microcrustacean Daphnia pulex genome. This gene-centered view of gene function and pathways will greatly facilitate the genome annotation of non-model species and metagenomics data. GAEV runs on a Windows or Linux system equipped with Python 3 and provides easy accessibility to users with no prior Unix command line experience.

We developed a Gene Annotation Easy Viewer (GAEV) that integrates the gene annotation data from the KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server. GAEV generates an easy-to-read table that summarizes the query gene name, the KO (KEGG Orthology) number, name of gene orthologs, functional definition of the ortholog, and the functional pathways that query gene has been mapped to. Via links to KEGG pathway maps, users can directly examine the interaction between gene products involved in the same molecular pathway. We provide a usage example by annotating the newly published freshwater microcrustacean Daphnia pulex genome. This gene-centered view of gene function and pathways will greatly facilitate the genome annotation of non-model species and metagenomics data. GAEV runs on a Windows or Linux system equipped with Python 3 and provides easy accessibility to users with no prior Unix command line experience.

We developed a Gene Annotation Easy Viewer (GAEV) that integrates the gene annotation data from the KEGG (Kyoto Encyclopedia of Genes and Genomes) Automatic Annotation Server. GAEV generates an easy-to-read table that summarizes the query gene name, the KO (KEGG Orthology) number, name of gene orthologs, functional definition of the ortholog, and the functional pathways that query gene has been mapped to. Via links to KEGG pathway maps, users can directly examine the interaction between gene products involved in the same molecular pathway. We provide a usage example by annotating the newly published freshwater microcrustacean Daphnia pulex genome. This gene-centered view of gene function and pathways will greatly facilitate the genome annotation of non-model species and metagenomics data. GAEV runs on a Windows or Linux system equipped with Python 3 and provides easy accessibility to users with no prior Unix command line experience.

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To illustrate possible use case of the annotations, and facilitate the analysis of the generated annotations, we provide examples of such pipelines, including data-visualization solutions in Fiji and KNIME, as well as a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Single-table text-to-SQL aims to transform a natural language question into a SQL query according to one single table. Recent work has made promising progress on this task by pre-trained language models and a multi-submodule framework. However, zero-shot table, that is, the invisible table in the training set, is currently the most critical bottleneck restricting the application of existing approaches to real-world scenarios. Although some work has utilized auxiliary tasks to help handle zero-shot tables, expensive extra manual annotation limits their practicality. In this paper, we propose a new approach for the zero-shot text-to-SQL task which does not rely on any additional manual annotations. Our approach consists of two parts. First, we propose a new model that leverages the abundant information of table content to help establish the mapping between questions and zero-shot tables. Further, we propose a simple but efficient meta-learning strategy to train our model. The strategy utilizes the two-step gradient update to force the model to learn a generalization ability towards zero-shot tables. We conduct extensive experiments on a public open-domain text-to-SQL dataset WikiSQL and a domain-specific dataset ESQL. Compared to existing approaches using the same pre-trained model, our approach achieves significant improvements on both datasets. Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive. More importantly, on the zero-shot subsets of both the datasets, our approach further increases the improvements.

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To facilitate and spread the usage of analysis tools, we provide examples of such pipelines, including a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

In this paper, improve the image annotation with semantic meaning, and name the new algorithm for semantic fusion of image annotation, that is a image is given to be labeled, use of training data set, the word set, and a collection of image area and other information to establish the probability model ,estimates the joint probability by word and given image areas.The probability value as the size, combined with keywords relevant table that integrates lexical semantics to extract keywords as the most representative image semantic annotation results. The algorithm can effectively use large-scale training data with rich annotation, so as to achieve better recall and precision than the existing automatic image annotation ,and validate the algorithm in the Corel data set.

142 Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and its outcome can be improved with better detection of incidental early CRC on routine CT of the abdomen and pelvis (CTAP). AI-second observer (AI) has the potential as shown in our companion abstract. The bottleneck in training AI is the time required for radiologists to segment the CRC. We compared two techniques for accelerating the segmentation process: 1) Sparse annotation (annotating some of the CT slice containing CRC instead of every slice); 2) Allowing AI to perform initial segmentation followed by human adjustment. Methods: 2D U-Net convolutional neural network (CNN) containing 31 million trainable parameters was trained with 58 CRC CT images from Banner MD Anderson (AZ) and MD Anderson Cancer Center (TX) (51 used for training and 7 for validation) and 59 normal CT scans from Banner MD Anderson Cancer Center. Twenty of the 25 CRC cases from public domain data (The Cancer Genome Atlas) were used to evaluate the performance of the models. The CRC was segmented using ITK-SNAP open-source software (v. 3.8). For the first objective, 3 separate models were trained (fully annotated CRC, every other slice, and every third slice). The AI-annotation on the TCGA dataset was analyzed by the percentage of correct detection of CRC, the number of false positives, and the Dice similarity coefficient (DSC). If parts of the CRC were flagged by AI, then it was considered correct. A detection was considered false positive if the marked lesion did not overlap with CRC; contiguous false positives across different slices of CT image were considered a single false positive. DSC measures the quality of the segmentation by measuring the overlap between the ground-truth and AI detected lesion. For the second objective, the time required to adjust the AI-produced annotation was compared to the time required for annotating the entire CRC without AI assistance. The AI-models were trained using ensemble learning (see our companion abstract for details of the techniques). Results: Our results showed that skipping slices of tumor in training did not alter the accuracy, false positives, or DSC classification of the model. When adjusting the AI-observer segmentation, there was a trend toward decreasing the time required to adjust the annotation compared to full manual segmentation, but the difference was not statistically significant (Table; p=0.121). Conclusions: Our results show that both skipping slices of tumor as well as starting with AI-produced annotation can potentially decrease the effort required to produce high-quality ground truth without compromising the performance of AI. These techniques can help improve the throughput to obtain a large volume of cases to train AI for detecting CRC.[Table: see text]

Abstract Background Ulcerative colitis is an idiopathic inflammatory disorder affecting the mucosa of the colon with superficial erosion and ulcers associated with bleeding. Severity assessment using current scoring schemes such as UCEIS and MAYO relies on the subjective interpretation of the physician and fails to take into account the size of the lesions, their number and distribution. Automatic lesion detection methods can enable fine-grained assessment of lesion severity, but require training stage based on time-consuming manual annotation. Most methods currently use generic datasets that are biased towards capsule endoscopy and are not adapted to locally available hardware. Methods We learn automatic bleeding and ulcer detectors on a local data set created by the Gastroenterology group at the Bichat and Beaujon hospitals. The patients’ videos were anonymous, analysed after obtaining their consent. The study was approved by the local research study committee. To minimise expert annotation burden, only rectangular annotations are provided instead of a precise delineation of lesion boundaries (Figure 1). This leads to many mislabelled pixels, especially in the corners, and affects the evaluation of the models’ performance and our ability to find correct models. Standard sensitivity and specificity cannot be used effectively on this data-set. We propose to evaluate model sensitivity on the annotation level and keep specificity at the pixel level. On the training set, we consider that a model correctly identifies a lesion if it agrees with the expert on a subset of the annotation, and count the detected annotations weighted by their area. For robustness and ease of interpretation, we explore the set of linear classifiers, and propose an efficient sampling scheme that rejects trivial models. This method is evaluated on a database of 10 colonoscopy videos (5 training videos and 5 test videos). Results In spite of the limited quality of the annotations, we find lesion detectors with a good annotation-level sensitivity (Table 1) and visual performance (see Figure 1). The detector’s performance is computed reliably. We evaluate sensitivity and specificity on 20 random subsets containing 10\% of the images, and obtain similar performance for the same patient for all models (cf Figure 2). Conclusion Despite mislabeled pixels, we obtain lesion detectors with good performance, and we show that the performance is computed reliably. However, the inter-patient performance is variable and the best models fail on some patients (sensitivity below 20\% in some cases Figure 2). This suggests that the models are not universal and that the appearance of bleeding and ulcers should be normalised further before automatic detection.

The identification of disease genes from candidated regions is one of the most important tasks in bioinformatics research. Most approaches based on function annotations cannot be used to identify genes for diseases without any known pathogenic genes or related function annotations. The authors have built a new web tool, DGHunter, to predict genes associated with these diseases which lack detailed function annotations. Its performance was tested with a set of 1506 genes involved in 1147 disease phenotypes derived from the morbid map table in the OMIM database. The results show that, on average, the target gene was in the top 13.60% of the ranked lists of candidates, and the target gene was in the top 5% with a 40.70% chance. DGHunter can identify disease genes effectively for those diseases lacking sufficient function annotations.

Abstract Combat trauma injuries require urgent and specialized care. When patient evacuation is infeasible, critical life-saving care must be given at the point of injury in real-time and under austere conditions associated to forward operating bases. Surgical telementoring allows local generalists to receive remote instruction from specialists thousands of miles away. However, current telementoring systems have limited annotation capabilities and lack of direct visualization of the future result of the surgical actions by the specialist. The System for Telementoring with Augmented Reality (STAR) is a surgical telementoring platform that improves the transfer of medical expertise by integrating a full-size interaction table for mentors to create graphical annotations, with augmented reality (AR) devices to display surgical annotations directly onto the generalist’s field of view. Along with the explanation of the system’s features, this paper provides results of user studies that validate STAR as a comprehensive AR surgical telementoring platform. In addition, potential future applications of STAR are discussed, which are desired features that state-of-the-art AR medical telementoring platforms should have when combat trauma scenarios are in the spotlight of such technologies.

regionReport is an R package for generating detailed interactive reports from regions of the genome. The report includes quality-control checks, an overview of the results, an interactive table of the genomic regions and reproducibility information. regionReport can easily be expanded with report templates for other specialized analyses. In particular, regionReport has an extensive report template for exploring derfinder results from annotation-agnostic RNA-seq differential expression analyses.

In the last few years, several works in the literature have addressed the problem of data extraction from web pages. The importance of this problem derives from the fact that, once extracted, data can be handled in a way similar to instances of a traditional database, which in turn can facilitate application of web data integration and various other domain specific problems. In this paper, we propose a novel table extraction technique that works on web pages generated dynamically from a back-end database. The proposed system can automatically discover table structure by relevant pattern mining from web pages in an efficient way, and can generate regular expression for the extraction process. Moreover, the proposed system can assign intuitive column names to the columns of the extracted table by leveraging Wikipedia knowledge base for the purpose of table annotation. To improve accuracy of the assignment, we exploit the structural homogeneity of the column values and their co-location information to weed out less likely candidates. This approach requires no human intervention and experimental results have shown its accuracy to be promising. Moreover, the wrapper generation algorithm works in linear time.

Context Genomic sequencing for cancer is offered by commercial for-profit laboratories, independent laboratory networks, and laboratories in academic medical centers and integrated health networks. The variability among the tests has created a complex, confusing environment. Objective To address the complexity, the Personalized Health Care (PHC) Committee of the College of American Pathologists proposed the development of a cancer genomics resource list (CGRL). The goal of this resource was to assist the laboratory pathology and clinical oncology communities. Design The PHC Committee established a working group in 2012 to address this goal. The group consisted of site-specific experts in cancer genetic sequencing. The group identified current next-generation sequencing (NGS)–based cancer tests and compiled them into a usable resource. The genes were annotated by the working group. The annotation process drew on published knowledge, including public databases and the medical literature. Results The compiled list includes NGS panels offered by 19 laboratories or vendors, accompanied by annotations. The list has 611 different genes for which NGS-based mutation testing is offered. Surprisingly, of these 611 genes, 0 genes were listed in every panel, 43 genes were listed in 4 panels, and 54 genes were listed in 3 panels. In addition, tests for 393 genes were offered by only 1 or 2 institutions. Table 1 provides an example of gene mutations offered for breast cancer genomic testing with the annotation as it appears in the CGRL 2014. Conclusions The final product, referred to as the Cancer Genomics Resource List 2014, is available as supplemental digital content.

In researching social network data and depression, it is often necessary to manually label depressed and non-depressed users, which is time-consuming and labor-intensive. The aim of this study is that it explores the relationship between social network data and depression. It can also contribute to detecting and identifying depression. Through collecting and analyzing college students’ microblog social data, a preliminary screening algorithm for college students’ suspected depression microblogs based on depression keywords, and semantic expansion is researched; a comprehensive lexical grammar was proposed. This research provided has a preliminary screening method based on depression keywords and semantic expansion for college students’ suspected depression microblogs, with a screening accuracy. This method forms a depression keyword table by constructing the basic keyword table and the semantic expansion based on the word embedding learning model Word2Vec. Finally, the word table is used to calculate the semantic similarity of the tested microblogs and then identify whether it is a suspected depression microblog. The experimental results on the microblog dataset of college students show that the comprehensive lexical method is better than the SDS questionnaire segmentation method and the expert lexical method in terms of screening accuracy; the comprehensive lexical approach can quickly and automatically screen out a tiny proportion of suspected doubts from a large number of college students’ microblogs. Depression Weibo can reduce the workload of experts’ annotation, improve annotation efficiency, and provide a suitable data processing basis for the subsequent accurate identification (classification problem) of patients with depression.

Abstract OrthoVenn is a powerful web platform for the comparison and analysis of whole-genome orthologous clusters. Here we present an updated version, OrthoVenn2, which provides new features that facilitate the comparative analysis of orthologous clusters among up to 12 species. Additionally, this update offers improvements to data visualization and interpretation, including an occurrence pattern table for interrogating the overlap of each orthologous group for the queried species. Within the occurrence table, the functional annotations and summaries of the disjunctions and intersections of clusters between the chosen species can be displayed through an interactive Venn diagram. To facilitate a broader range of comparisons, a larger number of species, including vertebrates, metazoa, protists, fungi, plants and bacteria, have been added in OrthoVenn2. Finally, a stand-alone version is available to perform large dataset comparisons and to visualize results locally without limitation of species number. In summary, OrthoVenn2 is an efficient and user-friendly web server freely accessible at https://orthovenn2.bioinfotoolkits.net.

Abstract Ruminant Genome Database (RGD; http://animal.nwsuaf.edu.cn/RGD) provides visualization and analysis tools for ruminant comparative genomics and functional annotations. As more high-quality ruminant genome assemblies have become available, we have redesigned the user interface, integrated and expanded multi-omics data, and developed novel features to improve the database. The new version, RGD v2.0, houses 78 ruminant genomes; 110-species synteny alignments for major livestock (including cattle, sheep, goat) and wild ungulates; 21 012 orthologous gene clusters with Gene Ontology and pathway annotation; ∼8 600 000 conserved elements; and ∼1 000 000 cis-regulatory elements by utilizing 1053 epigenomic data sets. The transcriptome data in RGD v2.0 has nearly doubled, currently with 1936 RNA-seq data sets, and 155 174 phenotypic data sets have been newly added. New and updated features include: (i) The UCSC Genome Browser, BLAT, BLAST and Table Browser tools were updated for six available ruminant livestock species. (ii) The LiftOver tool was newly introduced into our browser to allow coordinate conversion between different ruminant assemblies. And (iii) tissue specificity index, tau, was calculated to facilitate batch screening of specifically expressed genes. The enhanced genome annotations and improved functionality in RGD v2.0 will be useful for study of genome evolution, environmental adaption, livestock breeding and biomedicine.

e13148 Background: Tumor mutation profiling has become a key component for orienteering the treatment of oncologic patients. A crucial step for this is the correct identification and classification of pathogenic and actionable variants. In the present work we aimed at the development and validation of a tumor mutation profiling panel, based on NGS, which uses artificial intelligence for variant annotation. Methods: We designed a hybrid capture panel, containing 366 genes to evaluate somatic SNVs, INDELs and CNVs, and to calculate TMB, using a customized bioinformatics pipeline. MSI status was determined by fragment analysis using capillary electrophoresis. Analytical performance was determined using reference cell lines. FFPE samples from 70 tumors were accessed and 53 were sequenced. Variant annotation was performed by IBM Watson for Genomics (WfG) platform. Assay performance on clinical samples was defined based on orthogonal assays using Agilent CGH+SNParray 400K (for CNVs only) and Foundation One test (Foundation Medicine) (F1). Results: Breast, colon and lung were the most common tumor origins. Fifty-three samples were successfully sequenced, while 41 of them could also be analyzed by F1 test. A summary of the assay performance is presented in Table 1. Our pipeline detected 1219 variants and 290 (23%) were classified as Pathogenic, Likely Pathogenic or Actionable, according to WfG. Thirty-five samples (66%) presented a variant that could drive the treatment, with 37.7% of samples being sensitive to targeted therapies, while 22.6% were resistant; additionally, 86% had an indication for a clinical trial. Conclusions: The developed assay presented a good overall sensitivity and allele frequency correlation, with TMB and MSI having the best rates. Comparisons with F1 had reduced values of concordance; however, SNVs and INDELs presented a similar frequency. Differences on CNVs identification may rely on distinct thresholds established by the different groups. The high percentage of samples that could benefit from mutational profiling highlights the importance of such approach in the clinical routine. Additionally, the high number of variants features the need for updated information for annotation. [Table: see text]

This paper introduces Logical Semantics with Perception (LSP), a model for grounded language acquisition that learns to map natural language statements to their referents in a physical environment. For example, given an image, LSP can map the statement “blue mug on the table” to the set of image segments showing blue mugs on tables. LSP learns physical representations for both categorical (“blue,” “mug”) and relational (“on”) language, and also learns to compose these representations to produce the referents of entire statements. We further introduce a weakly supervised training procedure that estimates LSP’s parameters using annotated referents for entire statements, without annotated referents for individual words or the parse structure of the statement. We perform experiments on two applications: scene understanding and geographical question answering. We find that LSP outperforms existing, less expressive models that cannot represent relational language. We further find that weakly supervised training is competitive with fully supervised training while requiring significantly less annotation effort.

The interaction between users and digital devices has deeply changed in the last decades. In particular, the so called desktop metaphor relied on assumptions which have been modified by the rise of new paradigms, such as Web 2.0 and Cloud Computing. This paper discusses the limits of the desktop metaphor and proposes a new interaction model, TablePlusPlus (T++), aimed at providing Web and Cloud users with interaction mechanisms fulfilling their needs, i.e., the possibility of handling activity contexts, collaborating with other users, and homogeneously managing heterogeneous objects. T++ tables provide a context-based environment which enables a homogeneous treatment of heterogeneous content items, enhanced by a table-level annotation mechanism supporting an abstract view over resources, which is missing in standard current desktop and collaborative environments. In order to evaluate the effectiveness of the proposed model, this work developed a prototype and tested it in two controlled experiments, whose results are definitely encouraging.

Video offers solutions to many of the traditional problems with coach, trainer, commenter, umpires and other security issues of modern team games. This paper presents a novel framework to perform player identification and tracking technique for the sports (Kabaddi) with extending the implementation towards the event handling process which expands the game analysis of the third umpire assessment. In the proposed methodology, video preprocessing has done with Kalman Filtering (KF) technique. Extended Gaussian Mixture Model (EGMM) implemented to detect the object occlusions and player labeling. Morphological operations have given the more genuine results on player detection on the spatial domain by applying the silhouette spot model. Team localization and player tracking has done with Robust Color Table (RCT) model generation to classify each team members. Hough Grid Transformation (HGT) and Region of Interest (RoI) method has applied for background annotation process. Through which each court line tracing and labeling in the half of the court with respect to their state-of-art for foremost event handling process is performed. Extensive experiments have been conducted on real time video samples to meet out the all the challenging aspects. Proposed algorithm tested on both Self Developed Video (SDV) data and Real Time Video (RTV) with dynamic background for the greater tracking accuracy and performance measures in the different state of video samples.

Data science growing success relies on knowing where a relevant dataset exists, understanding its impact on a specific task, finding ways to enrich a dataset, and leveraging insights derived from it. With the growth of open data initiatives, data scientists need an extensible set of effective discovery operations to find relevant data from their enterprise datasets accessible via data discovery systems or open datasets accessible via data portals. Existing portals and systems suffer from limited discovery support and do not track the use of a dataset and insights derived from it. We will demonstrate KGLac, a system that captures metadata and semantics of datasets to construct a knowledge graph (GLac) interconnecting data items, e.g., tables and columns. KGLac supports various data discovery operations via SPARQL queries for table discovery, unionable and joinable tables, plus annotation with related derived insights. We harness a broad range of Machine Learning (ML) approaches with GLac to enable automatic graph learning for advanced and semantic data discovery. The demo will showcase how KGLac facilitates data discovery and enrichment while developing an ML pipeline to evaluate potential gender salary bias in IT jobs.

In recent years, there has been a growing interest in obtaining probiotic bacteria from plant origins. This is the case of Lactiplantibacillus pentosus LPG1, a lactic acid bacterial strain isolated from table olive biofilms with proven multifunctional features. In this work, we have sequenced and closed the complete genome of L. pentosus LPG1 using both Illumina and PacBio technologies. Our intention is to carry out a comprehensive bioinformatics analysis and whole-genome annotation for a further complete evaluation of the safety and functionality of this microorganism. The chromosomic genome had a size of 3,619,252 bp, with a GC (Guanine-Citosine) content of 46.34%. L. pentosus LPG1 also had two plasmids, designated as pl1LPG1 and pl2LPG1, with lengths of 72,578 and 8713 bp (base pair), respectively. Genome annotation revealed that the sequenced genome consisted of 3345 coding genes and 89 non-coding sequences (73 tRNA and 16 rRNA genes). Taxonomy was confirmed by Average Nucleotide Identity analysis, which grouped L. pentosus LPG1 with other sequenced L. pentosus genomes. Moreover, the pan-genome analysis showed that L. pentosus LPG1 was closely related to the L. pentosus strains IG8, IG9, IG11, and IG12, all of which were isolated from table olive biofilms. Resistome analysis reported the absence of antibiotic resistance genes, whilst PathogenFinder tool classified the strain as a non-human pathogen. Finally, in silico analysis of L. pentosus LPG1 showed that many of its previously reported technological and probiotic phenotypes corresponded with the presence of functional genes. In light of these results, we can conclude that L. pentosus LPG1 is a safe microorganism and a potential human probiotic with a plant origin and application as a starter culture for vegetable fermentations.

The use of Semantic Web technologies including ontologies and knowledge graphs is a widespread practice in the development of modern intelligent systems for information retrieval, recommendation and question-answering. The pro-cess of developing ontologies and knowledge graphs involves the use of various information sources, for example, databases, documents, conceptual models. Tables are one of the most accessible and widely used ways of storing and presenting information, as well as a valuable source of domain knowledge. In this paper, it is proposed to automate the extraction process of specific entities (facts) from tabular data for the subsequent filling of a target knowledge graph. A new approach is proposed for this purpose. A key feature of this approach is the semantic interpretation (annotation) of individual table elements. A description of its main stages is given, the application of the approach is shown in solv-ing practical problems of creating subject knowledge graphs, including in the field of industrial safety expertise of pet-rochemical equipment and technological complexes. An experimental quantitative evaluation of the proposed ap-proach was also obtained on a test set of tabular data. The obtained results showed the feasibility of using the pro-posed approach and the developed software to solve the problem of extracting facts from tabular data for the subsequent filling of the target knowledge graph.

Soybean is an important cash crop that is widely used as a source of vegetable protein and edible oil. The regeneration ability of soybean directly affects the application of biotechnology. In this study, we used the exogenous hormone 2,4-D to treat immature embryos. Different levels of somatic incidence were selected from the chromosome segment substitution lines (CSSLs) constructed by SN14 and ZYD00006. Transcriptome sequencing of extreme materials was performed, and 2666 differentially expressed genes were obtained. At the same time, a difference table was generated by combining the data on CSSL rearrangement. In the extreme materials, a total of 93 differentially expressed genes were predicted and were then analyzed by cluster analysis and Gene Ontology (GO) annotation. After screening and annotating the target genes, three differentially expressed genes with hormone pathways were identified. The expression patterns of the target genes were verified by real-time quantitative PCR (qRT-PCR). Haplotype polymorphism detection and linkage disequilibrium analysis were performed on the candidate gene Glyma.09g248200. This study provided more information on the regulation network of soybean somatic embryogenesis and regeneration processes, and further identified important genes in the soybean regeneration process and provided a theoretical basis for accelerating the application of biotechnology to soybean for improving its breeding efficiency.

Abstract The 3D structures of biomacromolecules stored in the Protein Data Bank [1] were correlated with different external, biological information from public databases. We have matched the feature table of SWISS-PROT [2] entries as well InterPro [3] domains and function sites with the corresponding 3D-structures. OMIM [4] (Online Mendelian Inheritance in Man) records, containing information of genetic disorders, were extracted and linked to the structures. The exhaustive all-against-all 3D structure comparison of protein structures stored in DALI [5] was condensed into single files for each PDB entry. Results are stored in XML format facilitating its incorporation into related software. The resulting annotation of the protein structures allows functional sites to be identified upon visualization.

77 Background: In February 2018, ASCO published a guideline on how clinicians should manage immune-related adverse events (irAEs) in cancer patients treated with immune checkpoint inhibitors (ICPis). Recommendations were based on informal consensus due to a lack of "high-quality" evidence. Our objective was to determine whether DOC Search, a cloud-based AI search engine, could be used to rapidly determine if any new evidence matches the inclusion criteria of the guideline. Methods: PubMed, ASCO abstracts, and 85 RSS feeds were queried within DOC Search to identify publications since the guideline search was last conducted. DOC Search automatically includes comprehensive synonym lists for the search terms entered, and annotates co-occurring characteristics, interventions and outcomes. Results: Between 11/1/2017 and 10/31/2018, 1178 published references were identified (85.7% from PubMed, 13.8% from ASCO, and 0.5% from official RSS feeds). Title/abstract screening of a sample of the most recent articles indicated that 44% were relevant, and of these, 8% specifically reported research on the management of irAEs. Through automated term indexing of search results, some of the most frequently reported terms were melanoma, corticosteroids, and colitis (Table). Conclusions: DOC Search employs robust ontology mapping—including UMLS, ASCO’s proprietary toxonomy, and more—which obviated the need for complex search strings. The machine learning and natural language processing technology provided real-time analysis and automated term indexing of search results, improving our understanding of the rapidly changing evidence landscape. This analysis met the objective to use DOC Search for rapid identification and review of new published evidence for an existing guideline.[Table: see text]

Current information retrieval systems are limited to text in documents for helping users with their information needs. With the progress in the field of natural language processing, there now exists the possibility of enriching large document collections with accurate semantic annotations. Annotations in the form of part-of-speech tags, temporal expressions, numerical values, geographic locations, and other named entities can help us look at terms in text with additional semantics. This doctoral dissertation presents methods for search and analysis of large semantically annotated document collections. Concretely, we make contributions along three broad directions: indexing, querying, and mining of large semantically annotated document collections. Indexing Annotated Document Collections. Knowledge-centric tasks such as information extraction, question answering, and relationship extraction require a user to retrieve text regions within documents that detail relationships between entities. Current search systems are ill-equipped to handle such tasks, as they can only provide phrase querying with Boolean operators. To enable knowledge acquisition at scale, we propose gyani, an indexing infrastructure for knowledge-centric tasks. gyani enables search for structured query patterns by allowing regular expression operators to be expressed between word sequences and semantic annotations. To implement grep-like search capabilities over large annotated document collections, we present a data model and index design choices involving word sequences, annotations, and their combinations. We show that by using our proposed indexing infrastructure we bring about drastic speedups in crucial knowledge-centric tasks: 95× in information extraction, 53× in question answering, and 12× in relationship extraction. Hyper-phrase queries are multi-phrase set queries that naturally arise when attempting to spot knowledge graph facts or subgraphs in large document collections. An example hyper-phrase query for the fact 〈mahatma gandhi, nominated for, nobel peace prize〉 is: 〈{ mahatma gandhi, m k gandhi, gandhi }, { nominated, nominee, nomination received }, { nobel peace prize, nobel prize for peace, nobel prize in peace }〉. Efficient execution of hyper-phrase queries is of essence when attempting to verify and validate claims concerning named entities or emerging named entities. To do so, it is required that the fact concerning the entity can be contextualized in text. To acquire text regions given a hyper-phrase query, we propose a retrieval framework using combinations of n-gram and skip-gram indexes. Concretely, we model the combinatorial space of the phrases in the hyper-phrase query to be retrieved using vertical and horizontal operators and propose a dynamic programming approach for optimized query processing. We show that using our proposed optimizations we can retrieve sentences in support of knowledge graph facts and subgraphs from large document collections within seconds. Querying Annotated Document Collections. Users often struggle to convey their information needs in short keyword queries. This often results in a series of query reformulations, in an attempt to find relevant documents. To assist users navigate large document collections and lead them to their information needs with ease, we propose methods that leverage semantic annotations. As a first step, we focus on temporal information needs. Specifically, we leverage temporal expressions in large document collections to serve time-sensitive queries better. Time-sensitive queries, e.g., summer olympics implicitly carry a temporal dimension for document retrieval. To help users explore longitudinal document collections, we propose a method that generates time intervals of interest as query reformulations. For instance, for the query world war , time intervals of interest are: [1914; 1918] and [1939;1945]. The generated time intervals are immediately useful in search-related tasks such as temporal query classification and temporal diversification of documents. As a second and final step, we focus on helping the user in navigating large document collections by generating semantic aspects. The aspects are generated using semantic annotations in the form of temporal expressions, geographic locations, and other named entities. Concretely, we propose the xFactor algorithm that generates semantic aspects in two steps. In the first step, xFactor computes the salience of annotations in models informed of their semantics. Thus, the temporal expressions 1930s and 1939 are considered similar as well as entities such as usain bolt and justin gatlin are considered related when computing their salience. Second, the xFactor algorithm computes the co-occurrence salience of annotations belonging to different types by using an efficient partitioning procedure. For instance, the aspect 〈{usain bolt}, {beijing, London}, [2008;2012]〉 signifies that the entity, locations, and the time interval are observed frequently in isolation as well as together in the documents retrieved for the query olympic medalists. Mining Annotated Document Collections. Large annotated document collections are a treasure trove of historical information concerning events and entities. In this regard, we first present EventMiner, a clustering algorithm, that mines events for keyword queries by using annotations in the form of temporal expressions, geographic locations, and other disambiguated named entities present in a pseudo-relevant set of documents. EventMiner aggregates the annotation evidences by mathematically modeling their semantics. Temporal expressions are modeled in an uncertainty and proximity-aware time model. Geographic locations are modeled as minimum bounding rectangles over their geographic co-ordinates. Other disambiguated named entities are modeled as a set of links corresponding to their Wikipedia articles. For a set of history-oriented queries concerning entities and events, we show that our approach can truly identify event clusters when compared to approaches that disregard annotation semantics. Second and finally, we present jigsaw, an end-to-end query-driven system that generates structured tables for user-defined schema from unstructured text. To define the table schema, we describe query operators that help perform structured search on annotated text and fill in table cell values. To resolve table cell values whose values can not be retrieved, we describe methods for inferring null values using local context. jigsaw further relies on semantic models for text and numbers to link together near-duplicate rows. This way, jigsaw is able to piece together paraphrased, partial, and redundant text regions retrieved in response to structured queries to generate high-quality tables within seconds. This doctoral dissertation was supervised by Klaus Berberich at the Max Planck Institute for Informatics and htw saar in Saarbrücken, Germany. This thesis is available online at: https://people.mpi-inf.mpg.de/~dhgupta/pub/dhruv-thesis.pdf.

Abstract Relapse within the central nervous system (CNS) remains a major challenge for the treatment and cure of childhood ALL. With current intensive treatment, at least one-third of relapses occur within the CNS. Evaluation of CSF is essential to the management of childhood ALL and relies on morphologic examination of cytospin smears. CSF has been re-discovered in the post genomic era as a source of potential protein biomarkers for various diseases. Such proteins may help establish a diagnosis, provide insight into pathogenesis, or identify therapeutic toxicity. In the present study, acellular CSF supernatants from newly diagnosed children with standard risk ALL treated on CCG-1991 were used for protein profiling. The morphologically negative CSF samples had previously been classified as minimal residual disease (MRD) negative (−) or positive (+) using real-time PCR techniques on centrifuged cell lysates. Three MRD −, 3 MRD +, and 2 morphologically + CSF samples were concentrated and desalted using Microcon filter device. Albumin was removed using a Human Albumin Kit, Albuminomics™. The MRD − and MRD + samples, each containing 15 mcg of protein, were pooled separately and processed for protein identification and quantitation using a multidimensional LC/MS/MS method. Custom software was developed (TandTRAQ) for database searching and peptide quantitation. Protein annotation was primarily derived from ENSEMBL and NCBI sources. Rank order summarization was used to identify putative candidates. In the comparison between MRD + and MRD− samples, we identified 226 unique genome identifiers, with 89 having functional annotation. Of these 89, 49 have high quality protein-level annotation, 35 have mRNA-level annotation, and 5 are predicted (have functional protein evidence). Using a 1.5 fold cut-off, there were 18 up-regulated and 12 down-regulated proteins among the 89 proteins in MRD + compared to MRD − supernatants. Focusing on the 49 with high quality protein-level annotation, 8 proteins show higher-level expression and 2 show lower level expression in MRD + CSF samples (Table). Eight highly expressed proteins are involved in either DNA binding interactions, cell migration, apoptosis, or immune response. Two under-expressed proteins are involved in cell adhesion. The 2 morphologically + CSF samples confirmed 4 over expressed and 1 under-expressed proteins. This research is the first attempt at protein profiling of CSF from patients with ALL. The proteins identified in this study may further our insight into the pathogenesis of CNS leukemia. Furthermore, future research may find that protein profiling of supernatants is a sensitive way to detect leukemia in CSF and to monitor treatment-related CNS toxicities.

e13547 Background: Improved scalability and affordability of next generation sequencing (NGS) has pivoted cancer care toward genomics-driven treatment decisions. Particularly in advanced-stage or refractory cancer, clinical insights gleaned from NGS have become an integral option as these patients have typically exhausted all lines of available therapy. As precision oncology evolves, NGS is expected to have a differential impact based on the cancer type. In this study, a comprehensive NGS panel was used to determine the strength of clinical evidence in various advanced stage tumor samples from The Cancer Genome Atlas (TCGA). Methods: A hybrid capture panel, Oncofoco, was developed to evaluate SNVs, INDELs, CNVs and TMB in 366 genes. The panel’s utility was validated by interrogating a broader cohort of 2847 TCGA samples (advanced tumors with T3 or T4; or N > = 1; or M > = 1). Watsonä for Genomics, an artificial intelligence offering, was used for variant interpretation and annotation of the 366 genes. A clinical evidence classification system that evaluated the strength of biomarker/drug response associations was used for annotation with level 1/R1 strongest and level 4 weakest from clinical literature, FDA drug labels and guidelines (PMID:28890946). Results: The highest level of evidence for the top nine frequently occurring advanced stage cancers in TCGA is shown in Table. Conclusions: Thyroid cancer and cutaneous melanoma have emerged as the cancer types with the most level 1 evidence (FDA approved drugs) owing to BRAF V600E mutations. Kidney and prostate cancers show no cases with level 1 evidence and also had the largest fraction of unactionable tumors. Over half of colorectal cancer cases had level R1 resistance evidence attributed to KRAS and NRAS mutations. The clinical utility of NGS in late-stage refractory cancer varies widely by tumor type. The presence of level 3 and level 4 evidence in all cancer types bodes well for the development of new targeted drugs. [Table: see text]

e18047 Background: Computer-aided diagnosis based on deep learning methodologies has demonstrated great potential to assist disease diagnosis with accuracy and efficiency. Specifically, the quantitative and qualitative analysis on lung nodules has proven to be important for the early-detection of lung cancer as well as its treatment in clinical practice. This study developed a 3D CNN model to facilitate the classification of pulmonary nodules. Methods: We collected 3956 lung CT scans (slice thickness≤3mm) with multiple lung nodules from 15 Class-A hospitals in China, 1155 lung CTs from Luna 16 dataset and Data Science Bowl 2017. There were 30 senior radiologists responsible for annotation and each CT scan was annotated by two of them randomly. Another 4 senior associate chief physicians were divided into two groups, each group was responsible for arbitration when conflicts occurred between the annotation doctors. All the annotated CTs were randomly selected and split to construct training, validation and test sets. We pre-processed the CTs and utilized 3D CNNs to classify these nodules as solid, partial-solid, ground glass opacity, calcified, pleural solid and pleural calcified. ROC analysis was used, and the classification capability was assessed by classification accuracy and the AUC score. Results: Table shows the overall results. The proposed model yielded an AUC score of 0.97 for the ground glass opacity and 0.90 for calcified nodules in the training set, while the AUC of them were 0.93 and 0.93 respectively in the validation set. For the test set, we got an AUC score of 0.94 for the ground glass opacity. The average classification time for each nodule was less than 0.005 sec. Conclusions: Our model may assist clinical diagnosis of lung cancer and increase its objectivity and accuracy, and the fast processing speed proves its feasibility to be applied in real clinical practice. In the future, we will enrich the dataset with clinical and genetic information, thus improving our model to boost its performance. [Table: see text]

The review presents an international collection of articles published in Slovenia, which is dedicated to the centenary of the death of the Slovenian writer Ivan Cankar. The collection reflects the state of modern studies of Cankar’s oeuvre in some European countries (Slovenia, Austria, Croatia, Hungary, Russia), raises topical issues in the study of the writer’s poetics, and outlines ways for their further development. Thematically, the articles can be divided into the study of the philosophical and aesthetic foundations of Cankar’s work and the study of his literary connections and the reception of his work. The collection is provided with a table of contents, an introduction and a name index, each article separately has an annotation and keywords in Slovenian and English, as well as a list of references.

Abstract Background Previous studies have described machine learning (ML) models to predict how human readers would score disease activity in UC using the endoscopic Mayo Score (eMS). So far, none employed deep human annotation that considers all the endoscopic features making up the eMS. Here we report the results of an ML model that is trained on eMS features using centrally read endoscopies. Methods 793 full-length videos were obtained from 249 patients with UC who participated in NCT02589665, a phase 2 trial with mirikizumab in patients with UC and associated with centrally read (single reader) eMS (CReMS) as the primary dataset. After cleaning for usable frames, the data were split into training, validation and testing subsets. The ML workflow consisted of annotation, segmentation, and classification (e.g., erosions, ulcers, erythema, vascular pattern, and bleeding). Human image classification and segmentation with bounding boxes and was subjected to quality control adjudicated by one of three IBD specialists, generating more than 60,000 eMS-relevant annotation labels. The model was evaluated on a test set of 147 videos using the CReMS, and a consensus set of 94 test videos, where CReMS and annotator reported eMS (AReMS) were in agreement without adjudication. The primary objective of the model was a categorical prediction of endoscopically inactive disease (eMS 0 & 1) compared with active disease (eMS 2 & 3). The secondary objectives of the model were to predict endoscopic healing (eMS 0) and to predict severe disease (eMS 3). Results The model performances are in Table 1. On the full test set of 147 videos, the model predicted inactive disease compared with active disease with an accuracy of 84%, positive predictive value (PPV) of 80%, and negative predictive value (NPV) of 85%. In the subset of 94 videos with CreMS and AReMS consensus, the model predicted inactive disease compared with active disease with an accuracy of 89%, PPV 87%, and NPV of 90%. In this same subset, the model predicted endoscopic healing and severe disease with an accuracy of 95% and 85%, PPVs of 86% and 82% and NPVs of 95% and 87%, respectively. For the secondary objectives in the full set of 147 videos, the model predicted endoscopic healing and severe disease with an accuracy of 90% and 80%, PPVs of 44% and 86%, and NPVs of 95% and 86%, respectively. Conclusion We have developed a ML predictive model of the eMS in UC using centrally read videos and demonstrate excellent distinction between active and inactive disease, and clear discrimination between other levels of endoscopic activity. We propose that this unique ML approach to endoscopic assessment be considered as a substitute to human central reading in future clinical trials.

Abstract Background Human immunodeficiency virus (HIV) disease progression are different among genders, in which women usually progress to acquired immunodeficiency syndrome (AIDS) faster than men. The mechanisms resulting in the gender biases of HIV progression are unclear. We conducted a bioinformatics analysis of differentially expressed genes (DEGs) in women and men with HIV disease to understand the sex-based differences in HIV pathogenesis. Methods We obtained microarray data from the Gene Expression Omnibus (GEO) database using our pre-defined search strategy and analyzed data using the GEO2R platform. The t-test was done to compare DEGs between females and males with HIV diseases. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was implemented to systematically extract biological features and processes of retrieving DEGs via gene ontology (GO) analysis. A Systemic search was performed to evaluate each DEG function and its possible association with HIV. Results One gene expression profiling data were retrieved: GSE 140713, composed of 40 males and 10 females with HIV1 infected samples. A GEO2R analysis yielded 19 DEGs (Table 1). The GO analysis result was demonstrated in Tables 2 and 3. Following a systemic search, we found two DEGs, which have previous studies reported an association with HIV: DDX3X (20 studies) and PDS5 (1 study). We proposed DDX3X (t 5.3, p 0.0037) is responsible for gender inequalities of HIV progression because of: 1. DDX3X is needed in the HIV1 life cycle. 2. Several studies confirmed a positive correlation between DDX3X expression and HIV1 replication. 3. Our study found an up-regulated DDX3X expression in women corresponded to the fact that women progress to AIDS faster than men. 4. Our GO analysis showed female up-regulated genes were enriched in positive regulation of the gene expression pathway, which can be explained by DDX3X and its underlying mechanism. Table 1: DEGs in women and men with HIV1 disease Table 2: GO functional enrichment pathway analyses of overall retrieving DEGs Table 3: GO functional enrichment pathway analyses of down- and up-regulated clusters of DEGs Conclusion Aberrant DDX3X expression may contribute to sex-based differences in HIV disease. Drugs modifying DDX3X gene expression will be beneficial in the treatment of HIV especially resolving the HIV drug resistance problem because current anti-HIV drugs target viral components posed the risk of viral mutation. Disclosures All Authors: No reported disclosures

7024 Background: Extramedullary (EM) involvement, including myeloid sarcoma (MS) and leukemia cutis (LC), is uncommon in patients with acute myeloid leukemia (AML). Mutational landscape of EM-AML is not well characterized, including concordance of sequencing data from EM vs. non-EM site (blood or bone marrow) and the potential for personalized targeted therapy in this patient cohort. Methods: In a multicenter retrospective study, clinical and genomic data were collected on EM-AML patients treated at Moffitt Cancer Center, Memorial Healthcare System, and University of Miami, as well as sequenced cases at a central laboratory. Next generation sequencing (NGS) data come from panels that interrogated 24- 406 genes, with 15 genes covered by all panels, including notably, IDH1, IDH2, KIT, KRAS, NPM1, NRAS, and TP53. Survival estimates using Kaplan-Meier statistics and multivariate analysis with Cox-regression were performed in SPSS (v.26). Results: Our study included 58 patients with EM-AML. Median age at diagnosis was 62 years; 55% of patients were males. In our cohort, 34 (59%) patients had MS, and 19 (33%) had LC. EM-AML was noted during relapse in 60% of evaluable patients (n=45), and 31% had isolated EM disease. Patients with LC had a significantly worse median overall survival (OS) than those with MS (5.7 months vs. 21.9 months, p= 0.008); Pattern of EM involvement (MS vs. LC) remained an independent prognostic factor for OS (p= 0.04) in a multivariate analysis including disease setting (new diagnosis vs. relapse) and ELN risk category. Results of NGS performed during EM presentation were available in 48 patients, 19 of which had NGS data from EM site. Most commonly mutated genes were NRAS on EM site NGS (37%) and NPM1 on non-EM site NGS (28%). Based on EM NGS, 52% patients had a targetable genomic alteration, with 37% mutations in IDH, 21% NPM1, 5% FLT3, and 11% MLL-PTD. Five (two with concurrent M+EM disease) out of nine evaluable patients had significant discordance in targetable mutations between EM and non-EM NGS at EM-AML. Three of four patients who received treatment with IDH1/2 inhibitors based on EM NGS achieved complete response. Conclusions: EM-AML has a distinct molecular architecture with an inferior OS in LC vs. MS patients. We conclude that EM site NGS is critical in patients with EM-AML, as 52% have potentially targetable mutations and could benefit from specific targeted therapies.[Table: see text]

Linear and generalized linear models are used extensively in many scientific fields, to model observed data and as the basis for hypothesis tests. The use of such models requires specification of a design matrix, and subsequent formulation of contrasts representing scientific hypotheses of interest. Proper execution of these steps requires a thorough understanding of the meaning of the individual coefficients, and is a frequent source of uncertainty for end users. Here, we present an R/Bioconductor package, ExploreModelMatrix, which enables interactive exploration of design matrices and linear model diagnostics. Given a sample annotation table and a desired design formula, the package displays how the model coefficients are combined to give the fitted values for each combination of predictor variables, which allows users to both extract the interpretation of each individual coefficient, and formulate desired linear contrasts. In addition, the interactive interface displays informative characteristics for the regular linear model corresponding to the provided design, such as variance inflation factors and the pseudoinverse of the design matrix.

We present ten simple rules that support converting a legacy vocabulary—a list of terms available in a print-based glossary or in a table not accessible using web standards—into a FAIR vocabulary. Various pathways may be followed to publish the FAIR vocabulary, but we emphasise particularly the goal of providing a globally unique resolvable identifier for each term or concept. A standard representation of the concept should be returned when the individual web identifier is resolved, using SKOS or OWL serialised in an RDF-based representation for machine-interchange and in a web-page for human consumption. Guidelines for vocabulary and term metadata are provided, as well as development and maintenance considerations. The rules are arranged as a stepwise recipe for creating a FAIR vocabulary based on the legacy vocabulary. By following these rules you can achieve the outcome of converting a legacy vocabulary into a standalone FAIR vocabulary, which can be used for unambiguous data annotation. In turn, this increases data interoperability and enables data integration.

Abstract The present paper approaches the theme of “understanding strangers” through discussing some of the methodological issues in interlanguage pragmatics (ILP), with special reference to Hungarian-English Interlanguage (IL) requests. Written discourse completion tasks (WDCT) were used to collect data from 20 English major university students. The CCSARP Project’s 9-scale request strategies table proposed by Blum-Kulka, House, and Kasper (1989) was incorporated into the research, the proposed categories were extended by labels relating to mixed strategies and responses where no answers were provided. The structure of the paper is as follows: after a brief overview of the literature in the field of ILP with a special focus on WDCT, the validity of the methodology is highlighted through the discussion of issues relating to labelling/coding categories as well as interannotator (dis)agreements. By analysing and comparing utterances on the basis of our annotation output and validating the results with the aid of ReCal, we have confirmed that WDCT is a reliable and valid tool for testing ILP competence in speech acts performance.

Netgraph Query Language for the Prague Dependency Treebank 2.0 We study the annotation of the Prague Dependency Treebank 2.0 (PDT 2.0) and assemble a list of requirements on a query language that would allow searching for and studying all linguistic phenomena annotated in the treebank. We propose an extension to the query language of an existing search tool Netgraph 1.0 and show that the extended query language satisfies the list of requirements. We demonstrate how all principal linguistic phenomena annotated in the treebank can be searched for with the proposed query language and compare the query language to some other treebank search systems. The proposed query language has been implemented in the search tool Netgraph - we talk about features of a search tool that can simplify the searching and make it more powerful. We also present a table that shows the extent of usage of various features of the implemented query language by the users of Netgraph and mention several usages of Netgraph for other treebanks than PDT 2.0.

e19533 Background: Therapeutic, prognostic, and diagnostic insights gained from next generation sequencing (NGS) are a key premise of genomics-informed cancer care in hematological diseases. Particularly in BCR-ABL negative myeloproliferative neoplasms (MPN), insights gained from NGS is integral for risk stratification and prognostication. In this study, MPN patients of South Korean descent were sequenced, interpreted, and compared with a published validation cohort to identify variations in mutational profiles specific to demographics. Methods: 31 South Korean MPN patients including 12 essential thrombocythemia, 6 polycythemia vera, 6 primary myelofibrosis, and 7 chronic myelogenous leukemia were sequenced in 2018 and 2019 using the 54 gene Illumina TruSight Myeloid Panel at Hallym University College of Medicine. Orthogonal testing for CALR mutations was done by Sanger sequencing. Watson for Genomics (WfG), an artificial intelligence offering was used for variant interpretation and annotation. A cohort of 151 MPN patients previously published in the New England Journal of Medicine (NEJM) was used for comparison (PMID:24325359). Results: The table shows identified actionable mutations. Conclusions: Two novel pathogenic mutations in CALR (c.1162delG and c.1100_1145del)) were identified in Korean MPN patients. NOTCH1 pathogenic mutations were exclusive while TP53 mutations were significantly enriched in the Korean cohort suggesting that these pathways may play a role in MPN. TP53 mutations in MPN are clinically significant as they have been associated with increased risk for leukemic transformation. Of note, MPL mutations were not detected in the Korean cohort. In conclusion, race and ethnicity may contribute to some mutational signatures in cancer. [Table: see text]

Extensive evaluation of RNA-seq methods have demonstrated that no single algorithm consistently outperforms all others. Removal of unwanted variation (RUV) has also been proposed as a method for stabilizing differential expression (DE) results. Despite this, it remains a challenge to run multiple RNA-seq algorithms to identify significant differences common to multiple algorithms, whilst also integrating and assessing the impact of RUV into all algorithms. consensusDE was developed to automate the process of identifying significant DE by combining the results from multiple algorithms with minimal user input and with the option to automatically integrate RUV. consensusDE only requires a table describing the sample groups, a directory containing BAM files or preprocessed count tables and an optional transcript database for annotation. It supports merging of technical replicates, paired analyses and outputs a compendium of plots to guide the user in subsequent analyses. Herein, we assess the ability of RUV to improve DE stability when combined with multiple algorithms and between algorithms, through application to real and simulated data. We find that, although RUV increased fold change stability between algorithms, it demonstrated improved FDR in a setting of low replication for the intersect, the effect was algorithm specific and diminished with increased replication, reinforcing increased replication for recovery of true DE genes. We finish by offering some rules and considerations for the application of RUV in a consensus-based setting. consensusDE is freely available, implemented in R and available as a Bioconductor package, under the GPL-3 license, along with a comprehensive vignette describing functionality: http://bioconductor.org/packages/consensusDE/.

800x600 Greatly reduced inspection intervals of skin damage repairs pose a significant financial problem for aging commercial aircraft fleets. Such intervals for visual inspections are the result of simplified conservative repair substantiation analyses, based on the same crack propagation scenarios and curves that were established in the initial project development. These neglect the structural role of external repair (“doubler”) and consider only the increase in hidden crack path. A more refined approach to reassessing inspection intervals after a repair may keep maintenance jobs in accordance with common C-check routines in most cases. This approach, based on new crack growth simulations for worst case scenarios that could occur at the region of repair, uses respective kinetic equation and new geometric stress intensity factor functions, obtained in additional FEM (Finite Element Method) analyses. In particular, for standard rectangular repairs, the number of possible geometric configurations is astonishing considering length, width, skin and “doubler” thickness, reinforced panel dimensions, and frame and stringer cross sections. This investigation deals mainly with defining a minimum sufficient number of intermediate crack length values for FEM analyses in each propagation scenario. A conservative but efficient definition of most relevant parameters for a new numerical analysis campaign is another important issue. The results obtained are helpful for the improvement of the operational efficiency and safety of an aging fleet. 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