Types Of Annotations Research Articles

Digitisation, Big Data, and the Future of the Medical Humanities: Text-Mining and the History of Medicine: Big Data, Big Questions?

Digitisation, Big Data, and the Future of the Medical Humanities: Text-Mining and the History of Medicine: Big Data, Big Questions?

Review of Interactive Video–Romanian Project Proposal

<p class="apa">In the recent years, the globalization and massification of video education offer involved more and more eLearning scenarios within universities. This article refers to interactive video and proposes an overview of it. We analyze the background information, regarding the eLearning campus used in virtual universities around the world, the MOOC movement in the last year, and the related interactive video platforms in the (education) field. At the same time, we pay particular attention to technical aspects of the interactive video: defining concept, types of video metadata, media fragments and types of annotations, as primordial elements that bring interactivity. We tested some free and commercial interactive web application. We gathered all the ideas. We propose a framework for an interactive system web based on the main modules: video resource management (production, transcoding and storage), annotations, Linked Open Data, distribution medium, player interface, data analytics and recommendation system. On the way, we offer our findings, together with our recommendations for an annotation interface and player module. It is our idea for Politehnica University Timisoara, either as a standalone solution or a complement to actual virtual campus (http://cv.upt.ro) depending on future development plans and financial aspects.</p>

Modular deconvolution of tissue transcriptomes: pitfalls and solutions

BackgroundMaking hypothesis-led interrogations of whole-genome transcriptomic datasets is a challenge. Transcriptional modules define specific biological processes, but multiple modules have now been published with overlapping annotations. In this study we aimed to generate and validate the modular discrimination index (MDI) score, ranking the accuracy and resolution of any cell type specific module to assess cellular enrichment in tissues from transcriptional datasets. MethodsWe made use of publicly available transcriptomic datasets of purified immune cell types to compare the expression of modules annotated to reflect the expression of different immune cells. MDI scores were derived from the relative expression of each module in its annotated cell type, relative to all other cells. Independent datasets from human tissue and blood samples were then used to validate the MDI score in relation to the relative enrichment of different modules' gene expression. FindingsWe found that 26 T-cell modules varied both in their composition and in their gene expression in a tuberculin skin test, a prototypical T cell driven cell inflammatory process. To address this heterogeneity, we assessed the accuracy of cell-type modules, by calculating MDI scores for all published T cell, B cell, natural killer cell and neutrophil modules. MDI scores strongly predicted each module's ability to show histologically proven cellular enrichment in a variety of independent tissue samples: T cells in psoriatic skin, B cells in lung fibrosis, and neutrophils in erythema nodosum leprosum skin lesions. In addition, MDI scores were also validated in relation to changes in cell frequency in blood: modules with the greatest MDI score were most predictive of each module's association with cell numbers for both high frequency cells (neutrophils r2=0·545, p=0·01) and low frequency cells (B cells r2=0·865, p=0·01) in blood. Finally, we showed that modules with the highest MDI score elicited the T cell enrichment seen in the tuberculin skin test, whereas this was most often not detected by low MDI scoring modules. InterpretationThis study shows that MDI scoring provides a robust and reproducible assessment of a transcriptional module's accuracy and resolution, and should be determined before using modular deconvolution of tissue samples. The MDI scoring framework can be extended and applied across a range of disciplines for which annotated modules could be used to interrogate transcriptional datasets. FundingWellcome Trust.

BLAST-based structural annotation of protein residues using Protein Data Bank.

BackgroundIn the era of next-generation sequencing where thousands of genomes have been already sequenced; size of protein databases is growing with exponential rate. Structural annotation of these proteins is one of the biggest challenges for the computational biologist. Although, it is easy to perform BLAST search against Protein Data Bank (PDB) but it is difficult for a biologist to annotate protein residues from BLAST search.ResultsA web-server StarPDB has been developed for structural annotation of a protein based on its similarity with known protein structures. It uses standard BLAST software for performing similarity search of a query protein against protein structures in PDB. This server integrates wide range modules for assigning different types of annotation that includes, Secondary-structure, Accessible surface area, Tight-turns, DNA-RNA and Ligand modules. Secondary structure module allows users to predict regular secondary structure states to each residue in a protein. Accessible surface area predict the exposed or buried residues in a protein. Tight-turns module is designed to predict tight turns like beta-turns in a protein. DNA-RNA module developed for predicting DNA and RNA interacting residues in a protein. Similarly, Ligand module of server allows one to predicted ligands, metal and nucleotides ligand interacting residues in a protein.ConclusionsIn summary, this manuscript presents a web server for comprehensive annotation of a protein based on similarity search. It integrates number of visualization tools that facilitate users to understand structure and function of protein residues. This web server is available freely for scientific community from URL http://crdd.osdd.net/raghava/starpdb.ReviewersThis article was reviewed by Prof Michael Gromiha, Prof. Thomas Dandekar and Dr. I. King Jordan.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0106-9) contains supplementary material, which is available to authorized users.

Design and Evaluation of Data Annotation Workflows for CAVE-like Virtual Environments.

Data annotation finds increasing use in Virtual Reality applications with the goal to support the data analysis process, such as architectural reviews. In this context, a variety of different annotation systems for application to immersive virtual environments have been presented. While many interesting interaction designs for the data annotation workflow have emerged from them, important details and evaluations are often omitted. In particular, we observe that the process of handling metadata to interactively create and manage complex annotations is often not covered in detail. In this paper, we strive to improve this situation by focusing on the design of data annotation workflows and their evaluation. We propose a workflow design that facilitates the most important annotation operations, i.e., annotation creation, review, and modification. Our workflow design is easily extensible in terms of supported annotation and metadata types as well as interaction techniques, which makes it suitable for a variety of application scenarios. To evaluate it, we have conducted a user study in a CAVE-like virtual environment in which we compared our design to two alternatives in terms of a realistic annotation creation task. Our design obtained good results in terms of task performance and user experience.

The Impact of Multimedia Annotations on Second Language Vocabulary Acquisition and Reading Comprehension

Abstract The purpose of this study is to review research literature regarding the impact of multimedia annotations on second language (L2) vocabulary acquisition and reading comprehension. Many studies have endeavored to show how different types of multimedia annotations facilitate L2 reading comprehension and vocabulary acquisition, including multiple multimedia annotations, coupled multimedia annotations, and single multimedia annotations. The studies discussed compared traditional glosses with multimedia glosses on one hand, and compared the use of various types of multimedia gloss on the other hand. This research showed that multimedia annotations facilitate L2 reading comprehension and vocabulary acquisition. In addition, the research concluded that implementing multimedia verbal glosses coupled with textual glosses is more effective for second language learners’ performance in reading and vocabulary learning. The research participants included only adult students, who ranged from high school to college age. Limitations and pedagogical implications are mentioned for further studies in this field.

Abstract 1999: Proteogenomic characterization of breast cancer sub-types in patient derived xenografts

Abstract The goal of this talk is to introduce an integrated quantitative proteogenomic approach to comprehensively map proteomic information back to their encoding genes. We seek evidence from mass spectrometry-based large-scale proteomic data of patient populations in conjunction with patient-centric next-generation sequencing data and unbiased sequencing strategies to study breast cancer (BC) subtypes from a genomic context. We have obtained global and phosphoproteomic data with matching next generation sequencing data for 18 patient-derived xenografts (PDXs) representing the major clinical subtypes of BC. Our workflow starts with the creation of several protein sequence databases that serve as a template for mass spectrometry database identifications. These databases include 1) completely annotated reference protein sequences, 2) patient-specific databases that were created using next generation sequencing data, 3) isoform databases that contain all possible splicing combinations, and 4) amino acid sequence database resulting from a six-frame translation of the entire human reference and customized genomes. All mass spectrometry raw data are searched against the databases for obtaining identifications at the peptide level, and assembly of peptides for quantification using taxonomy-based label-free quantitation (LFQ) that can specifically quantify unique human peptide sequences found in PDXs. The peptides are then mapped to the human genome and visualized using a genome browser. Quantitative changes across PDXs are presented at the protein level or at the isoform level via peptide role-up to specific exons and visualized as a quantitative data track. By combining search results from these databases we obtain a comprehensive view of our PDXs. The complementary nature of the databases enable greater proteomic depth, i.e. databases with complete splicing combinations capture proteomic evidence when patient-specific databases fail due to possible erroneous RNA-seq reads. Similarly, 6-frame translated amino acid databases can capture potentially novel coding regions but are unable to detect splicing. Peptide maps are obtained for individual genes or specific protein isoforms covering both knowledge-driven and novel genomic annotation types. We compile peptides carrying variants, splice junctions, fusions, and new coding regions specific to each PDX or in common with a specific BC subtype. Majority of data is mapped back to the genome loci using unmodified peptides via global proteomics while phosphopeptides that contain variants and splicing are also mapped in a similar manner using phosphoproteomic data. We have currently annotated 455 novel proteogenomic hits covering many examples outlined above for genes related to breast cancer and show how these can be specifically identified and in some instances differentially quantified in the PDX models. Citation Format: Harsha P. Gunawardena, John A. Wrobel, Jonathon O'Brien, Ling Xie, Petra Erdmann-Gilmore, Sherri R. Davies, Shunqiang Li, Song Cao, Michael McLellan, Kelly V. Ruggles, David Fenyo, R. Reid Townsend, Li Ding, Bahjat F. Qaqish, Matthew J. Ellis, Xian Chen. Proteogenomic characterization of breast cancer sub-types in patient derived xenografts. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1999. doi:10.1158/1538-7445.AM2015-1999

Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation

We consider the problem of parameter estimation and energy minimization for a region-based semantic segmentation model. The model divides the pixels of an image into non-overlapping connected regions, each of which is to a semantic class. In the context of energy minimization, the main problem we face is the large number of putative pixel-to-region assignments. We address this problem by designing an accurate linear programming based approach for selecting the best set of regions from a large dictionary. The dictionary is constructed by merging and intersecting segments obtained from multiple bottom-up over-segmentations. The linear program is solved efficiently using dual decomposition. In the context of parameter estimation, the main problem we face is the lack of fully supervised data. We address this issue by developing a principled framework for parameter estimation using diverse data. More precisely, we propose a latent structural support vector machine formulation, where the latent variables model any missing information in the human annotation. Of particular interest to us are three types of annotations: (i) images segmented using generic foreground or background classes; (ii) images with bounding boxes specified for objects; and (iii) images labeled to indicate the presence of a class. Using large, publicly available datasets we show that our methods are able to significantly improve the accuracy of the region-based model.

LEARNER CORPUS ANNOTATION IN LATVIA AND LITHUANIA

Learner corpora are gaining popularity in the Baltic States as well as elsewhere in the world. The aim of the article is to discuss what kinds of annotation have been used in learner corpus research in Latvia and Lithuania so far and to describe which ones of them would be most suitable for the newly created learner corpus of the second Baltic language – Esam . A lot of learner corpus research in Latvia and Lithuania is undertaken without any annotation. The most common types of annotation are the ones based on the theory of levels of language – morphological and syntactic annotation. There is little collaboration between researchers of neighbour countries, but linguists of each country collaborate closely with each other using similar annotation schemes and creating corpora that are comparable in some aspects. The learner corpus of the second Baltic language should try to fit in the picture to some extent. Part of speech annotation and simple syntactic annotation could help in that. However, things that have not yet become so popular in learner corpus research in this region could also be useful. Therefore, error annotation and lemmatization have been chosen to be included in the annotation plan of the corpus Esam as well. DOI: http://dx.doi.org/10.7220/2335-2027.7.8

A Unified Framework for Semantic Comparison of Objects: Extension to Semantic Graphcomparison

Comparing objects described (or annotated) with an ontology is quite important in several application domains. In our previous work we have shown that when objects are annotated with single concepts or sets of concepts, most of the semantic measures found in literature can be rewritten by using a unified form and the notion of approximated information content. In this paper, we argue how this unified form and the notion of approximated information content can be extended to semantic graphs, being the latter resulting from objects annotated with graphs of concepts connected via relationships found in the ontology. We show that open issues and distortions found in the relevant state of the art are put under control. The resulting unified framework (covering all types of annotations, from single concepts to graphs of concepts) is therefore relevant for practicing, in a unique environment, several semantic measures, as required for building a new one or selecting a measure for given application objectives.

An annotated type catalogue of the turtles (Testudines: Pleurodira: Chelidae) in the collection of the Western Australian Museum

An annotated catalogue is provided for the type specimens of turtles (Testudines: Chelidae) in the herpetological collection of the Western Australian Museum. The collection currently holds eight type

Student Annotations Triggered by Teacher Feedback in L2 Business Writing

Courses that explore business writing in the second language in Korea have become the new necessity for college students before entering the workplace. The purpose of this study, therefore, attempts to investigate the effects of teacher feedback on the student annotations in L2 business writing to improve and revise the business letters more effectively. Over the 5-week intensive period, the student annotation group with comment on comment (SACC) and the student annotation group (SA) completed three persuasive business letters: the pretest, the in-class writing task, and the post-test. In the SACC group, once the students received the draft with the teacher feedback, the students responded to the teacher feedback by giving two additional types of student annotations: a success indicator of reflection and a student comment on the teacher comment in the in-class writing task. The results of the study indicate significant difference in the degree of involvement in terms of the total number and the types of student annotations triggered by teacher feedback. The findings reveal positive effects of business writing instruction on student annotations: (1) to elicit better teacher feedback to match the needs of the L2 student-writer and (2) to increase student-teacher, student-text, and within the student participation and interaction.

Late data layout

Values need to be represented differently when interacting with certain language features. For example, an integer has to take an object-based representation when interacting with erased generics, although, for performance reasons, the stack-based value representation is better. To abstract over these implementation details, some programming languages choose to expose a unified high-level concept (the integer) and let the compiler choose its exact representation and insert coercions where necessary. This pattern appears in multiple language features such as value classes, specialization and multi-stage programming: they all expose a unified concept which they later refine into multiple representations. Yet, the underlying compiler implementations typically entangle the core mechanism with assumptions about the alternative representations and their interaction with other language features. In this paper we present the Late Data Layout mechanism, a simple but versatile type-driven generalization that subsumes and improves the state-of-the-art representation transformations. In doing so, we make two key observations: (1) annotated types conveniently capture the semantics of using multiple representations and (2) local type inference can be used to consistently and optimally introduce coercions. We validated our approach by implementing three language features as Scala compiler extensions: value classes, specialization (using the miniboxing representation) and a simplified multi-stage programming mechanism.

Promoting different reading comprehension levels through online annotations

Previous studies have evaluated reading comprehension as the general understanding of reading texts. However, this broad and generic assessment of reading comprehension overlooks the specific aspects and processes that students need to develop. This study adopted Kintsch's Construction–Integration model to tap into reading comprehension at different levels and investigated how English as a foreign language (EFL) students benefit from online annotations to scaffold reading comprehension. Fifty EFL students participated in this study and the data collected included (1) the pre- and post-test assessments in reading comprehension levels, (2) the students’ online annotations on reading texts, and (3) the discussion transcripts from an online discussion forum. The results from the first two data sources (1 and 2) were the data-sets used to investigate the influence of the annotation types on reading comprehension levels. The third data source was the data-set used to probe into the process of how students benefited from annotation types to reach different levels of reading comprehension. Four types of online annotations were frequently used by the students while reading online, including (1) marking vocabulary, (2) adding Chinese explanatory notes to unknown vocabulary, (3) marking text information, and (4) adding summary notes to each paragraph. Different frequencies of using the four types of online annotations resulted in three different comprehension levels: surface-based, text-based, and situation-based levels. Marking vocabulary and adding Chinese explanatory notes helped scaffold the students' learning to achieve surface comprehension levels to facilitate recognition and meaning of unknown vocabulary. Marking text information and in particular, the adding of summary notes to each paragraph were the core annotations that helped students comprehend the online text in text-based and situation-based levels. EFL teachers can guide students to use online annotations to different extents in order to deepen their reading comprehension.

Noninvasive fetal QRS detection using an echo state network and dynamic programming

We address a classical fetal QRS detection problem from abdominal ECG recordings with a data-driven statistical machine learning approach. Our goal is to have a powerful, yet conceptually clean, solution. There are two novel key components at the heart of our approach: an echo state recurrent neural network that is trained to indicate fetal QRS complexes, and several increasingly sophisticated versions of statistics-based dynamic programming algorithms, which are derived from and rooted in probability theory. We also employ a standard technique for preprocessing and removing maternal ECG complexes from the signals, but do not take this as the main focus of this work. The proposed approach is quite generic and can be extended to other types of signals and annotations. Open-source code is provided.

Static analysis of XML security views and query rewriting

In this paper, we revisit the view based security framework for XML without imposing any of the previously considered restrictions on the class of queries, the class of DTDs, and the type of annotations used to define the view. First, we study query rewriting with views when the classes used to define queries and views are Regular XPath and MSO. Next, we investigate problems of static analysis of security access specifications (SAS): we introduce the novel class of interval-bounded SAS and we define three different manners to compare views (i.e. queries) from a security point of view. We provide a systematic study of the complexity for deciding these three comparisons, when the depth of the XML documents is bounded, when the document may have an arbitrary depth but the queries defining the views are restricted to guarantee the interval-bounded property, and in the general setting without restriction on queries and document.

'RetinoGenetics': a comprehensive mutation database for genes related to inherited retinal degeneration.

Inherited retinal degeneration (IRD), a leading cause of human blindness worldwide, is exceptionally heterogeneous with clinical heterogeneity and genetic variety. During the past decades, tremendous efforts have been made to explore the complex heterogeneity, and massive mutations have been identified in different genes underlying IRD with the significant advancement of sequencing technology. In this study, we developed a comprehensive database, ‘RetinoGenetics’, which contains informative knowledge about all known IRD-related genes and mutations for IRD. ‘RetinoGenetics’ currently contains 4270 mutations in 186 genes, with detailed information associated with 164 phenotypes from 934 publications and various types of functional annotations. Then extensive annotations were performed to each gene using various resources, including Gene Ontology, KEGG pathways, protein–protein interaction, mutational annotations and gene–disease network. Furthermore, by using the search functions, convenient browsing ways and intuitive graphical displays, ‘RetinoGenetics’ could serve as a valuable resource for unveiling the genetic basis of IRD. Taken together, ‘RetinoGenetics’ is an integrative, informative and updatable resource for IRD-related genetic predispositions.Database URL: http://www.retinogenetics.org/.

Evaluating the effects of machine pre-annotation and an interactive annotation interface on manual de-identification of clinical text

The Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor method requires removal of 18 types of protected health information (PHI) from clinical documents to be considered “de-identified” prior to use for research purposes. Human review of PHI elements from a large corpus of clinical documents can be tedious and error-prone. Indeed, multiple annotators may be required to consistently redact information that represents each PHI class. Automated de-identification has the potential to improve annotation quality and reduce annotation time. For instance, using machine-assisted annotation by combining de-identification system outputs used as pre-annotations and an interactive annotation interface to provide annotators with PHI annotations for “curation” rather than manual annotation from “scratch” on raw clinical documents. In order to assess whether machine-assisted annotation improves the reliability and accuracy of the reference standard quality and reduces annotation effort, we conducted an annotation experiment. In this annotation study, we assessed the generalizability of the VA Consortium for Healthcare Informatics Research (CHIR) annotation schema and guidelines applied to a corpus of publicly available clinical documents called MTSamples. Specifically, our goals were to (1) characterize a heterogeneous corpus of clinical documents manually annotated for risk-ranked PHI and other annotation types (clinical eponyms and person relations), (2) evaluate how well annotators apply the CHIR schema to the heterogeneous corpus, (3) compare whether machine-assisted annotation (experiment) improves annotation quality and reduces annotation time compared to manual annotation (control), and (4) assess the change in quality of reference standard coverage with each added annotator’s annotations.

The Automatic Identification of the Producers of Co-occurring Communicative Behaviours

Multimodal communicative behaviours depend on numerous factors such as the communicative situation, the task, the culture and respective relationship of the people involved, their role, age, and background. This paper addresses the identification of the producers of co-occurring communicative non-verbal behaviours in a manually annotated multimodal corpus of spontaneous conversations. The work builds upon a preceding study in which a support vector machine was trained to identify the producers of communicative body behaviours using the annotations of individual behaviour types. In the present work, we investigate to which extent classification results can be improved adding to the training data the shape description of co-occurring body behaviours and temporal information. The inclusion of co-occurring behaviours reflects the fact that people often use more body behaviours at the same time when they communicate. The results of the classification experiments show that the identification of the producers of communicative behaviours improves significantly if co-occurring behaviours are added to the training data. Classification performance further improves when it also uses temporal information. Even though the results vary from body type to body type, they all show that the individual variation of communicative behaviours is large even in a very homogeneous group of people and that this variation is better modelled using information on co-occurring behaviours than individual behaviours. Being able to identify and then react correctly to individual behaviours of people is extremely important in the field of social robotics which involves the use of robots in private homes where they must interact in a natural way with different types of persons having varying needs.

Joint Analysis of Functional Genomic Data and Genome-wide Association Studies of 18 Human Traits

Annotations of gene structures and regulatory elements can inform genome-wide association studies (GWASs). However, choosing the relevant annotations for interpreting an association study of a given trait remains challenging. I describe a statistical model that uses association statistics computed across the genome to identify classes of genomic elements that are enriched with or depleted of loci influencing a trait. The model naturally incorporates multiple types of annotations. I applied the model to GWASs of 18 human traits, including red blood cell traits, platelet traits, glucose levels, lipid levels, height, body mass index, and Crohn disease. For each trait, I used the model to evaluate the relevance of 450 different genomic annotations, including protein-coding genes, enhancers, and DNase-I hypersensitive sites in over 100 tissues and cell lines. The fraction of phenotype-associated SNPs influencing protein sequence ranged from around 2% (for platelet volume) up to around 20% (for low-density lipoprotein cholesterol), repressed chromatin was significantly depleted for SNPs associated with several traits, and cell-type-specific DNase-I hypersensitive sites were enriched with SNPs associated with several traits (for example, the spleen in platelet volume). Finally, reweighting each GWAS by using information from functional genomics increased the number of loci with high-confidence associations by around 5%.