Annotation Model Research Articles

Extended 3D annotations as a new mechanism to explicitly communicate geometric design intent and increase CAD model reusability

A successful implementation of the Model-Based Enterprise concept (MBE) requires maximizing the potential benefits of annotated 3D models. The foundations of the MBE model are established by digital product definition data practices, which are currently regulated by standards such as ASME Y14.41-2003 and ISO 16792:2006. At the center of the MBE concept is the notion of CAD model reusability, which relies on the idea that 3D CAD models can be reused both throughout the entire product lifecycle and as a starting point for future development of new products. In this context, a critical aspect of CAD model reuse is the proper identification and understanding of the geometric design intent that is usually expressed implicitly within the CAD model.In this work, we present a method to communicate geometric design intent explicitly by overloading and extending the scope of the current annotation instruments available in the MBE approach. We propose a new broader type of model annotation that we call “extended annotation”, where design information is represented both internally within the 3D model and externally, on a separate repository. This structure naturally demands additional mechanisms to support the interaction of users with the information. In order to manage the information stored in these extended annotations effectively, we implemented an annotation manager that automatically synchronizes the dual representation of the annotations. To reduce the visual clutter in the 3D model, the software provides powerful filtering, editing, and visualization capabilities, giving users complete control of the information stored in the model. Finally, a study was conducted with 60 participants to evaluate the performance of the proposed model and the usability of the annotation manager. Results show a statistically significant benefit of using the extended annotation system, suggesting the use of this model as a valuable approach to improve design intent communication.

The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation model.

Knowledge contained within in vivo imaging annotated by human experts or computer programs is typically stored as unstructured text and separated from other associated information. The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation information model is an evolution of the National Institute of Health's (NIH) National Cancer Institute's (NCI) Cancer Bioinformatics Grid (caBIG®) AIM model. The model applies to various image types created by various techniques and disciplines. It has evolved in response to the feedback and changing demands from the imaging community at NCI. The foundation model serves as a base for other imaging disciplines that want to extend the type of information the model collects. The model captures physical entities and their characteristics, imaging observation entities and their characteristics, markups (two- and three-dimensional), AIM statements, calculations, image source, inferences, annotation role, task context or workflow, audit trail, AIM creator details, equipment used to create AIM instances, subject demographics, and adjudication observations. An AIM instance can be stored as a Digital Imaging and Communications in Medicine (DICOM) structured reporting (SR) object or Extensible Markup Language (XML) document for further processing and analysis. An AIM instance consists of one or more annotations and associated markups of a single finding along with other ancillary information in the AIM model. An annotation describes information about the meaning of pixel data in an image. A markup is a graphical drawing placed on the image that depicts a region of interest. This paper describes fundamental AIM concepts and how to use and extend AIM for various imaging disciplines.

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource.

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed.

Reducing Recon 2 for steady-state flux analysis of HEK cell culture

A representative stoichiometric model is essential to perform metabolic flux analysis (MFA) using experimentally measured consumption (or production) rates as constraints. For Human Embryonic Kidney (HEK) cell culture, there is the opportunity to use an extremely well-curated and annotated human genome-scale model Recon 2 for MFA. Performing MFA using Recon 2 without any modification would have implied that cells have access to all functionality encoded by the genome, which is not realistic. The majority of intracellular fluxes are poorly determined as only extracellular exchange rates are measured. This is compounded by the fact that there is no suitable metabolic objective function to suppress non-specific fluxes. We devised a heuristic to systematically reduce Recon 2 to emphasize flux through core metabolic reactions. This implies that cells would engage these dominant metabolic pathways to grow, and any significant changes in gross metabolic phenotypes would have invoked changes in these pathways. The reduced metabolic model becomes a functionalized version of Recon 2 used for identifying significant metabolic changes in cells by flux analysis.

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

Model-driven multicomponent volume exploration

The current multicomponent volume segmentation and labeling methods are mostly hard to get correct segmentation and labeling results automatically and rely hardly on experts' aids, which make related volume exploration to be time consuming, laborious and prone to errors and omissions. To solve this problem, we present a novel volume exploration method driven by admitted model. We first apply Gaussian mixture models to segment the raw volume. However, different components with similar value are still mixed. To segment these components further, we make use of region-grown principle to produce a fine-grained part segmentation. To label different parts automatically, we found that it is helpful to take advantage of annotated model, like human anatomy model (PlasticboyCC, http://www.plasticboy.co.uk/store/index.html , 2013). However, it is not straightforward to label segmented volume with geometric model automatically. Inspired by electors voting (Au et al., Comput Graph Forum 29:645---654, 2010), we propose a volume-model correspondence schema to overcome this intractable challenge. Moreover, it is essential to exploit intuitive interactive methods for interactive exploration, so we also developed practical precise interaction techniques to assist volume exploration. Our experiments with various data and discussion with specialists show that our method provides an efficient and impactful way to explore volume data.

Natural antisense transcripts in Plasmodium falciparum isolates from patients with complicated malaria

Mechanisms regulating gene expression in malaria parasites are not well understood. Little is known about how the parasite regulates its gene expression during transition from one developmental stage to another and in response to various environmental conditions. Parasites in a diseased host face environments which differ from the static, well adapted in vitro conditions. Parasites thus need to adapt quickly and effectively to these conditions by establishing transcriptional states which are best suited for better survival. With the discovery of natural antisense transcripts (NATs) in this parasite and considering the various proposed mechanisms by which NATs might regulate gene expression, it has been speculated that these might be playing a critical role in gene regulation. We report here the diversity of NATs in this parasite, using isolates taken directly from patients with differing clinical symptoms caused by malaria infection. Using a custom designed strand specific whole genome microarray, a total of 797 NATs targeted against annotated loci have been detected. Out of these, 545 NATs are unique to this study. The majority of NATs were positively correlated with the expression pattern of the sense transcript. However, 96 genes showed a change in sense/antisense ratio on comparison between uncomplicated and complicated disease conditions. The antisense transcripts map to a broad range of biochemical/metabolic pathways, especially pathways pertaining to the central carbon metabolism and stress related pathways. Our data strongly suggests that a large group of NATs detected here are unannotated transcription units antisense to annotated gene models. The results reveal a previously unknown set of NATs that prevails in this parasite, their differential regulation in disease conditions and mapping to functionally well annotated genes. The results detailed here call for studies to deduce the possible mechanism of action of NATs, which would further help in understanding the in vivo pathological adaptations of these parasites.

@Java: Bringing a richer annotation model to Java

The ability to annotate code and, in general, the capability to attach arbitrary meta-data to portions of a program are features that have become more and more common in programming languages.Annotations in Java make it possible to attach custom, structured meta-data to declarations of classes, fields and methods. However, the mechanism has some limits: annotations can only decorate declarations and their instantiation can only be resolved statically.With this work, we propose an extension to Java (named @Java) with a richer annotation model, supporting code block and expression annotations, as well as dynamically evaluated members. In other words, in our model, the granularity of annotations extends to the statement and expression level and annotations may hold the result of runtime-evaluated expressions.Our extension to the Java annotation model is twofold: (i) we introduced block and expression annotations and (ii) we allow every annotation to hold dynamically evaluated values. Our implementation also provides an extended reflection API to support inspection and retrieval of our enhanced annotations.

Automatic Image Annotation Based on Co-Training

Automatic image annotation is a critical and challenging problem in pattern recognition and image understanding areas. There are some problems in existing automatic image annotation areas. For example, the size of unlabeled data is much larger than the labeled data. Besides, most image annotation models can only use one kind of image segmentation strategy and certain image description method. According to the above problems, an automatic image annotation model based on Co-training is proposed. In this model, four independent feature properties are constructed and then four corresponding sub-classifiers are built. In this way, different image segmentation strategies and feature representation methods can be integrated into a unified framework. An adaptive algorithm based on vote and consistency is proposed to extend the training dataset. The proposed method use Co-training algorithm and mass unlabeled data to improve the performance of automatic image annotation. Experiments conducted on Corel 5 K dataset verify the effectiveness of proposed method.

Genetic characterization of the Neurospora crassa molybdenum cofactor biosynthesis

Molybdenum (Mo) is a trace element that is essential for important cellular processes. To gain biological activity, Mo must be complexed in the molybdenum cofactor (Moco), a pterin derivative of low molecular weight. Moco synthesis is a multi-step pathway that involves a variable number of genes in eukaryotes, which are assigned to four steps of eukaryotic Moco biosynthesis. Moco biosynthesis mutants lack any Moco-dependent enzymatic activities, including assimilation of nitrate (plants and fungi), detoxification of sulfite (humans and plants) and utilization of hypoxanthine as sole N-source (fungi). We report the first comprehensive genetic characterization of the Neurospora crassa (N. crassa) Moco biosynthesis pathway, annotating five genes which encode all pathway enzymes, and compare it with the characterized Aspergillus nidulans pathway. Biochemical characterization of the corresponding knock-out mutants confirms our annotation model, documenting the N. crassa/A. nidulans (fungal) Moco biosynthesis as unique, combining the organizational structure of both plant and human Moco biosynthesis genes.

A Novel Semantic Statistical Model for Automatic Image Annotation Using the Relationship between the Regions Based on Multi-Criteria Decision Making

Automatic image annotation has emerged as an important research topic due to the existence of the semantic gap and in addition to its potential application on image retrieval and management. In this paper we present an approach which combines regional contexts and visual topics to automatic image annotation. Regional contexts model the relationship between the regions, whereas visual topics provide the global distribution of topics over an image. Conventional image annotation methods neglected the relationship between the regions in an image, while these regions are exactly explanation of the image semantics, therefore considering the relationship between them are helpful to annotate the images. The proposed model extracts regional contexts and visual topics from the image, and incorporates them by MCDM (Multi Criteria Decision Making) approach based on TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method. Regional contexts and visual topics are learned by PLSA (Probability Latent Semantic Analysis) from the training data. The experiments on 5k Corel images show that integrating these two kinds of information is beneficial to image annotation. DOI: http://dx.doi.org/10.11591/ijece.v4i1.4598

An Efficient Method for Noisy Annotation Data Modeling

Probabilistic topic models are used for analyzing and extracting content-related annotations from noisy annotated discrete data like WebPages on WWW and these WebPages are stored using social bookmarking services with the help of social bookmarking services, reason behind this process most of time users can attach annotations freely, some annotations do not describe the semantics of the content, therefore they are noisy, simply they are not content related. The extraction of content-related annotations can be used as a prepossessing step in machine learning. Prepossessing step in machine learning is like text classification and image recognition, and can improve information retrieval performance. The proposed model is a generative model for content and annotations, where annotations are assumed to be originated either from topics that generated the content or from a general distribution unrelated to the content. We demonstrate the effectiveness of the proposed method with the help of synthetic data and real social annotation data for text and images.

Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications.

BackgroundScientific publications are documentary representations of defeasible arguments, supported by data and repeatable methods. They are the essential mediating artifacts in the ecosystem of scientific communications. The institutional “goal” of science is publishing results. The linear document publication format, dating from 1665, has survived transition to the Web.Intractable publication volumes; the difficulty of verifying evidence; and observed problems in evidence and citation chains suggest a need for a web-friendly and machine-tractable model of scientific publications. This model should support: digital summarization, evidence examination, challenge, verification and remix, and incremental adoption. Such a model must be capable of expressing a broad spectrum of representational complexity, ranging from minimal to maximal forms.ResultsThe micropublications semantic model of scientific argument and evidence provides these features. Micropublications support natural language statements; data; methods and materials specifications; discussion and commentary; challenge and disagreement; as well as allowing many kinds of statement formalization.The minimal form of a micropublication is a statement with its attribution. The maximal form is a statement with its complete supporting argument, consisting of all relevant evidence, interpretations, discussion and challenges brought forward in support of or opposition to it. Micropublications may be formalized and serialized in multiple ways, including in RDF. They may be added to publications as stand-off metadata.An OWL 2 vocabulary for micropublications is available at http://purl.org/mp. A discussion of this vocabulary along with RDF examples from the case studies, appears as OWL Vocabulary and RDF Examples in Additional file 1.ConclusionMicropublications, because they model evidence and allow qualified, nuanced assertions, can play essential roles in the scientific communications ecosystem in places where simpler, formalized and purely statement-based models, such as the nanopublications model, will not be sufficient. At the same time they will add significant value to, and are intentionally compatible with, statement-based formalizations.We suggest that micropublications, generated by useful software tools supporting such activities as writing, editing, reviewing, and discussion, will be of great value in improving the quality and tractability of biomedical communications.

Preserving sequence annotations across reference sequences.

BackgroundMatching and comparing sequence annotations of different reference sequences is vital to genomics research, yet many annotation formats do not specify the reference sequence types or versions used. This makes the integration of annotations from different sources difficult and error prone.ResultsAs part of our effort to create linked data for interoperable sequence annotations, we present an RDF data model for sequence annotation using the ontological framework established by the OBO Foundry ontologies and the Basic Formal Ontology (BFO). We defined reference sequences as the common domain of integration for sequence annotations, and identified three semantic relationships between sequence annotations. In doing so, we created the Reference Sequence Annotation to compensate for gaps in the SO and in its mapping to BFO, particularly for annotations that refer to versions of consensus reference sequences. Moreover, we present three integration models for sequence annotations using different reference assemblies.ConclusionsWe demonstrated a working example of a sequence annotation instance, and how this instance can be linked to other annotations on different reference sequences. Sequence annotations in this format are semantically rich and can be integrated easily with different assemblies. We also identify other challenges of modeling reference sequences with the BFO.

Multilabel image annotation based on double-layer PLSA model.

Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. The low-level features of images are represented as visual words by Bag-of-Words model; latent semantic topics are obtained by the first layer PLSA from two aspects of visual and texture, respectively. Furthermore, we adopt the second layer PLSA to fuse the visual and texture latent semantic topics and achieve a top-layer latent semantic topic. By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. Experimental results demonstrate that our automatic image annotation model based on double-layer PLSA can achieve promising performance for labeling and outperform previous methods on standard Corel dataset.

Clustering hidden Markov models with variational HEM

The hidden Markov model (HMM) is a widely-used generative model that copes with sequential data, assuming that each observation is conditioned on the state of a hidden Markov chain. In this paper, we derive a novel algorithm to HMMs based on the hierarchical EM (HEM) algorithm. The proposed algorithm i) clusters a given collection of HMMs into groups of HMMs that are similar, in terms of the distributions they represent, and ii) characterizes each group by a cluster center, that is, a novel HMM that is representative for the group, in a manner that is consistent with the underlying generative model of the HMM. To cope with intractable inference in the E-step, the HEM algorithm is formulated as a variational optimization problem, and efficiently solved for the HMM case by leveraging an appropriate variational approximation. The benefits of the proposed algorithm, which we call variational HEM (VHEM), are demonstrated on several tasks involving time-series data, such as hierarchical clustering of motion capture sequences, and automatic annotation and retrieval of music and of online hand-writing data, showing improvements over current methods. In particular, our variational HEM algorithm effectively leverages large amounts of data when learning annotation models by using an efficient hierarchical estimation procedure, which reduces learning times and memory requirements, while improving model robustness through better regularization.

A new annotation system based on a semantic analysis of a learner's annotative activity to invoke web services

Many annotation systems proposed in the literature suffer from an under-exploitation of the annotation's semantic at the level of the assistance presented to the learner during annotative activity. Thus, these tools offer the same classic features to its users, such as management, search, sharing and storage of annotations. In this paper, we propose a new annotation system which differs in its novel features. The system tries to assist the learner via web services during learning activities. Therefore, from a user's annotation, our system is able to interpret a goal implicitly expressed and tries to discover and invoke a web service that can meet this annotation's objective. This system is based on an annotation model composed of an ontology and pattern annotation. This enrichment will provide advantages for both the provider, who will find his service within the consumer's reach, and the learner, who will have at his disposal an intelligent annotation tool.

Semantic Annotation: The Mainstay of Semantic Web

Given that semantic Web realization is based on the critical mass of metadata accessibility and the representation of data with formal knowledge, it needs to generate metadata that is specific, easy to understand and well-defined. However, semantic annotation of the web documents is the successful way to make the Semantic Web vision a reality. This paper introduces the Semantic Web and its vision (stack layers) with regard to some concept definitions that helps the understanding of semantic annotation. Additionally, this paper introduces the semantic annotation categories, tools, domains and models.

English

This paper describes the model for semantic annotation of educational materials and related concepts, as well as the structure of dependent services that extend the functionalities of current learning management systems (LMS). The goal of the model is to enable simple discovery, while allowing formulation and execu- tion of advanced search queries, augmented with visualizations of result sets and domain structure and adaptation of results according to students' interests. Several components were also implemented and their descriptions and other remarks are given following the description of the model. In modern educational systems, the final outcome of the learning process heavily depends on providing learn- ers with an access to the right information at the right time. An appropriate presentation and visualization of information can strongly influence the learning experience by allowing learners to explore an educational do- main instinctively, gaining a better insight into its structure. The main goal of this paper is to enhance data man- agement, visualization, and adaptation within an e-learning system. A model for semantic annotation of concepts present in learning management systems is presented, including the description of a structure of se- mantic applications that will utilize annotated resources to enhance data handling, browsing, searching, vi- sualization, and adaptivity. The developed solution is based around the principles of modularity and independency among components.

Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development

BackgroundThe ever-growing wealth of biological information available through multiple comprehensive database repositories can be leveraged for advanced analysis of data. We have now extensively revised and updated the multi-purpose software tool Biofilter that allows researchers to annotate and/or filter data as well as generate gene-gene interaction models based on existing biological knowledge. Biofilter now has the Library of Knowledge Integration (LOKI), for accessing and integrating existing comprehensive database information, including more flexibility for how ambiguity of gene identifiers are handled. We have also updated the way importance scores for interaction models are generated. In addition, Biofilter 2.0 now works with a range of types and formats of data, including single nucleotide polymorphism (SNP) identifiers, rare variant identifiers, base pair positions, gene symbols, genetic regions, and copy number variant (CNV) location information.ResultsBiofilter provides a convenient single interface for accessing multiple publicly available human genetic data sources that have been compiled in the supporting database of LOKI. Information within LOKI includes genomic locations of SNPs and genes, as well as known relationships among genes and proteins such as interaction pairs, pathways and ontological categories.Via Biofilter 2.0 researchers can:• Annotate genomic location or region based data, such as results from association studies, or CNV analyses, with relevant biological knowledge for deeper interpretation• Filter genomic location or region based data on biological criteria, such as filtering a series SNPs to retain only SNPs present in specific genes within specific pathways of interest• Generate Predictive Models for gene-gene, SNP-SNP, or CNV-CNV interactions based on biological information, with priority for models to be tested based on biological relevance, thus narrowing the search space and reducing multiple hypothesis-testing.ConclusionsBiofilter is a software tool that provides a flexible way to use the ever-expanding expert biological knowledge that exists to direct filtering, annotation, and complex predictive model development for elucidating the etiology of complex phenotypic outcomes.