Tagging Tools Research Articles

Expanding the genetic toolbox for Neisseria meningitidis with efficient tools for unmarked gene editing, complementation, and labeling.

The efficient natural transformation of Neisseria meningitidis allows the rapid construction of bacterial mutants in which the genes of interest are interrupted or replaced by antibiotic-resistance cassettes. However, this proved to be a double-edged sword, i.e., although facilitating the genetic characterization of this important human pathogen, it has limited the development of strategies for constructing markerless mutants without antibiotic-resistance markers. In addition, efficient tools for complementation or labeling are also lacking in N. meningitidis. In this study, we significantly expand the meningococcal genetic toolbox by developing new and efficient tools for the construction of markerless mutants (using a dual counterselection strategy), genetic complementation (using integrative vectors), and cell labeling (using a self-labeling protein tag). This expanded toolbox paves the way for more in-depth genetic characterization of N. meningitidis and might also be useful in other Neisseria species.IMPORTANCENeisseria meningitidis and Neisseria gonorrhoeae are two important human pathogens. Research focusing on these bacteria requires genetic engineering, which is facilitated by their natural ability to undergo transformation. However, the ease of mutant engineering has led the Neisseria community to neglect the development of more sophisticated tools for gene editing, particularly for N. meningitidis. In this study, we have significantly expanded the meningococcal genetic toolbox by developing novel and efficient tools for markerless mutant construction, genetic complementation, and cell tagging. This expanded toolbox paves the way for more in-depth genetic characterization of N. meningitidis and might also be useful in other Neisseria species.

LarvaTagger: manual and automatic tagging of Drosophila larval behaviour.

As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data. We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth. We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data. All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.

Evaluation of individual visual implant elastomer tag marking of Japanese eel (Anguilla japonica) larvae

Visual implant elastomer (VIE) marking was evaluated as a tool for individual tagging of larval Japanese eels, Anguilla japonica, during rearing conditions. Combinations of eight colors of VIE tag were used to individually mark leptocephalus larvae (104–110 days post hatched) by injecting them with either a mixture of a silicone color material and a curing agent, or only color material. Injections were at three locations on the dorso-lateral body surface for individual identification, and a sham-operated control group was also prepared. Tag retention was 100% with or without curing agent, and individuals could be tracked until larvae completely metamorphosed into glass eels. After some initial mortality, the survival and growth rates were not different between each treatment group. These results suggested that VIE tagging could provide good individual larva identification method for research during rearing conditions.

Comparison of Sentences using POS Tagging Tool under Subjective Examination

The Question answering system is used to generate the correct result that is asked by humans in natural language. In an online examination system, most of the work has been done but still, problems occur in preprocessing i.e. Part Of Speech (POS). POS tagger is used to properly tag each word in the sentences. In this paper, we used two datasets i.e. TREC DATA and data collected from the student. We apply the POS tagger to both datasets and compare the result. For generating the POS tagger we used NLTK and spaCy libraries for comparison. We observed that using those libraries the same word has a different tag. Using both tools, we computed the difference between the words and assigned the count to the POS tagging on that result, we calculate the accuracy of both libraries. The result shows that the spaCy library is best for POS tagging because it generates more correct results as compared to NLTK.

Metaphorical translation strategies of the white paper "China's Green Development in the New Era"

In recent years, the use of metaphor in white papers has proliferated as it plays a significant role in cognitive processing. In order to shed light on the intricate relationship between humans and the natural environment, this study focuses on the analysis of metaphor usage in a particular white paper titled "China's Green Development in the New Era" within the field of ecopolitics. A combination of qualitative and quantitative methods, namely the utilization of a self-built corpus and case studies, is employed to categorize the translation strategies of metaphors. The findings of this study demonstrate that the metaphors utilized in the white paper effectively capture elements of social cognition and ideology. Moreover, with the assistance of part-of-speech tagging tools, it is observed that most translated metaphors strive to maintain the salient features of the source text, aiming to achieve equivalence in both form and meaning.

The affordances of a mobile video-tagging tool for evaluating presentation skills in a second language

ABSTRACT This study investigated the affordances of a mobile video observation and tagging tool used to evaluate presentation skills in English language classrooms. The data consists of 35 video-recorded presentations in a higher education setting. Using a digital evaluation grid, the students received feedback based on visual analytics generated by the mobile app. The students then were asked to reflect on their performances, and were also asked to comment on the affordances and limitations of the method and the tool. Qualitative data that came from (1) students’ written self-evaluations and reflections and (2) their reported perceptions of the affordances of the tool were analyzed using the Constant Comparison Method. Analyses of reflective writings indicated the dominance of negative self-evaluations of language use, while affective factors were also a strong theme. The video-tagging tool was found to be very beneficial by the learners, mainly in enabling them to notice their strengths and weaknesses in presenting in English and facilitating effective feedback. The results show that this data-led reflective presentation model can be beneficial for learners as they can identify points of development.

Advances in the Automatic Lemmatization of Old English: Class IV Strong Verbs (L-Y)

The morphological features of an inflectional language like Old English (OE), which also presents generalized spelling inconsistencies, limit the use of lemmatizing and tagging tools that can be applied to natural languages. Consequently, the development of Natural Language Processing (NLP) models, which crucially depend on lemmatized corpora, is slowed down. Against this background, this article develops a lemmatizer within the framework of Morphological Generation that allows for the type-based automatic lemmatization of OE class IV strong verbs (L–Y). The lemmatizer incorporates a set of algorithms to account for features of inflectional, derivational, morphophonological and diatopic variation. The generated forms are automatically compared with Taylor et al. (2003) and Healey et al. (2004) to confirm their attestation and are assigned a lemma. Overall, the research proves successful in setting up form-lemma associations, while highlighting areas of ambiguity and mismatches. The main conclusion of the article is that taking the route of automatic lemmatization with this methodological framework will contribute to the field of OE lexicography by both lemmatizing attested inflectional forms and by identifying areas for manual revision.

Image networks and practice analysis of larger data corpora. An approach to cluster and recontextualize visual practice in social media

This paper reports a methodological exploration combining image network analysis and standardized practice analysis on social media data. Through applying the open source software Memespector to access the Clarifai API, the potential of an easy-at-hand image tagging tool as an instrument to manage larger data corpora is explored. Using the example of the German-speaking Twitter hashtag #systemrelevant, we relate image clusters to the results of standardized practice analysis of posts that contain images. The proposed method is intended for research that attempts to carve out the co-constituting of public discourse in social media by different groups of actors. The approach systematically differentiates the contributions of societal groups such as journalism, civil society, or private individuals, and the embedding of their tweets in selected anchoring practices and further modalities of participation. Altogether, the multistep analytical process offers a possible approach to process larger image corpora, while maintaining a sensitivity for the practice-theoretical demand of (re)contextualizing image use.

KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines

Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculating reliability key performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average F1-scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments, the AI-assisted tool leads to an 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators, and therefore support the optimization of wind turbine operation and maintenance.

The use of Video Enhanced Observation in video-mediated post-observation conversations on pre-service EFL teachers’ online practicum teaching

Post-observation conversations are common practices in EFL teacher education contexts, which provide pre-service teachers with professional development opportunities through reflections on their practicum teaching. When performed in a data-led and evidence-based way, these conversations can constitute a truly reflective environment in and through which pre-service teachers can see their own interactional management of language learning processes. This study presents a case that comes from the online practicum teaching of EFL teachers in video-mediated classrooms. We deal with how a teacher trainer, during the video-mediated post-observation meeting, utilizes a digital observation and video tagging tool (i.e., Video Enhanced Observation) on the shared screen and topicalizes the pedagogical and technological aspects of the pre-service teacher's online practicum teaching with reference to the pre-practicum meetings, mentor teacher's role during the process, and the overall pedagogical outcomes of the process in situ. Using multimodal Conversation Analysis as the research methodology, we examine the reflective conversations between a teacher trainer and a pre-service teacher based on the screen-recorded video-mediated practicum teaching in online upper secondary EFL classrooms. The findings bring new insights into EFL learning, teaching, and teacher education in video-mediated fully online settings and describe the affordances and constraints of the digital tool.

Semantic Tagging for the Urdu Language: Annotated Corpus and Multi-Target Classification Methods

Extracting and analysing meaning-related information from natural language data has attracted the attention of researchers in various fields, such as natural language processing, corpus linguistics, information retrieval, and data science. An important aspect of such automatic information extraction and analysis is the annotation of language data using semantic tagging tools. Different semantic tagging tools have been designed to carry out various levels of semantic analysis, for instance, named entity recognition and disambiguation, sentiment analysis, word sense disambiguation, content analysis, and semantic role labelling. Common to all of these tasks, in the supervised setting, is the requirement for a manually semantically annotated corpus, which acts as a knowledge base from which to train and test potential word and phrase-level sense annotations. Many benchmark corpora have been developed for various semantic tagging tasks, but most are for English and other European languages. There is a dearth of semantically annotated corpora for the Urdu language, which is widely spoken and used around the world. To fill this gap, this study presents a large benchmark corpus and methods for the semantic tagging task for the Urdu language. The proposed corpus contains 8,000 tokens in the following domains or genres: news, social media, Wikipedia, and historical text (each domain having 2K tokens). The corpus has been manually annotated with 21 major semantic fields and 232 sub-fields with the USAS (UCREL Semantic Analysis System) semantic taxonomy which provides a comprehensive set of semantic fields for coarse-grained annotation. Each word in our proposed corpus has been annotated with at least one and up to nine semantic field tags to provide a detailed semantic analysis of the language data, which allowed us to treat the problem of semantic tagging as a supervised multi-target classification task. To demonstrate how our proposed corpus can be used for the development and evaluation of Urdu semantic tagging methods, we extracted local, topical and semantic features from the proposed corpus and applied seven different supervised multi-target classifiers to them. Results show an accuracy of 94% on our proposed corpus which is free and publicly available to download.

Bioorthogonal Probes for Analyzing Non-Enzymatic Post-Translational Modifications.

Non-enzymatic post-translational modifications (nPTMs) have been proposed as indicators of cellular stresses and diseases. Unfortunately, direct assessment of nPTMs in native environment is extremely challenging due to the heterogeneity of adducts and the lack of tagging tools. Given these challenges, bioorthogonal probes (BPs) have been developed for the analysis of nPTMs. The rationality is that BPs could selectively install azides or alkynes into nPTMs as a biorthogonal handle for the following enrichment or tracking. Herein, we review the state-of-art of BPs used for nPTMs studies, clarify their working principles, and highlight how they advance our understanding of the biological functions of nPTMs.

Molecular and Physiological Variability in Bread Wheat and Its Wild Relative (Aegilops tauschii Coss.) Species under Water-Deficit Stress Conditions.

Aegilops and Triticum spp. are two ideal gene pools for the breeding purposes of wheat. In this study, a set of Iranian accessions of Aegilops tauschii Coss. and Triticum aestivum L. species were evaluated in terms of some physiological and biochemical features under control and water-deficit stress conditions. Moreover, several simple sequence repeat (SSR) markers were employed to identify marker loci associated with the measured traits. The results indicated that water-deficit stress significantly affected all measured traits and the highest reductions due to water-deficit were recorded for shoot fresh and dry biomasses (SFB and SDB), stomatal conductance (Gs), leaf relative water content (RWC), and chlorophyll b content (Chl b). In molecular analysis, 25 SSR markers generated 50 fragments, out of which 49 fragments (98%) were polymorphic. Furthermore, the genetic variation observed within species is more than between species. The results of cluster and Bayesian model analysis classified all evaluated accessions into three main clusters. Under control and water-deficit stress conditions, 28 and 27 significant marker-trait associations (MTAs) were identified, respectively. Furthermore, 10 MTAs showed sufficiently stable expression across both growth conditions. Of these, the markers Xgwm-111, Xgwm-44, Xgwm-455, Xgwm-272, and Xgwm-292 were associated with multiple traits. Hence, these markers could serve as useful molecular tools for population characterization, gene tagging, and other molecular breeding studies.

Use and Evaluation of a pES213-Derived Plasmid for the Constitutive Expression of gfp Protein in Pathogenic Vibrios: a Tagging Tool for In Vitro Studies.

Insertion of green fluorescent protein (GFP) into bacterial cells for constitutive expression is a powerful tool for the localization of species of interest within complex mixtures. Here, we demonstrate and evaluate the efficacy of the pES213-derived donor plasmid pVSV102 (gfp Knr) as a conjugative tool for the tagging of Vibrio and related species (termed vibrios). Using a triparental mating assay assisted by the helper plasmid pEVS104 (tra trb Knr), we successfully tagged 12 species within the Vibrionaceae family representing 8 of the proposed clades. All transconjugant strains demonstrated bright fluorescence and were readily differentiable within complex mixtures of nontagged cells. Plasmid retention was assessed using persistence and subculture experimentation. Persistence experiments evaluated plasmid loss over time for nonsubcultured samples inoculated into antibiotic-free media and sterile artificial seawater, whereas subculture trials evaluated plasmid loss following one to four subculture passages. Strong plasmid retention (≥80%) was observed in persistence experiments for all transconjugant strains for up to 48 h in both antibiotic-free media and artificial seawater with the exception of Vibrio cholerae, which showed a substantial decline in media after 24 h. Subculturing experiments also demonstrated strong plasmid stability, with all transconjugant strains showing ≥80% retention after four subculture passages. The results of this research suggest that pVSV102 is a stable GFP plasmid for the tagging of a broad range of vibrios. IMPORTANCE Prior research has suggested that the use of Aliivibrio fischeri-derived donor plasmids with the pES213 origin of replication may provide increased plasmid stability for the tagging of vibrios compared to Escherichia coli-derived p15A plasmids. Here, we present a structured protocol for conjugation-based tagging of vibrios using the pES213-derived plasmid pVSV102 and evaluate the plasmid stability of tagged strains. These methods and the resulting transconjugant strains provide important standardized tools to facilitate experimentation requiring the use of traceable vibrio strains. Furthermore, the determination of the species-specific plasmid stability provides an estimation of the anticipated level of plasmid loss under the given set of culture conditions. This estimation can be used to reduce the occurrence of experimental biases introduced by plasmid drift.

Tools for supporting teacher noticing about classroom video in online professional development

Teachers’ noticing of students’ mathematical thinking plays an important role in supporting student learning. Yet little is known about how online professional development (PD)—a growing setting for PD in the USA—can cultivate this noticing. Here, we explore the potential of using two online tools for engaging video to support K-2 teachers’ noticing of students’ mathematical thinking in the context of a six-week online video-based PD program. While participating in the program, teachers used a commenting tool that allowed them to view video in its entirety and write a summary note, as well as a tagging tool that allowed them to mark moments of video while viewing and associate notes with those moments. We found that teachers’ regular use of the tagging tool promoted their increased noticing of students’ mathematical thinking in video. Further, the tagging tool and commenting tool appeared to function in complementary ways to support teachers’ noticing. These findings contribute to the field’s growing understanding of how technological advances can support the development and study of mathematics teacher noticing, with implications for the design of online teacher learning environments.

Biomedical Text NER Tagging Tool with Web Interface for Generating BERT-Based Fine-Tuning Dataset

In this paper, a tagging tool is developed to streamline the process of locating tags for each term and manually selecting the target term. It directly extracts the terms to be tagged from sentences and displays it to the user. It also increases tagging efficiency by allowing users to reflect candidate categories in untagged terms. It is based on annotations automatically generated using machine learning. Subsequently, this architecture is fine-tuned using Bidirectional Encoder Representations from Transformers (BERT) to enable the tagging of terms that cannot be captured using Named-Entity Recognition (NER). The tagged text data extracted using the proposed tagging tool can be used as an additional training dataset. The tagging tool, which receives and saves new NE annotation input online, is added to the NER and RE web interfaces using BERT. Annotation information downloaded by the user includes the category (e.g., diseases, genes/proteins) and the list of words associated to the named entity selected by the user. The results reveal that the RE and NER results are improved using the proposed web service by collecting more NE annotation data and fine-tuning the model using generated datasets. Our application programming interfaces and demonstrations are available to the public at via the website link provided in this paper.

How to measure disagreement as a premise for learning from controversy in a social media context

Learning scenarios building on disagreement in a learning group or a whole classroom are well established in modern pedagogy. In the specific tradition of collaborative learning, such approaches have been traced back to theories of socio-cognitive conflict and have been associated with argumentative learning interactions. An important premise for these types of learning scenarios is the identification of disagreement. In the spirit of learning analytics, this calls for analytic tools and mechanisms to detect and measure disagreement in learning groups.Our mathematical analysis of several methods shows that methods of different origin are largely equivalent, only differing in the normalization factors and ensuing scaling properties. We have selected a measure that scales best and applied it to a target scenario in which learners judged types and levels of “toxicity” of social media content using an interactive tagging tool. Due restrictions imposed by the pandemic, we had to replace the originally envisaged classroom scenario by online experiments. We report on two consecutive experiments involving 42 students in the first and 89 subjects in the second instance. The results corroborate the adequacy of the measure in combination with the interactive, game-based approach to collecting judgements. We also saw that a revision of categories after the first study reduced the ambiguity. In addition to applying the disagreement measure to the learner judgements, we also assessed several personality traits, such as authoritarianism and social closeness. Regarding the dependency of the learner judgements on personality traits, we could only observe a weak influence of authoritarianism.

Cell sorting microbeads as novel contrast agent for magnetic resonance imaging

The success of several cell-based therapies and prevalent use of magnetic resonance imaging (MRI) in the clinic has fueled the development of contrast agents for specific cell tracking applications. Safe and efficient labeling of non-phagocytic cell types such as T cells nonetheless remains challenging. We developed a one-stop shop approach where the T cell sorting agent also labels the cells which can subsequently be depicted using non-invasive MRI. We compared the MR signal effects of magnetic-assisted cell sorting microbeads (CD25) to the current preclinical gold standard, ferumoxytol. We investigated in vitro labeling efficiency of regulatory T cells (Tregs) with MRI and histopathologic confirmation. Thereafter, Tregs and T cells were labeled with CD25 microbeads in vitro and delivered via intravenous injection. Liver MRIs pre- and 24 h post-injection were performed to determine in vivo tracking feasibility. We show that CD25 microbeads exhibit T2 signal decay properties similar to other iron oxide contrast agents. CD25 microbeads are readily internalized by Tregs and can be detected by non-invasive MRI with dose dependent T2 signal suppression. Systemically injected labeled Tregs can be detected in the liver 24 h post-injection, contrary to T cell control. Our CD25 microbead-based labeling method is an effective tool for Treg tagging, yielding detectable MR signal change in cell phantoms and in vivo. This novel cellular tracking method will be key in tracking the fate of Tregs in inflammatory pathologies and solid organ transplantation.

Artificial optimization of bamboo Ppmar2 transposase and host factors effects on Ppmar2 transposition in yeast.

Mariner-like elements (MLEs) are promising tools for gene cloning, gene expression, and gene tagging. We have characterized two MLE transposons from moso bamboo, Ppmar1 and Ppmar2. Ppmar2, is smaller in size and has higher natural activities, thus making it a more potential genomic tool compared to Ppmar1. Using a two-component system consisting of a transposase expression cassette and a non-autonomous transposon cotransformed in yeast, we investigated the transposition activity of Ppmar2 and created hyperactive transposases. Five out of 19 amino acid mutations in Ppmar2 outperformed the wild-type in terms of catalytic activities, especially with the S347R mutant having 6.7-fold higher transposition activity. Moreover, 36 yeast mutants with single-gene deletion were chosen to screen the effects of the host factors on Ppmar2NA transposition. Compared to the control strain (his3Δ), the mobility of Ppmar2 was greatly increased in 9 mutants and dramatically decreased in 7 mutants. The transposition ability in the efm1Δ mutant was 15-fold higher than in the control, while it was lowered to 1/66 in the rtt10Δ mutant. Transcriptomic analysis exhibited that EFM1 defection led to the significantly impaired DDR2, HSP70 expression and dramatically boosted JEN1 expression, whereas RTT10 defection resulted in significantly suppressed expression of UTP20, RPA190 and RRP5. Protein methylation, chromatin and RNA transcription may affect the Ppmar2NA transposition efficiency in yeast. Overall, the findings provided evidence for transposition regulation and offered an alternative genomic tool for moso bamboo and other plants.

Move–Bundle Connection in Conclusion Sections of Research Articles Across Disciplines

Abstract This study explores how lexical bundles link smaller functional units (steps) in research article (RA) conclusions in hard and soft science knowledge fields by using corpus-based and corpus-driven approaches. RA conclusion corpora from the hard and soft sciences were compiled and four-word lexical bundles were extracted from each corpus. Conclusions containing lexical bundles were then imported into the UAMCorpus tool for manual tagging of functions, structures, moves, and steps associated with lexical bundles. It has been found that the four-word bundles play an important role in realizing the communicative purpose of the functional units in RA conclusions in both the hard and soft sciences. The results demonstrate that the resultative lexical bundle (e.g. the findings of the) is a strong indicator of the conclusions in both soft and hard sciences. Specific bundle structures and functions are identified as signalling different steps of the conclusions. The findings of this study add to the growing body of knowledge regarding the bundle–move connections and contribute to the construction of the RA conclusions in different disciplinary cultures.