Keyword Annotation Research Articles

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy dataset containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.

Gene expression changes in sickle cell reticulocytes and their clinical associations

Transcriptional changes in compensatory erythropoiesis in sickle cell anemia (SCA) and their disease modulation are unclear. We detected 1226 differentially expressed genes in hemoglobin SS reticulocytes compared to non-anemic hemoglobin AA controls. Assessing developmental expression changes in hemoglobin AA erythroblasts for these genes suggests heightened terminal differentiation in early erythroblasts in SCA that diminishes toward the polychromatic to orthochromatic stage transition. Comparison of reticulocyte gene expression changes in SCA with that in Chuvash erythrocytosis, a non-anemic disorder of increased erythropoiesis due to constitutive activation of hypoxia inducible factors, identified 453 SCA-specific changes attributable to compensatory erythropoiesis. Peripheral blood mononuclear cells (PBMCs) in SCA contain elevated proportions of erythroid progenitors due to heightened erythropoiesis. Deconvolution analysis in PBMCs from 131 SCA patients detected 54 genes whose erythroid expression correlated with erythropoiesis efficiency, which were enriched with SCA-specific changes (OR = 2.9, P = 0.00063) and annotation keyword “ubiquitin-dependent protein catabolic process”, “protein ubiquitination”, and “protein polyubiquitination” (OR = 4.2, P = 7.5 × 10–5). An erythroid expression quantitative trait locus of one of these genes, LNX2 encoding an E3 ubiquitin ligase, associated with severe pain episodes in 774 SCA patients (OR = 1.7, P = 3.9 × 10–5). Thus, erythroid gene transcription responds to unique conditions within SCA erythroblasts and these changes potentially correspond to vaso-occlusive manifestations.

Amaranth Genomic Resource Database: an integrated database resource of Amaranth genes and genomics.

Amaranth (Amaranthus L.) is native to Mexico and North America, where it was cultivated thousands of years ago, but now amaranth is grown worldwide. Amaranth is one of the most promising food crops with high nutritional value and belongs to the family Amaranthaceae. The high-quality genome assembly of cultivated amaranth species (A. hypochondriacus, A. cruentus) and wild/weedy species (A. tuberculatus, A. hybridus, and A. palmeri) has already been reported; therefore, we developed an Amaranth Genomic Resource Database (AGRDB) to provide access to all the genomic information such as genes, SSRs, SNPs, TFs, miRNAs, and transporters in one place. The AGRDB database contains functionally annotated gene information with their sequence details, genic as well as genomic SSRs with their three sets of primers, transcription factors classified into different families with their sequence information and annotation details, putative miRNAs with their family, sequences, and targeted gene details, transporter genes with their superfamily, trans-membrane domain details, and details of genic as well as nongenic SNPs with 3' and 5' flanking sequence information of five amaranth species. A database search can be performed using the gene ID, sequence ID, sequence motif, motif repeat, family name, annotation keyword, scaffold or chromosome numbers, etc. This resource also includes some useful tools, including JBrowse for the visualization of genes, SSRs, SNPs, and TFs on the respective amaranth genomes and BLAST search to perform a BLAST search of the user's query sequence against the amaranth genome as well as protein sequences. The AGRDB database will serve as a potential platform for genetic improvement and characterization of this futuristic crop. The AGRDB database will be accessible via the link: http://www.nbpgr.ernet.in:8080/AmaranthGRD/.

AI-Based Knowledge Extraction from the Bioprinting Literature for Identifying Technology Trends.

Bioprinting is a rapidly evolving field, as represented by the exponential growth of articles and reviews published each year on the topic. As the number of publications increases, there is a need for an automatic tool that can help researchers do more comprehensive literature analysis, standardize the nomenclature, and so accelerate the development of novel manufacturing techniques and materials for the field. In this context, we propose an automatic keyword annotation model, based on Natural Language Processing (NLP) techniques, that can be used to find insights in the bioprinting scientific literature. The approach is based on two main data sources, the abstracts and related author keywords, which are used to train a composite model based on (i) an embeddings part (using the FastText algorithm), which generates word vectors for an input keyword, and (ii) a classifier part (using the Support Vector Machine algorithm), to label the keyword based on its word vector into a manufacturing technique, employed material, or application of the bioprinted product. The composite model was trained and optimized based on a two-stage optimization procedure to yield the best classification performance. The annotated author keywords were then reprojected on the abstract collection to both generate a lexicon of the bioprinting field and extract relevant information, like technology trends and the relationship between manufacturing-material-application. The proposed approach can serve as a basis for more complex NLP-related analysis toward the automated analysis of the bioprinting literature.

The Psychological Effects of Park Therapy Components on Campus Landscape Preferences

COVID-19 has doubled the prevalence of mental health problems among young adults. In Indonesia, it extends the vulnerability of families, increases economic uncertainty, interrupts food security, and affects psychological well-being. Accordingly, this research examined the correlation between psychological effects and preferred landscape elements. Experiments were conducted in a campus park, arboretum, and a road. Participants captured attractive views during the walk using the Visitor-Employed Photography method and evaluated psychological effects using the Profile of Mood States (POMS) and the State-Trait Anxiety Inventory (STAI) pre-and post-walking. Google Cloud Vision API was used to get the image annotation keywords. The results indicated a correlation between psychological effects and preferred landscape elements. Park therapy components, including plant, flower, and sky, were negatively correlated with negative moods and anxiety levels. These findings presented scientific evidence for the psychological relaxation outcome of walking and prominent components of park therapy to support therapeutic campus greenspace planning.

Phage display of environmental protein toxins and virulence factors reveals the prevalence, persistence, and genetics of antibody responses

Microbial exposures are crucial environmental factors that impact healthspan by sculpting the immune system and microbiota. Antibody profiling via Phage ImmunoPrecipitation Sequencing (PhIP-Seq) provides a high-throughput, cost-effective approach for detecting exposure and response to microbial protein products. We designed and constructed a library of 95,601 56-amino acid peptide tiles spanning 14,430 proteins with "toxin" or "virulence factor" keyword annotations. We used PhIP-Seq to profile the antibodies of ∼1,000 individuals against this "ToxScan" library. In addition to enumerating immunodominant antibody epitopes, we studied the age-dependent stability of the ToxScan profile and used a genome-wide association study to find that the MHC-II locus modulates bacterial epitope selection. We detected previously described anti-flagellin antibody responses in a Crohn's disease cohort and identified an association between anti-flagellin antibodies and juvenile dermatomyositis. PhIP-Seq with the ToxScan library is thus an effective tool for studying the environmental determinants of health and disease at cohort scale.

DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021update).

DAVID is a popular bioinformatics resource system including a web server and web service for functional annotation and enrichment analyses of gene lists. It consists of a comprehensive knowledgebase and a set of functional analysis tools. Here, we report all updates made in 2021. The DAVID Gene system was rebuilt to gain coverage of more organisms, which increased the taxonomy coverage from 17 399 to 55 464. All existing annotation types have been updated, if available, based on the new DAVID Gene system. Compared with the last version, the number of gene-term records for most annotation types within the updated Knowledgebase have significantly increased. Moreover, we have incorporated new annotations in the Knowledgebase including small molecule-gene interactions from PubChem, drug-gene interactions from DrugBank, tissue expression information from the Human Protein Atlas, disease information from DisGeNET, and pathways from WikiPathways and PathBank. Eight of ten subgroups split from Uniprot Keyword annotation were assigned to specific types. Finally, we added a species parameter for uploading a list of gene symbols to minimize the ambiguity between species, which increases the efficiency of the list upload and eliminates confusion for users. These current updates have significantly expanded the Knowledgebase and enhanced the discovery power of DAVID.

Postcolonial Assembly Protocols for Unnamed Automation Projects [Special Section

This creative contribution offers a sardonic manual to navigate the exuberant world of the startup economy in Bangalore, India, once known as a backend site of technological labor. As Bangalore fashions itself to become a “new” StartUp City, this manual continually tacks between the material sites and discourses of these seemingly new practices to bring into focus the persistent and revised forms of inequality that undergird them. Adopting the form of annotated keywords allows the manual to recast the circulating buzzwords of startup capitalism within their larger social and cultural histories. Each index entry expands a site of ethnographic investigation and cultural critique to allow the reader to explore the manual across registers: as a sardonic text, as a movement between words and their histories, and as an introduction to academic work that contextualizes technological experimentation.

OntoAnnClass: ontology-based image annotation driven by classification using HMAX features

Several approaches have been proposed in the area of Automatic Image Annotation (AIA) in order to exploit the relationships between words that are extracted from image categories, and to automatically generate annotation words for a given image. Other methods exploit ontologies, where the annotation keywords were derived from ontology to improve image annotation. In this paper, we propose an ontology-based image annotation driven by classification using HMAX features. The idea is (1) to train visual-feature-classifiers and to build an ontology that can finely represent the semantic information associated with training images, and (2) to combine classifier outputs and ontology for image annotation. To annotate images, we define a membership value of words in images. In particular, we propose to evaluate the membership value based on the confidence value of classifiers and the semantic similarity between words. The membership value depends on the word relationships found in the ontology that serve to select annotation words. The obtained experimental results show that the exploitation of both classifier outputs and ontology by evaluating our proposed membership value enables an improvement of image annotation.

Bi-Modal Progressive Mask Attention for Fine-Grained Recognition

Traditional fine-grained image recognition is required to distinguish different subordinate categories ( e.g. , birds species) based on the visual cues beneath raw images. Due to both small inter-class variations and large intra-class variations, it is desirable to capture the subtle differences between these sub-categories, which is crucial but challenging for fine-grained recognition. Recently, language modality aggregation has been proved as a successful technique to improve visual recognition in the experience. In this paper, we introduce an end-to-end trainable Progressive Mask Attention (PMA) model for fine-grained recognition by leveraging both visual and language modalities. Our Bi-Modal PMA model can not only stage-by-stage capture the most discriminative part in the visual modality by our mask-based fashion, but also explore the out-of-visual-domain knowledge from the language modality in an interactional alignment paradigm. Specifically, at each stage, a self-attention module is proposed to attend to the key patch from images or text descriptions. Besides, a query-relational module is designed to seize the key words/phrases of texts and further bridge the connection between two modalities. Later, the learned representations of bi-modality from multiple stages are aggregated as the final features for recognition. Our Bi-Modal PMA model only needs raw images and raw text descriptions, without requiring bounding boxes/part annotations in images or key word annotations in texts. By conducting comprehensive experiments on fine-grained benchmark datasets, we demonstrate that the proposed method achieves superior performance over the competing baselines, on either vision and language bi-modality or single visual modality.

A two-stage hybrid probabilistic topic model for refining image annotation

Refining image annotation has become one of the core research topics in computer vision and pattern recognition due to its great potentials in image retrieval. However, it is still in its infancy and is not sophisticated enough to extract perfect semantic concepts just according to the image low-level features. In this paper, we propose a two-stage hybrid probabilistic topic model to improve the quality of automatic image annotation. To start with, a probabilistic latent semantic analysis model with asymmetric modalities is learned to estimate the posterior probabilities of each annotation keyword, during which the image-to-word relation can be well established. Next, a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels. By this way, the information from image low-level visual features and high-level semantic concepts can be seamlessly integrated by fully taking into account the word-to-word and image-to-image relations. Finally, the rank-two relaxation heuristics is exploited to further mine the correlation of the candidate annotations so as to capture the refining results, which plays a critical role in semantic based image retrieval. Extensive experiments show that the proposed model achieves not only superior annotation accuracy but also better retrieval performance.

An Effective Automatic Image Annotation Model Via Attention Model and Data Equilibrium

Nowadays, a huge number of images are available. However, retrieving a required image for an ordinary user is a challenging task in computer vision systems. During the past two decades, many types of research have been introduced to improve the performance of the automatic annotation of images, which are traditionally focused on content-based image retrieval. Although, recent research demonstrates that there is a semantic gap between content-based image retrieval and image semantics understandable by humans. As a result, existing research in this area has caused to bridge the semantic gap between low-level image features and high-level semantics. The conventional method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, we propose a novel AIA model based on the deep learning feature extraction method. The proposed model has three phases, including a feature extractor, a tag generator, and an image annotator. First, the proposed model extracts automatically the high and low-level features based on dual-tree continues wavelet transform (DT-CWT), singular value decomposition, distribution of color ton, and the deep neural network. Moreover, the tag generator balances the dictionary of the annotated keywords by a new log-entropy auto-encoder (LEAE) and then describes these keywords by word embedding. Finally, the annotator works based on the long-short-term memory (LSTM) network in order to obtain the importance degree of specific features of the image. The experiments conducted on two benchmark datasets confirm that the superiority of the proposed model compared to the previous models in terms of performance criteria.

Аннотация и список ключевых слов к научной статье. Методические рекомендации по выполнению самостоятельной работы для слабослышащих и глухих студентов (на примере философии)

Аннотация и список ключевых слов к научной статье. Методические рекомендации по выполнению самостоятельной работы для слабослышащих и глухих студентов (на примере философии)

Automatic image annotation based on Gaussian mixture model considering cross-modal correlations

Automatic image annotation has been an active topic of research in the field of computer vision and pattern recognition for decades. In this paper, we present a new method for automatic image annotation based on Gaussian mixture model (GMM) considering cross-modal correlations. To be specific, we first employ GMM fitted by the rival penalized expectation-maximization (RPEM) algorithm to estimate the posterior probabilities of each annotation keyword. Next, a label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity by seamlessly integrating the information from both image low level visual features and high level semantic concepts together, which can effectively avoid the phenomenon that different images with the same candidate annotations would obtain the same refinement results. Followed by the rank-two relaxation heuristics over the built label similarity graph is applied to further mine the correlation of the candidate annotations so as to capture the refining annotation results, which plays a crucial role in the semantic based image retrieval. The main contributions of this work can be summarized as follows: (1) Exploiting GMM that is trained by the RPEM algorithm to capture the initial semantic annotations of images. (2) The label similarity graph is constructed by a weighted linear combination of label similarity and visual similarity of images associated with the corresponding labels. (3) Refining the candidate set of annotations generated by the GMM through solving the max-bisection based on the rank-two relaxation algorithm over the weighted label graph. Compared to the current competitive model SGMM-RW, we can achieve significant improvements of 4% and 5% in precision, 6% and 9% in recall on the Corel5k and Mirflickr25k, respectively.

An Ontological Approach towards Dialoguebased Information Visualization System: Quran Corpus for Juz' Amma

This paper presents a corpus that offers rich and authentic knowledge for Juz' Amma using the ontological engineering approach. The reason of the study conducted is because of lack of understanding of Juz' Amma in the form of listing result. At the same time, the listing result does not provide pictorial tidings for clustering the listing result. Therefore, the corpus covers knowledge from various authentic sources, including translation, annotation of keywords, tafsir, and related hadiths for every verse in English and Malay. The ontology is engineered and designed to effectively utilize the authentic knowledge extracted from the corpus intended to serve a dialogue-based information visualization system for Quranic text. A standard ontology engineering methodology was applied for developing the ontology. After creation, the ontology was evaluated and was found to fulfill the goals for which it was developed. The corpus and ontology are hoped to aid various semantic applications related to Quranic text in the future.

Image recommendation based on keyword relevance using absorbing Markov chain and image features

Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user’s requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user’s input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images.

Annotated Corpus for Citation Context Analysis

In this paper, we present a corpus composed of 85 scientific articles annotated with 2092 citations analyzed using context analysis. We obtained a high Inter-annotator agreement; therefore, we assure reliability and reproducibility of the annotation performed by three coders in an independent way. We applied this corpus to classify citations according to qualitative criteria using a medium granularity categorization scheme enriched by annotated keywords and labels to obtain high granularity. The annotation schema handle three dimensions: PURPOSE: POLARITY: ASPECTS. Citation purpose define functions classification: use, critique, comparison and background with more specific classes stablished using keywords: Based on, Supply; Useful; Contrast; Acknowledge, Corroboration, Debate; Weakness and Hedges. Citation aspects complement the citation characterization: concept, method, data, tool, task, among others. Polarity has three levels: Positive, Negative and Neutral. We developed the schema and annotated the corpus focusing in applications for citation influence assessment, but we suggest that applications as summary generation and information retrieval also could use this annotated corpus because of the organization of the scheme in clearly defined general dimensions.

A Method of Keywords Annotation Based on Linked Triples

A Method of Keywords Annotation Based on Linked Triples

Robustness and reliability evaluations of image annotation

The semantic gap problem in image retrieval has motivated much work focusing on automatic image annotation, aimed at facilitating computers to automatically assign keywords to images. The basic measure for evaluating the annotation performance is usually to examine the annotation accuracy. To do this, the fraction of the relevant images, which have been correctly classified by a specific classifier or image annotation system, is measured. Consequently, the evaluation result can be thought of as a surrogate for the judgment of real users. However, the ability of this kind of quantitative evaluation measure to fully evaluate the performance and value of image annotation systems is limited. This paper introduces two complementary metrics related to the rates of annotation accuracy, which can help to further assess the robustness and stability of image annotation systems. They are: (i) the number of annotated keywords with zero-rate accuracy and (ii) the coefficient of variation of annotation accuracy. The evaluation results based on three datasets show that these two metrics are very useful to make a more reliable conclusion for image annotation systems.

MetaPathways v2.5: quantitative functional, taxonomic and usability improvements.

Summary: Next-generation sequencing is producing vast amounts of sequence information from natural and engineered ecosystems. Although this data deluge has an enormous potential to transform our lives, knowledge creation and translation need software applications that scale with increasing data processing and analysis requirements. Here, we present improvements to MetaPathways, an annotation and analysis pipeline for environmental sequence information that expedites this transformation. We specifically address pathway prediction hazards through integration of a weighted taxonomic distance and enable quantitative comparison of assembled annotations through a normalized read-mapping measure. Additionally, we improve LAST homology searches through BLAST-equivalent E-values and output formats that are natively compatible with prevailing software applications. Finally, an updated graphical user interface allows for keyword annotation query and projection onto user-defined functional gene hierarchies, including the Carbohydrate-Active Enzyme database.Availability and implementation: MetaPathways v2.5 is available on GitHub: http://github.com/hallamlab/metapathways2.Contact: shallam@mail.ubc.caSupplementary information: Supplementary data are available at Bioinformatics online.