Types Of Annotations Research Articles

Multimodal Sentiment Analysis for the Malay Language: New Corpus using CNN-based Framework

Sentiment analysis in the Malay language has traditionally focused on text-based data. Malay is the native language of Malaysia and other surrounding countries. While text-based sentiment analysis has shown good performance, it often lacks accuracy due to the absence of the speaker's affective state and intentions, which can lead to misinterpretations. Multimodal sentiment analysis addresses these shortcomings and has demonstrated improved performance in various prediction tasks. Unfortunately, there has been little research in this area for the Malay language, due to a lack of corpus and baseline studies. This paper introduces a new Malay Multimodal Sentiment Corpus, ‘MyMSC’, with annotations at both the multimodal and unimodal text levels. It contains 1208 segments covering political and social topics. The corpus development processes are described in detail, along with the necessary guidelines and considerations. This paper proposes a CNN-based framework with a late fusion method as the baseline model. Experiments with the proposed model demonstrate that the multimodal approach (F1 score = 0.77) outperforms the unimodal approach (F1 score = 0.68), validating the contribution of multimodality to classification performance. The differences between the two types of annotations and their impact are further elaborated. The full corpus is available at https://github.com/serenaroseaini/MyMSC .

Toward high-quality pseudo masks from noisy or weak annotations for robust medical image segmentation

Deep learning networks excel in image segmentation with abundant accurately annotated training samples. However, in medical applications, acquiring large quantities of high-quality labeled images is prohibitively expensive. Thus, learning from imperfect annotations (e.g. noisy or weak annotations) has emerged as a prominent research area in medical image segmentation. This work aims to extract high-quality pseudo masks from imperfect annotations with the assistance of a small number of clean labels. Our core motivation is based on the understanding that different types of flawed imperfect annotations inherently exhibit unique noise patterns. Comparing clean annotations with corresponding imperfectly annotated labels can effectively identify potential noise patterns at minimal additional cost. To this end, we propose a two-phase framework including a noise identification network and a noise-robust segmentation network. The former network implicitly learns noise patterns and revises labels accordingly. It includes a three-branch network to identify different types of noises. The latter one further mitigates the negative influence of residual annotation noises based on parallel segmentation networks with different initializations and a label softening strategy. Extensive experimental results on two public datasets demonstrate that our method can effectively refine annotation flaws and achieve superior segmentation performance to the state-of-the-art methods.

Enhancement of early proximal caries annotations in radiographs: introducing the Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset

BackgroundProximal caries datasets for training artificial intelligence (AI) algorithms commonly include clinician-annotated radiographs. These conventional annotations are susceptible to observer variability, and early caries may be missed. Micro-computed tomography (CT), while not feasible in clinical applications, offers a more accurate imaging modality to support the creation of a reference-standard dataset for caries annotations. Herein, we present the Academic Center for Dentistry Amsterdam—Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset, which is the first dataset pairing dental radiographs and micro-CT scans to enable higher-quality annotations.MethodsThe ACTA-DIRECT dataset encompasses 179 paired micro-CT scans and radiographs of early proximal carious teeth, along with three types of annotations: conventional annotations on radiographs, micro-CT-assisted annotations on radiographs, and micro-CT annotations (reference standard). Three dentists independently annotated proximal caries on radiographs, both with and without micro-CT assistance, enabling determinations of interobserver agreement and diagnostic accuracy. To establish a reference standard, one dental radiologist annotated all caries on the related micro-CT scans.ResultsMicro-CT support improved interobserver agreement (Cohen’s Kappa), averaging 0.64 (95% confidence interval [CI]: 0.59–0.68) versus 0.46 (95% CI: 0.44–0.48) in its absence. Likewise, average sensitivity and specificity increased from 42% (95% CI: 34–51%) to 63% (95% CI: 54–71%) and from 92% (95% CI: 88–95%) to 95% (95% CI: 92–97%), respectively.ConclusionThe ACTA-DIRECT dataset offers high-quality images and annotations to support AI-based early caries diagnostics for training and validation. This study underscores the benefits of incorporating micro-CT scans in lesion assessments, providing enhanced precision and reliability.Graphical

Generation and analysis of a mouse multitissue genome annotation atlas.

Generating an accurate and complete genome annotation for an organism is complex because the cells within each tissue can express a unique set of transcript isoforms from a unique set of genes. A comprehensive genome annotation should contain information on what tissues express what transcript isoforms at what level. This tissue-level isoform information can then inform a wide range of research questions as well as experiment designs. Long-read sequencing technology combined with advanced full-length cDNA library preparation methods has now achieved throughput and accuracy where generating these types of annotations is achievable. Here, we show this by generating a genome annotation of the mouse (Mus musculus). We used the nanopore-based R2C2 long-read sequencing method to generate 64 million highly accurate full-length cDNA consensus reads-averaging 5.4 million reads per tissue for a dozen tissues. Using the Mandalorion tool, we processed these reads to generate the Tissue-level Atlas of Mouse Isoforms which is available as a trackhub for the UCSC Genome Browser and contains at least one full-length isoform for the vast majority of expressed genes in each tissue.

An annotated type catalogue of praying mantises (Mantodea) in the Zoological Museum Hamburg (ZMH)

With this publication we provide an updated catalogue of the type material of mantises (Mantodea) deposited in the Zoological Museum Hamburg (ZMH). We report 84 type specimens (51 holotypes, 25 paratypes and 8 syntypes) belonging to 64 species (45 valid names and 19 synonyms). Furthermore, we present high resolution illustrations for these type specimens.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1–6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10–12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome—a map of projections between regions—from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

Whole-brain annotation and multi-connectome cell typing of Drosophila

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

Exploring annotation taxonomy in grouped bar charts: A qualitative classroom study

Annotations are an essential part of data analysis and communication in visualizations, which focus a readers attention on critical visual elements (e.g. an arrow that emphasizes a downward trend in a bar chart). Annotations enhance comprehension, mental organization, memorability, user engagement, and interaction and are crucial for data externalization and exploration, collaborative data analysis, and narrative storytelling in visualizations. However, we have identified a general lack of understanding of how people annotate visualizations to support effective communication. In this study, we evaluate how visualization students annotate grouped bar charts when answering high-level questions about the data. The resulting annotations were qualitatively coded to generate a taxonomy of how they leverage different visual elements to communicate critical information. We found that the annotations used significantly varied by the task they were supporting and that whereas several annotation types supported many tasks, others were usable only in special cases. We also found that some tasks were so challenging that ensembles of annotations were necessary to support the tasks sufficiently. The resulting taxonomy of approaches provides a foundation for understanding the usage of annotations in broader contexts to help visualizations achieve their desired message.

Detecting cell types and densities in the tumor microenvironment improves prognostic risk assessment for breast cancer.

A comprehensive evaluation of the relationship between the densities of various cell types in the breast cancer tumor microenvironment and patient prognosis is currently lacking. Additionally, the absence of a large patch-level whole slide imaging (WSI) dataset of breast cancer with annotated cell types hinders the ability of artificial intelligence to evaluate cell density in breast cancer WSI. We first employed Lasso-Cox regression to build a breast cancer prognosis assessment model based on cell density in a population study. Pathology experts manually annotated a dataset containing over 70,000 patches and used transfer learning based on ResNet152 to develop an artificial intelligence model for identifying different cell types in these patches. The results showed that significant prognostic differences were observed among breast cancer patients stratified by cell density score (P = 0.0018), with the cell density score identified as an independent prognostic factor for breast cancer patients (P < 0.05). In the validation cohort, the predictive performance for overall survival (OS) was satisfactory, with area under the curve (AUC) values of 0.893 (OS) at 1-year, 0.823 (OS) at 3-year, and 0.861 (OS) at 5-year intervals. We trained a robust model based on ResNet152, achieving over 99% classification accuracy for different cell types in patches. These achievements offer new public resources and tools for personalized treatment and prognosis assessment.

Survey of Annotations in Extended Reality Systems.

Annotation in 3D user interfaces such as Augmented Reality (AR) and Virtual Reality (VR) is a challenging and promising area; however, there are not currently surveys reviewing these contributions. In order to provide a survey of annotations for Extended Reality (XR) environments, we conducted a structured literature review of papers that used annotation in their AR/VR systems from the period between 2001 and 2021. Our literature review process consists of several filtering steps which resulted in 103 XR publications with a focus on annotation. We classified these papers based on the display technologies, input devices, annotation types, target object under annotation, collaboration type, modalities, and collaborative technologies. A survey of annotation in XR is an invaluable resource for researchers and newcomers. Finally, we provide a database of the collected information for each reviewed paper. This information includes applications, the display technologies and its annotator, input devices, modalities, annotation types, interaction techniques, collaboration types, and tasks for each paper. This database provides a rapid access to collected data and gives users the ability to search or filter the required information. This survey provides a starting point for anyone interested in researching annotation in XR environments.

Integrating Mendelian Randomization with Single-cell Sequencing Data Reveals the Causal Effect and Related Mechanisms of Smoking on Parkinson's Disease.

Smoking (nicotine) has been reported to possibly be neuroprotective and conducive to patients with early Parkinson's disease (PD). However, the causal effect of smoking on PD and the molecular mechanisms of smoking-related genes (SRGs) are vague. First, genome-wide association study summary data on smoking (ukb-b-6244) and PD (ieu-b-7) were retrieved from the Integrative Epidemiology Unit OpenGWAS database for Mendelian randomization (MR) analysis. Sensitivity analyses were performed to validate the results of the MR analyses. Subsequently, a differential analysis of PD patients and controls was performed to identify differentially expressed SRGs (DE-SRGs). Finally, the expression of DE-SRGs was analyzed in annotated cell types. The MR analysis revealed that smoking was a protective factor causally related to PD (P=0.008, odds ratio=0.288). Furthermore, a total of five DE-SRGs enriched in Toll-like receptor signaling pathways were identified in GSE7621 dataset. Regarding single-cell analysis of GSE184950 dataset, a total of nine cell types were annotated. The expression of LRRN1 in oligodendrocyte progenitor cells and oligodendrocytes, respectively, differed significantly between PD patients and controls. Our study supported a causal relationship between smoking and PD and found that five SRGs (MAPK8IP1, LRRN1, LINC00324, HIST1H2BK, and YOD1) enriched in Toll-like receptor signaling pathways might be beneficial in PD. In addition, single-cell sequencing indicated that four SRGs were differentially expressed in different cell types. All four genes except MAPK8IP1 were significantly correlated with the 10 genes calculated by scPagwas. Thus, this evidence provides a theoretical basis for further research on the effect of nicotine (smoking) on PD. In order to explore the potential etiology and pathogenesis of Parkinson's disease, this study combined Mendelian randomization, transcriptomics and single-cell sequencing analysis to explore the association between exposure factors and Parkinson's disease, observe and confirm the relationship and mechanism between the two from the perspective of genetics, and provide more reliable evidence for causal inference.

Unsupervised 3D Object Segmentation of Point Clouds by Geometry Consistency.

In this paper, we study the problem of 3D object segmentation from raw point clouds. Unlike existing methods which usually require a large amount of human annotations for full supervision, we propose the first unsupervised method, called OGC, to simultaneously identify multiple 3D objects in a single forward pass, without needing any type of human annotations. The key to our approach is to fully leverage the dynamic motion patterns over sequential point clouds as supervision signals to automatically discover rigid objects. Our method consists of three major components, 1) the object segmentation network to directly estimate multi-object masks from a single point cloud frame,2)the auxiliary self-supervised scene flow estimator,and 3)our core object geometry consistency component. By carefully designing a series of loss functions, we effectively take into account the multi-object rigid consistency and the object shape invariance in both temporal and spatial scales. This allows our method to truly discover the object geometry even in the absence of annotations. We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation and general object segmentation in both indoor and the challenging outdoor scenarios. Our code and data are available at https://github.com/vLAR-group/OGC.

Enhancing knowledge graphs with microdata and LLMs: the case of Schema.org and Wikidata in touristic information

PurposeKnowledge graphs (KGs) are structured knowledge bases that represent real-world entities and are used in a variety of applications. Many of them are created and curated from a combination of automated and manual processes. Microdata embedded in Web pages for purposes of facilitating indexing and search engine optimization are a potential source to augment KGs under some assumptions of complementarity and quality that have not been thoroughly explored to date. In that direction, this paper aims to report results on a study that evaluates the potential of using microdata extracted from the Web to augment the large, open and manually curated Wikidata KG for the domain of touristic information. As large corpora of Web text is currently being leveraged via large language models (LLMs), these are used to compare the effectiveness of the microdata enhancement method.Design/methodology/approachThe Schema.org taxonomy was used as the source to determine the annotation types to be collected. Here, the authors focused on tourism-related pages as a case study, selecting the relevant Schema.org concepts as point of departure. The large CommonCrawl resource was used to select those annotations from a large recent sample of the World Wide Web. The extracted annotations were processed and matched with Wikidata to estimate the degree to which microdata produced for SEO might become a valuable resource to complement KGs or vice versa. The Web pages themselves can also serve as a context to produce additional metadata elements using them as context in pipelines of an existing LLMs. That way, both the annotations and the contents itself can be used as sources.FindingsThe samples extracted revealed a concentration of metadata annotations in only a few of the relevant Schema.org attributes and also revealed the possible influence of authoring tools in a significant fraction of microdata produced. The analysis of the overlapping of attributes in the sample with those of Wikidata showed the potential of the technique, limited by the disbalance of the presence of attributes. The combination of those with the use of LLMs to produce additional annotations demonstrates the feasibility of the approach in the population of existing Wikidata locations. However, in both cases, the effectiveness appears to be lower in the cases of less content in the KG, which are arguably the most relevant when considering the scenario of an automated population approach.Originality/valueThe research reports novel empirical findings on the way touristic annotations with a SEO orientation are being produced in the wild and provides an assessment of their potential to complement KGs, or reuse information from those graphs. It also provides insights on the potential of using LLMs for the task.

Abstract PO1-15-01: Digital pathology models reveal case-specific characteristics of the tumor microenvironment

Abstract Background Phenotypic characteristics and genetic content have been observed to vary within individual tumors. However, translational research and some clinical practices often rely on extrapolating single histopathology slides to be representative of whole-tumor biology, even when the extent of intra-tumor biological heterogeneity is unknown. In addition, technical factors in slide preparation and digitization can also contribute to variability. Here, we use digital pathology models to identify cell and tissue types in whole-slide images of breast cancer tumors, and quantify intra- and inter-case heterogeneity in cell and tissue features of the tumor microenvironment. Methods We developed PathExplore, a suite of convolutional neural network models trained on pathologist annotations of cell and tissue types in hematoxylin and eosin-stained slides. The models were deployed on the TCGA breast cancer dataset (n=1083 primary solid tumors). Quantitative human-interpretable features (HIFs) relating to cancer, stromal, and necrotic tissue areas, as well as the abundance and distribution of cancer cells, fibroblasts, and immune cells within these tissues, were extracted for each slide. Similarity between slides on sets of HIFs was assessed using Pearson correlation. Intra- and inter-case variability of individual HIFs was measured using the normalized percent difference (range divided by mean) for each slide pair. Case-specificity was quantified by the AUC of correlation or difference distributions for intra-case vs inter-case slide pairs. Only pairs of slides with matching metadata, including tissue source site, cancer stage, and scanner model, were considered for inter-case analysis. Results A total of 55 cases, comprising 114 slides, were identified as having more than one slide per case. Considering all 123 proportional HIFs, intra-case slide pairs showed high correlation compared to inter-case pairs (r=0.97 vs r=0.90, AUC=0.88), with 29% of slides coming from a multi-slide case most closely correlated with another slide from the same case out of the entire TCGA breast dataset. Subsets of these proportional HIFs relating to each identified cell type were also case-specific, with AUC’s ranging from 0.79 for macrophage HIFs to 0.87 for cancer cell HIFs. All proportion, density, and ratio HIFs (n=297) individually showed case specificity (AUC &amp;gt;0.5), with median intra-case differences ranging from 0.6% to 69%. Area proportion of necrosis was more variable than cancer or stroma tissue, while cancer cell count proportions were less variable than fibroblast or immune cell proportions. Cell count proportions were in general less variable and more specific than their corresponding density HIFs; for instance, the frequency of fibroblasts out of all cells in stroma had a median intra-case difference of 11% compared to 20% for the density of fibroblasts in stroma. Conclusions We quantified heterogeneity of key features of the tumor microenvironment both within and across tumors. These results reveal biological and technical variability that can inform selection and interpretation of biomarkers derived from single slides. The ability to uniquely identify slides from the same case additionally demonstrates the technical robustness of digital pathology models for yielding quantitative insights into tumor biology. Citation Format: Ylaine Gerardin, Christian Kirkup, Archit Khosla, Laura Chambre, Michael Drage, Amaro Taylor-Weiner. Digital pathology models reveal case-specific characteristics of the tumor microenvironment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-15-01.

How to Arrange Texts and Pictures for Online Visitors — Comparing Basic Ceramic Display Forms with Eye Tracking

In the context of online text–picture relic exhibitions, two exploratory experiments were conducted to investigate the role of integrated/separate display, background information, and annotation type in learning tangible heritage. Using ceramics as an example, we tracked the eye movement of subjects under different display forms and tested whether they obtained the relevant information. Experiment 1 (N = 48) adopted a 2 (integrated/separate display) × 2 (with/without background information) design and Experiment 2 (N = 93) investigated distinct types of annotation (no annotation, indicative/direct/picture/contour shape annotation). We found that the following. (1) In the segmented relic display, the usage time, fixation count, and total fixation duration of relic names were lower than those in the integrated case. The probability that subjects would learn comparatively was also lower in the separate display. However, the performance on retention or transfer tests did not differ depending on the integrated/separate display. After reading the background information, subjects paid less attention to relic names but had better knowledge transfer performance. (2) The viewers’ attention allocation to the materials was not significantly influenced by the annotations. Mere visual annotations did not provide an advantage for information acquisition. By contrast, indicative verbal annotation required relatively more time for better target information memory, and the direct verbal cue consumed the least time. Based on the results, we discussed the application scenario of multimedia learning principles and potential recommendations for designing online relic displays.

Abstract 5362: Targeting IRE1α alleviates the immunosuppressive tumor microenvironment in prostate cancer

Abstract Unfolded protein response (UPR) is a central stress response pathway in normal cells that is hijacked by tumor cells for their survival. However, how activation of UPR in cancer cells shapes the tumor microenvironment (TME) remain largely unexplored. Here, we investigated the role of IRE1α-XBP1s pathway on modulation of TME dynamics in prostate cancer (PCa). We found that IRE1α is increased in PCa patients and genetic or pharmacological inhibition of IRE1α signaling in the immunocompetent syngeneic mouse PCa model dramatically reduced tumor growth. Multiomics analysis of tumor samples upon IRE1α deletion in cancer cells showed significantly potentiated interferon (IFN) response and activation of immune system related pathways in the TME. Single-cell RNA sequencing (scRNA-seq) analysis revealed that the abundance of immunosuppressive cells, such as tumor-associated macrophages (TAMs) and T regulatory cells were markedly reduced in the IRE1α deficient tumors. Analysis of differentially expressed genes in the annotated cell types in the TME demonstrated that expression signatures associated with IFN responses were significantly enriched in TAMs, cancer cells, and dendritic cells. In addition, a novel scRNA-seq derived TAM gene signature is strongly associated with poor PCa survival. Furthermore, IRE1α inhibition by the small molecule MKC8866 (ORIN1001) that is in clinical trials significantly enhanced anti-PD-1 checkpoint inhibitor therapy in mice. Our findings indicate that IRE1α not only promotes cancer cell growth and survival, but it also strongly inhibits anti-tumor immunity in the PCa TME. Citation Format: Bilal Unal, Omer Faruk Kuzu, Yang Jin, Daniel Camilo Hurtado, Marieke Lydia Kuijjer, Mads Daugaard, Htoo Zarni Oo, John B. Patterson, Fahri Saatcioglu. Targeting IRE1α alleviates the immunosuppressive tumor microenvironment in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5362.

Prioritization of Kidney Cell Types Highlights Myofibroblast Cells in Regulating Human Blood Pressure

IntroductionBlood pressure (BP) is a highly heritable trait with over two thousand underlying genomic loci identified to date. While the kidney plays a key role, little is known about specific cell types involved in the genetic regulation of BP. MethodsHere, we applied stratified linkage disequilibrium score (LDSC) regression to connect blood pressure GWAS results to specific cell types of the mature human kidney. We used the largest single-stage BP genome-wide analysis to date, including up to 1,028,980 adults of European ancestry, and single-cell transcriptomic data from 14 mature human kidneys, 41 years mean of age. ResultsOur analyses prioritized myofibroblasts and endothelial cells, among the total of 33 annotated cell type, as specifically involved in BP regulation (p-value < 0.05/33 i.e., 0.001515). Enrichment of heritability for systolic BP was observed in myofibroblast cells in mature human kidney cortex, and enrichment of heritability for diastolic BP was observed in descending vasa recta and peritubular capillary endothelial cells as well as stromal myofibroblast cells. The new finding of myofibroblast, the significant cell type for both BP traits, was consistent in eight replication efforts using seven sets of independent data, including in human fetal kidney, in East Asian ancestry, using mouse scRNA-seq data, and when using another prioritization method. ConclusionsOur findings provide a solid basis for follow up studies to further identify genes and mechanisms in myofibroblast cells that underlie the regulation of BP.

Annotating microbial functions with ProkFunFind.

Analyzing microbial genomes has become an essential part of microbiology research, giving valuable insights into the functions and evolution of microbial species. Identifying genes of interest and assigning putative annotations to those genes is a central task in genome analysis, and a plethora of tools and approaches have been developed for this task. The ProkFunFind tool was developed to bridge the gap between these various annotation approaches, providing a flexible and customizable search approach to annotate microbial functions. ProkFunFind is designed around hierarchical definitions of biological functions, where individual genes can be identified using heterogeneous search terms consisting of sequences, profile hidden Markov models, protein domains, and orthology groups. This flexible and customizable search approach allows for searches to be tailored to specific biological functions, and the search results are output in multiple formats to facilitate downstream analyses. The utility of the ProkFunFind search tool was demonstrated through its application in searching for bacterial flagella, which are complex organelles composed of multiple genes. Overall, ProkFunFind provides an accessible and flexible way to integrate multiple types of annotation and sequence data while annotating biological functions in microbial genomes.IMPORTANCEGenome sequencing and analysis are increasingly important parts of microbiology, providing a way to predict metabolic functions, identify virulence factors, and understand the evolution of microbes. The expanded use of genome sequencing has also brought an abundance of search and annotation methods, but integrating the information from these different methods can be challenging and is often done through ad hoc approaches. To bridge the gap between different types of annotations, we developed ProkFunFind, a flexible and customizable search tool incorporating multiple search approaches and annotation types to annotate microbial functions. We demonstrated the utility of ProkFunFind by searching for gene clusters encoding flagellar genes using a combination of different annotation types and searches. Overall, ProkFunFind provides a reproducible and flexible way to identify gene clusters of interest, facilitating the meaningful analysis of new and existing microbial genomes.

A Qualitative Analysis of Common Practices in Annotations: A Taxonomy and Design Space.

Annotations play a vital role in highlighting critical aspects of visualizations, aiding in data externalization and exploration, collaborative sensemaking, and visual storytelling. However, despite their widespread use, we identified a lack of a design space for common practices for annotations. In this paper, we evaluated over 1,800 static annotated charts to understand how people annotate visualizations in practice. Through qualitative coding of these diverse real-world annotated charts, we explored three primary aspects of annotation usage patterns: analytic purposes for chart annotations (e.g., present, identify, summarize, or compare data features), mechanisms for chart annotations (e.g., types and combinations of annotations used, frequency of different annotation types across chart types, etc.), and the data source used to generate the annotations. We then synthesized our findings into a design space of annotations, highlighting key design choices for chart annotations. We presented three case studies illustrating our design space as a practical framework for chart annotations to enhance the communication of visualization insights. All supplemental materials are available at https://shorturl.at/bAGM1.

A Survey on Deep Active Learning: Recent Advances and New Frontiers.

Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label newly selected samples in a human-in-the-loop manner. This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep active learning (DAL), remain scarce. Therefore, we conduct an advanced and comprehensive survey on DAL. We first introduce reviewed paper collection and filtering. Second, we formally define the DAL task and summarize the most influential baselines and widely used datasets. Third, we systematically provide a taxonomy of DAL methods from five perspectives, including annotation types, query strategies, deep model architectures, learning paradigms, and training processes, and objectively analyze their strengths and weaknesses. Then, we comprehensively summarize the main applications of DAL in natural language processing (NLP), computer vision (CV), data mining (DM), and so on. Finally, we discuss challenges and perspectives after a detailed analysis of current studies. This work aims to serve as a useful and quick guide for researchers in overcoming difficulties in DAL. We hope that this survey will spur further progress in this burgeoning field.