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Metagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome from shotgun metagenomics data. The package of Contig Annotation Tool (CAT), Bin Annotation Tool (BAT), and RAT is available at https://github.com/MGXlab/CAT%5fpack (from CAT pack v6.0). The CAT pack now also supports Genome Taxonomy Database (GTDB) annotations. Metagenomic taxonomic profiling usually relies either on reads or assembled contigs/MAGs. Here, authors present RAT, a tool that integrates taxonomic signals from reads, contigs, and MAGs into one profile with high precision and sensitivity. RAT provides a comprehensive view of the microbiome. [ABSTRACT FROM AUTHOR]

Cotoneaster glaucophyllus is a semi-evergreen plant that blossoms in late summer, producing dense, attractive, fragrant white flowers with significant ornamental and ecological value. Here, a chromosome-scale genome assembly was obtained by integrating PacBio and Illumina sequencing data with the aid of Hi-C technology. The genome assembly was 563.3 Mb in length, with contig N50 and scaffold N50 values of ~6 Mb and ~31 Mb, respectively. Most (95.59%) of the sequences were anchored onto 17 pseudochromosomes (538.4 Mb). We predicted 35,856 protein-coding genes, 1,401 miRNAs, 655 tRNAs, 425 rRNAs, and 795 snRNAs. The functions of 34,967 genes (97.52%) were predicted. The availability of this chromosome-level genome will provide valuable resources for molecular studies of this species, facilitating future research on speciation, functional genomics, and comparative genomics within the Rosaceae family. [ABSTRACT FROM AUTHOR]

Although the emergence of deep learning has improved the performance of automatic building extraction, there is still a long way to go before it can completely replace the labour-intensive manual delineation of building contours. To narrow the gap between building extraction results of deep learning methods and manual-level annotations, this paper introduces an interactive semantic segmentation framework that uses manual clicks as interactive information to guide the process of semantic segmentation towards the manual annotation level. In our framework, we first use an interactive semantic segmentation network for coarse building extraction from high resolution remote sensing images. Then, we use an optimization network to further refine the extraction results. We comprehensively compare the automatic deep learning methods, the proposed interactive building extraction framework, and the full manual delineation in practical experimental settings. First, our interactive method can significantly improve the performance of automatic building extraction. Second, by comparing the efficiency of manual annotation using the ArcGIS software and our interactive method, it is found that the proposed method can save more than half of the time. The above results show the potential of the interactive method in improving the efficiency of contour annotation and reducing the cost of manual annotation. [ABSTRACT FROM AUTHOR]

Background: Contrastive learning, a successful form of representational learning, has shown promising results in pretraining deep learning (DL) models for downstream tasks. When working with limited annotation data, as in medical image segmentation tasks, learning domain‐specific local representations can further improve the performance of DL models. Purpose: In this work, we extend the contrastive learning framework to utilize domain‐specific contrast information from unlabeled Magnetic Resonance (MR) images to improve the performance of downstream MR image segmentation tasks in the presence of limited labeled data. Methods: The contrast in MR images is controlled by underlying tissue properties (e.g., T1 or T2) and image acquisition parameters. We hypothesize that learning to discriminate local representations based on underlying tissue properties should improve subsequent segmentation tasks on MR images. We propose a novel constrained contrastive learning (CCL) strategy that uses tissue‐specific information via a constraint map to define positive and negative local neighborhoods for contrastive learning, embedding this information in the representational space during pretraining. For a given MR contrast image, the proposed strategy uses local signal characteristics (constraint map) across a set of related multi‐contrast MR images as a surrogate for underlying tissue information. We demonstrate the utility of the approach for downstream: (1) multi‐organ segmentation tasks in T2‐weighted images where a DL model learns T2 information with constraint maps from a set of 2D multi‐echo T2‐weighted images (n = 101) and (2) tumor segmentation tasks in multi‐parametric images from the public brain tumor segmentation (BraTS) (n = 80) dataset where DL models learn T1 and T2 information from multi‐parametric BraTS images. Performance is evaluated on downstream multi‐label segmentation tasks with limited data in (1) T2‐weighted images of the abdomen from an in‐house Radial‐T2 (Train/Test = 30/20), (2) public Cartesian‐T2 (Train/Test = 6/12) dataset, and (3) multi‐parametric MR images from the public brain tumor segmentation dataset (BraTS) (Train/Test = 40/50). The performance of the proposed CCL strategy is compared to state‐of‐the‐art self‐supervised contrastive learning techniques. In each task, a model is also trained using all available labeled data for supervised baseline performance. Results: The proposed CCL strategy consistently yielded improved Dice scores, Precision, and Recall metrics, and reduced HD95 values across all segmentation tasks. We also observed performance comparable to the baseline with reduced annotation effort. The t‐SNE visualization of features for T2‐weighted images demonstrates its ability to embed T2 information in the representational space. On the BraTS dataset, we also observed that using an appropriate multi‐contrast space to learn T1+T2, T1, or T2 information during pretraining further improved the performance of tumor segmentation tasks. Conclusions: Learning to embed tissue‐specific information that controls MR image contrast with the proposed constrained contrastive learning improved the performance of DL models on subsequent segmentation tasks compared to conventional self‐supervised contrastive learning techniques. The use of such domain‐specific local representations could help understand, improve performance, and mitigate the scarcity of labeled data in MR image segmentation tasks. [ABSTRACT FROM AUTHOR]

This article examines the Annotationes in Novum Testamentum of Hugo Grotius (1583–1645) at Matt 23:2–3, both to understand more about Grotius's exegetical method and to generate additional data for the effects and uses of those verses. The following are general findings from the examination. First, Grotius shows a degree of independence to make text-critical decisions that affect translation. Second, Grotius's interpretation of Jesus's command to the scribes and Pharisees "who sit on the chair of Moses" depends upon an understanding of jurisprudence, eschews speculation about divine institution of the teaching office, and cuts off redeployment of Jesus's command in contemporary polemics among Christians. Third, Grotius puts Jesus's statements in conversation with the received wisdom from "pagan" antiquity. Fourth, Grotius demonstrates an awareness of options in the history of interpretation and desires, maybe naïvely and with some self-contradiction, to show himself in harmony with ancient Christian traditions. [ABSTRACT FROM AUTHOR]

Genetic studies of nematodes have been dominated by Caenorhabditis elegans as a model species. A lack of genomic resources has limited the expansion of genetic research to other groups of nematodes. Here, we report a draft genome assembly of a mermithid nematode, Mermis nigrescens. Mermithidae are insect parasitic nematodes with hosts including a wide range of terrestrial arthropods. We sequenced, assembled, and annotated the whole genome of M. nigrescens using nanopore long reads and 10X Chromium link reads. The assembly is 524 Mb in size consisting of 867 scaffolds. The N50 value is 2.42 Mb, and half of the assembly is in the 30 longest scaffolds. The assembly BUSCO score from the eukaryotic database (eukaryota_odb10) indicates that the genome is 86.7% complete and 5.1% partial. The genome has a high level of heterozygosity (6.6%) with a repeat content of 83.98%. mRNA-seq reads from different sized nematodes (≤2 cm, 3.5–7 cm, and >7 cm body length) representing different developmental stages were also generated and used for the genome annotation. Using ab initio and evidence-based gene model predictions, 12,313 protein-coding genes and 24,186 mRNAs were annotated. These genomic resources will help researchers investigate the various aspects of the biology and host–parasite interactions of mermithid nematodes. [ABSTRACT FROM AUTHOR]

This article investigates the stylometric usefulness of morphosyntactic annotation. Focusing on the style of literary texts, it argues that including morphosyntactic annotation in analyses of style has at least two important advantages: (1) maintaining a topic agnostic approach and (2) providing input variables that are interpretable in traditional grammatical terms. This study demonstrates how widely available Universal Dependency parsers can generate useful morphological and syntactic data for texts in a range of languages. These data can serve as the basis for input features that are strongly informative about the style of individual novels, as indicated by accuracy in classification tests. The interpretability of such features is demonstrated by a discussion of the weakness of an "authorial" signal as opposed to the clear distinction among individual works. [ABSTRACT FROM AUTHOR]

We describe an efficient pipeline for morpho-syntactically annotating an ancient language corpus which takes advantage of bootstrapping techniques. This pipeline is designed for ancient language scholars looking to jump-start their own treebank projects, which can in turn serve further pedagogical research projects in the target language. We situate our work in the field of similar ancient language treebank projects, arguing that our approach shows that individual humanities scholars can leverage current machine-learning tools to produce their own richly annotated corpora. We illustrate this pipeline by producing a new Akkadian-language treebank based on two volumes from the online editions of the State Archives of Assyria project hosted on Oracc, as well as a spaCy language model named AkkParser trained on that treebank. Both of these are made publicly available for annotating other Akkadian corpora. In addition, we discuss linguistic issues particular to the Neo-Assyrian letter corpus and data-encoding complications of cuneiform texts in Oracc. The strategies, language models, and processing scripts we developed to handle both linguistic and data-encoding issues in this project will be of special interest to scholars seeking to develop their own cuneiform treebanks. [ABSTRACT FROM AUTHOR]

This article introduces the CELI corpus, a new learner corpus of written Italian consisting of ca. 600,000 tokens, evenly distributed among CEFR (Common European Framework of Reference for Languages) proficiency levels B1, B2, C1 and C2. The collected texts derive from the language certification exams administered by the University for Foreigners of Perugia all around the world. The corpus contains rich metadata pertaining to text-related and learner-related variables. It expands the domain of learner corpora by being, among other things, both freely available online to the research community, and by focusing on a target language other than English. The article also presents and evaluates the POS-tagging procedure, thus contributing to best practices in learner corpus annotation. [ABSTRACT FROM AUTHOR]

The article focuses on the Queen's Men Editions (QME) project, which provides critical and performance editions of plays associated with the Queen's Men using the Linked Early Modern Drama Online (LEMDO) platform. Topics include the project's extensive annotations and multimedia integration, the adoption of Text Encoding Initiative (TEI) guidelines for transparency and consistency, and the user-friendly design of the LEMDO platform.

Manual anatomical landmarking for morphometric knee bone characterization in orthopedics is highly time-consuming and shows high operator variability. Therefore, automation could be a substantial improvement for diagnostics and personalized treatments relying on landmark-based methods. Applications include implant sizing and planning, meniscal allograft sizing, and morphological risk factor assessment. For twenty MRI-based 3D bone and cartilage models, anatomical landmarks were manually applied by three experts, and morphometric measurements for 3D characterization of the distal femur and proximal tibia were calculated from all observations. One expert performed the landmark annotations three times. Intra- and inter-observer variations were assessed for landmark position and measurements. The mean of the three expert annotations served as the ground truth. Next, automated landmark annotation was performed by elastic deformation of a template shape, followed by landmark optimization at extreme positions (highest/lowest/most medial/lateral point). The results of our automated annotation method were compared with ground truth, and percentages of landmarks and measurements adhering to different tolerances were calculated. Reliability was evaluated by the intraclass correlation coefficient (ICC). For the manual annotations, the inter-observer absolute difference was 1.53 ± 1.22 mm (mean ± SD) for the landmark positions and 0.56 ± 0.55 mm (mean ± SD) for the morphometric measurements. Automated versus manual landmark extraction differed by an average of 2.05 mm. The automated measurements demonstrated an absolute difference of 0.78 ± 0.60 mm (mean ± SD) from their manual counterparts. Overall, 92% of the automated landmarks were within 4 mm of the expert mean position, and 95% of all morphometric measurements were within 2 mm of the expert mean measurements. The ICC (manual versus automated) for automated morphometric measurements was between 0.926 and 1. Manual annotations required on average 18 min of operator interaction time, while automated annotations only needed 7 min of operator-independent computing time. Considering the time consumption and variability among observers, there is a clear need for a more efficient, standardized, and operator-independent algorithm. Our automated method demonstrated excellent accuracy and reliability for landmark positioning and morphometric measurements. Above all, this automated method will lead to a faster, scalable, and operator-independent morphometric analysis of the knee. [ABSTRACT FROM AUTHOR]

Annotation on the Judgment of the Court (First Chamber) of 20 April 2023, Case C-25/21 ZA and Others v Repsol Comercial de Productos Petrolíferos SA [2023] EU:C:2023:298. [ABSTRACT FROM AUTHOR]

Transposable elements (TEs) are major components of eukaryotic genomes and are implicated in a range of evolutionary processes. Yet, TE annotation and characterization remain challenging, particularly for nonspecialists, since existing pipelines are typically complicated to install, run, and extract data from. Current methods of automated TE annotation are also subject to issues that reduce overall quality, particularly (i) fragmented and overlapping TE annotations, leading to erroneous estimates of TE count and coverage, and (ii) repeat models represented by short sections of total TE length, with poor capture of 5′ and 3′ ends. To address these issues, we present Earl Grey, a fully automated TE annotation pipeline designed for user-friendly curation and annotation of TEs in eukaryotic genome assemblies. Using nine simulated genomes and an annotation of Drosophila melanogaster , we show that Earl Grey outperforms current widely used TE annotation methodologies in ameliorating the issues mentioned above while scoring highly in benchmarking for TE annotation and classification and being robust across genomic contexts. Earl Grey provides a comprehensive and fully automated TE annotation toolkit that provides researchers with paper-ready summary figures and outputs in standard formats compatible with other bioinformatics tools. Earl Grey has a modular format, with great scope for the inclusion of additional modules focused on further quality control and tailored analyses in future releases. [ABSTRACT FROM AUTHOR]

Copyright of Iranian Studies / Pizhūhish/hā-yi Īrānshināsī is the property of University of Tehran and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ḥawliyyaẗ Kulliyyaẗ Al-Adāb: Ǧāmiʿaẗ Binī Sūwaīf is the property of Beni Suef University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Facial expression recognition (FER) in the wild is extremely challenging due to occlusions, variant head poses under unconstrained conditions and incorrect annotations (e.g., label noise). In this article, we aim to improve the performance of in-the-wild FER with Transformers and online label correction. Different from pure CNNs based methods, we propose a Transformer-augmented network (TAN) to dynamically capture the relationships within each facial patch and across the facial patches. Specifically, the TAN translates a number of facial patch images into a set of visual feature sequences by a backbone convolutional neural network. The intra-patch Transformer is subsequently utilized to capture the most discriminative features within each visual feature sequence. The position-disentangled attention mechanism of the intra-patch Transformer is proposed to better incorporate the positional information for feature sequences. Furthermore, we propose the inter-patch Transformer to model the dependencies across these feature sequences. More importantly, we present the online label correction (OLC) framework to correct suspicious hard labels and accumulate soft labels based on the predictions of the model, which strengthens the robustness of our model against label noise. We validate our method on several widely-used datasets (RAF-DB, FERPlus, AffectNet), realistic occlusion and pose variation datasets, and synthetic noisy datasets. Extensive experiments on these benchmarks demonstrate that the proposed method performs favorably against state-of-the-art methods. The source code will be made publicly available. [ABSTRACT FROM AUTHOR]

To train machine learning algorithms to predict emotional expressions in terms of arousal and valence, annotated datasets are needed. However, as different people perceive others’ emotional expressions differently, their annotations are subjective. To account for this, annotations are typically collected from multiple annotators and averaged to obtain ground-truth labels. However, when exclusively trained on this averaged ground-truth, the model is agnostic to the inherent subjectivity in emotional expressions. In this work, we therefore propose an end-to-end Bayesian neural network capable of being trained on a distribution of annotations to also capture the subjectivity-based label uncertainty. Instead of a Gaussian, we model the annotation distribution using Student's $t$ t -distribution, which also accounts for the number of annotations available. We derive the corresponding Kullback-Leibler divergence loss and use it to train an estimator for the annotation distribution, from which the mean and uncertainty can be inferred. We validate the proposed method using two in-the-wild datasets. We show that the proposed $t$ t -distribution based approach achieves state-of-the-art uncertainty modeling results in speech emotion recognition, and also consistent results in cross-corpora evaluations. Furthermore, analyses reveal that the advantage of a $t$ t -distribution over a Gaussian grows with increasing inter-annotator correlation and a decreasing number of annotations available. [ABSTRACT FROM AUTHOR]

Although laughter is known to be a multimodal signal, it is primarily annotated from audio. It is unclear how laughter labels may differ when annotated from modalities like video, which capture body movements and are relevant in in-the-wild studies. In this work we ask whether annotations of laughter are congruent across modalities, and compare the effect that labeling modality has on machine learning model performance. We compare annotations and models for laughter detection, intensity estimation, and segmentation, using a challenging in-the-wild conversational dataset with a variety of camera angles, noise conditions and voices. Our study with 48 annotators revealed evidence for incongruity in the perception of laughter and its intensity between modalities, mainly due to lower recall in the video condition. Our machine learning experiments compared the performance of modern unimodal and multi-modal models for different combinations of input modalities, training, and testing label modalities. In addition to the same input modalities rated by annotators (audio and video), we trained models with body acceleration inputs, robust to cross-contamination, occlusion and perspective differences. Our results show that performance of models with body movement inputs does not suffer when trained with video-acquired labels, despite their lower inter-rater agreement. [ABSTRACT FROM AUTHOR]

Automating the detection and classification of archaeological features in remotely sensed imagery poses significant challenges due to image variability and the heterogeneity of archaeological objects, which traditional object-based solutions have struggled to fully address. However, recent advancements in deep learning offer promising avenues for overcoming these obstacles. Despite their widespread adoption, deep learning algorithms still face problems such as the lack of annotated data and the opacity of model decision-making processes. We address these challenges through an approach linking annotation scarcity and model explainability. We propose leveraging explainability techniques to not only gain insights into model decision-making processes but also to reduce annotation costs. Through a case study mapping ancient settlement mounds in Upper Mesopotamia, we apply three explainability methods to three widely-used deep learning architectures that were trained to recognize whether or not a site is present within an image. We then employ the output of the explainability methods to both delineate the sites’ boundaries, a more time-consuming process for a human expert, and to generate meaningful visual insights of the features found relevant by the networks for which we provide detailed interpretations. Additionally, we propose a new variant of an explainability method that produces site boundaries that are more consistent with the expert estimations than the existing solutions.

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

Background: Microbiota are closely associated with human health and disease. Metaproteomics can provide a direct means to identify microbial proteins in microbiota for compositional and functional characterization. However, in-depth and accurate metaproteomics is still limited due to the extreme complexity and high diversity of microbiota samples. It is generally recommended to use metagenomic data from the same samples to construct the protein sequence database for metaproteomic data analysis. Although different metagenomics-based database construction strategies have been developed, an optimization of gene taxonomic annotation has not been reported, which, however, is extremely important for accurate metaproteomic analysis. Results: Herein, we proposed an accurate taxonomic annotation pipeline for genes from metagenomic data, namely contigs directed gene annotation (ConDiGA), and used the method to build a protein sequence database for metaproteomic analysis. We compared our pipeline (ConDiGA or MD3) with two other popular annotation pipelines (MD1 and MD2). In MD1, genes were directly annotated against the whole bacterial genome database; in MD2, contigs were annotated against the whole bacterial genome database and the taxonomic information of contigs was assigned to the genes; in MD3, the most confident species from the contigs annotation results were taken as reference to annotate genes. Annotation tools, including BLAST, Kaiju, and Kraken2, were compared. Based on a synthetic microbial community of 12 species, it was found that Kaiju with the MD3 pipeline outperformed the others in the construction of protein sequence database from metagenomic data. Similar performance was also observed with a fecal sample, as well as in silico mixed datasets of the simulated microbial community and the fecal sample. Conclusions: Overall, we developed an optimized pipeline for gene taxonomic annotation to construct protein sequence databases. Our study can tackle the current taxonomic annotation reliability problem in metagenomics-derived protein sequence database and can promote the in-depth metaproteomic analysis of microbiome. The unique metagenomic and metaproteomic datasets of the 12 bacterial species are publicly available as a standard benchmarking sample for evaluating various analysis pipelines. The code of ConDiGA is open access at GitHub for the analysis of microbiota samples. 6yn7yKvS5c7jYK96vohJFU Video Abstract [ABSTRACT FROM AUTHOR]

Background: The constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and entity linking. However, manual annotation is expensive and time-consuming especially if not assisted by interactive, intuitive, and collaborative computer-aided tools. To support healthcare experts in the annotation process and foster annotated corpora creation, we present MetaTron. MetaTron is an open-source and free-to-use web-based annotation tool to annotate biomedical data interactively and collaboratively; it supports both mention-level and document-level annotations also integrating automatic built-in predictions. Moreover, MetaTron enables relation annotation with the support of ontologies, functionalities often overlooked by off-the-shelf annotation tools. Results: We conducted a qualitative analysis to compare MetaTron with a set of manual annotation tools including TeamTat, INCEpTION, LightTag, MedTAG, and brat, on three sets of criteria: technical, data, and functional. A quantitative evaluation allowed us to assess MetaTron performances in terms of time and number of clicks to annotate a set of documents. The results indicated that MetaTron fulfills almost all the selected criteria and achieves the best performances. Conclusions: MetaTron stands out as one of the few annotation tools targeting the biomedical domain supporting the annotation of relations, and fully customizable with documents in several formats—PDF included, as well as abstracts retrieved from PubMed, Semantic Scholar, and OpenAIRE. To meet any user need, we released MetaTron both as an online instance and as a Docker image locally deployable. [ABSTRACT FROM AUTHOR]

With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine's ability to understand urban scenes. Mesh data have a distinct advantage over point cloud data for large-scale scenes, as they can provide inherent geometric topology information and consume less memory space. However, existing publicly available large-scale scene mesh datasets are limited in scale and semantic richness and do not cover a wide range of urban semantic information. The development of 3D semantic segmentation algorithms depends on the availability of datasets. Moreover, existing large-scale 3D datasets lack various types of official annotation data, which hinders the widespread applicability of benchmark applications and may cause label errors during data conversion. To address these issues, we present a comprehensive urban-scale semantic segmentation benchmark dataset. It is suitable for various research pursuits on semantic segmentation methodologies. This dataset contains finely annotated point cloud and mesh data types for 3D, as well as high-resolution original 2D images with detailed 2D semantic annotations. It is constructed from a 3D reconstruction of 10,840 UVA aerial images and spans a vast area of approximately 2.85 square kilometers that covers both urban and rural scenes. The dataset is composed of 152,298,756 3D points and 289,404,088 triangles. Each 3D point, triangular mesh, and the original 2D image in the dataset are carefully labeled with one of the ten semantic categories. Six typical 3D semantic segmentation methods were compared on the CUS3D dataset, with KPConv demonstrating the highest overall performance. The mIoU is 59.72%, OA is 89.42%, and mAcc is 97.88%. Furthermore, the experimental results on the impact of color information on semantic segmentation suggest that incorporating both coordinate and color features can enhance the performance of semantic segmentation. The current limitations of the CUS3D dataset, particularly in class imbalance, will be the primary target for future dataset enhancements. [ABSTRACT FROM AUTHOR]

Transposable elements (TEs) are a major component of eukaryotic genomes and are present in almost all eukaryotic organisms. TEs are highly dynamic between and within species, which significantly affects the general applicability of the TE databases. Orthoptera is the only known group in the class Insecta with a significantly enlarged genome (0.93-21.48 Gb). When analyzing the large genome using the existing TE public database, the efficiency of TE annotation is not satisfactory. To address this limitation, it becomes imperative to continually update the available TE resource library and the need for an Orthoptera-specific library as more insect genomes are publicly available. Here, we used the complete genome data of 12 Orthoptera species to de novo annotate TEs, then manually re-annotate the unclassified TEs to construct a non-redundant Orthoptera-specific TE library: Orthoptera-TElib. Orthoptera-TElib contains 24,021 TE entries including the re-annotated results of 13,964 unknown TEs. The naming of TE entries in Orthoptera-TElib adopts the same naming as RepeatMasker and Dfam and is encoded as the three-level form of "level1/level2-level3". Orthoptera-TElib can be directly used as an input reference database and is compatible with mainstream repetitive sequence analysis software such as RepeatMasker and dnaPipeTE. When analyzing TEs of Orthoptera species, Orthoptera-TElib performs better TE annotation as compared to Dfam and Repbase regardless of using low-coverage sequencing or genome assembly data. The most improved TE annotation result is Angaracris rhodopa, which has increased from 7.89% of the genome to 53.28%. Finally, Orthoptera-TElib is stored in Sqlite3 for the convenience of data updates and user access. [ABSTRACT FROM AUTHOR]

Background: The spread of Popillia japonica in non-native areas (USA, Canada, the Azores islands, Italy and Switzerland) poses a significant threat to agriculture and horticulture, as well as to endemic floral biodiversity, entailing that appropriate control measures must be taken to reduce its density and limit its further spread. In this context, the availability of a high quality genomic sequence for the species is liable to foster basic research on the ecology and evolution of the species, as well as on possible biotechnologically-oriented and genetically-informed control measures. Results: The genomic sequence presented and described here is an improvement with respect to the available draft sequence in terms of completeness and contiguity, and includes structural and functional annotations. A comparative analysis of gene families of interest, related to the species ecology and potential for polyphagy and adaptability, revealed a contraction of gustatory receptor genes and a paralogous expansion of some subgroups/subfamilies of odorant receptors, ionotropic receptors and cytochrome P450s. Conclusions: The new genomic sequence as well as the comparative analyses data may provide a clue to explain the staggering invasive potential of the species and may serve to identify targets for potential biotechnological applications aimed at its control. [ABSTRACT FROM AUTHOR]

Summary Knowledge of immunoglobulin and T cell receptor encoding genes is derived from high-quality genomic sequencing. High-throughput sequencing is delivering large volumes of data, and precise, high-throughput approaches to annotation are needed. Digger is an automated tool that identifies coding and regulatory regions of these genes, with results comparable to those obtained by current expert curational methods. Availability and implementation Digger is published under open source license at https://github.com/williamdlees/Digger and is available as a Python package and a Docker container. [ABSTRACT FROM AUTHOR]

Motivation Automatic cell type annotation methods assign cell type labels to new datasets by extracting relationships from a reference RNA-seq dataset. However, due to the limited resolution of gene expression features, there is always uncertainty present in the label assignment. To enhance the reliability and robustness of annotation, most machine learning methods address this uncertainty by providing a full reject option, i.e. when the predicted confidence score of a cell type label falls below a user-defined threshold, no label is assigned and no prediction is made. As a better alternative, some methods deploy hierarchical models and consider a so-called partial rejection by returning internal nodes of the hierarchy as label assignment. However, because a detailed experimental analysis of various rejection approaches is missing in the literature, there is currently no consensus on best practices. Results We evaluate three annotation approaches (i) full rejection, (ii) partial rejection, and (iii) no rejection for both flat and hierarchical probabilistic classifiers. Our findings indicate that hierarchical classifiers are superior when rejection is applied, with partial rejection being the preferred rejection approach, as it preserves a significant amount of label information. For optimal rejection implementation, the rejection threshold should be determined through careful examination of a method's rejection behavior. Without rejection, flat and hierarchical annotation perform equally well, as long as the cell type hierarchy accurately captures transcriptomic relationships. Availability and implementation Code is freely available at https://github.com/Latheuni/Hierarchical%5freject and https://doi.org/10.5281/zenodo.10697468. [ABSTRACT FROM AUTHOR]

Motivation MetaCerberus is a massively parallel, fast, low memory, scalable annotation tool for inference gene function across genomes to metacommunities. MetaCerberus provides an elusive HMM/HMMER-based tool at a rapid scale with low memory. It offers scalable gene elucidation to major public databases, including KEGG (KO), COGs, CAZy, FOAM, and specific databases for viruses, including VOGs and PHROGs, from single genomes to metacommunities. Results MetaCerberus is 1.3× as fast on a single node than eggNOG-mapper v2 on 5× less memory using an exclusively HMM/HMMER mode. In a direct comparison, MetaCerberus provides better annotation of viruses, phages, and archaeal viruses than DRAM, Prokka, or InterProScan. MetaCerberus annotates more KOs across domains when compared to DRAM, with a 186× smaller database, and with 63× less memory. MetaCerberus is fully integrated for automatic analysis of statistics and pathways using differential statistic tools (i.e. DESeq2 and edgeR), pathway enrichment (GAGE R), and pathview R. MetaCerberus provides a novel tool for unlocking the biosphere across the tree of life at scale. Availability and implementation MetaCerberus is written in Python and distributed under a BSD-3 license. The source code of MetaCerberus is freely available at https://github.com/raw-lab/metacerberus compatible with Python 3 and works on both Mac OS X and Linux. MetaCerberus can also be easily installed using bioconda: mamba create -n metacerberus -c bioconda -c conda-forge metacerberus. [ABSTRACT FROM AUTHOR]

Background: Accurate pre-surgical templating of the pelvic tilt (PT) angle is essential for hip and spine surgeries, yet the reliability of PT annotations is often compromised by human error, inherent subjectivity, and variations in radiographic quality. This study aims to identify challenges leading to inadequate annotations at a landmark dimension and evaluating their impact on PT. Methods: We retrospectively collected 115 consecutive sagittal radiographs for the measurement of PT based on two definitions: the anterior pelvic plane and a line connecting the femoral head's centre to the sacral plate's midpoint. Five annotators engaged in the measurement, followed by a secondary review to assess the adequacy of the annotations across all the annotators. Results: The outcomes indicated that over 60% images had at least one landmark considered inadequate by the majority of the reviewers, with poor image quality, outliers, and unrecognized anomalies being the primary causes. Such inadequacies led to discrepancies in the PT measurements, ranging from −2° to 2°. Conclusion: This study highlights that landmarks annotated from clear anatomical references were more reliable than those estimated. It also underscores the prevalence of suboptimal annotations in PT measurements, which extends beyond the scope of traditional statistical analysis and could result in significant deviations in individual cases, potentially impacting clinical outcomes. [ABSTRACT FROM AUTHOR]

The proliferation of single-cell RNA-seq data has greatly enhanced our ability to comprehend the intricate nature of diverse tissues. However, accurately annotating cell types in such data, especially when handling multiple reference datasets and identifying novel cell types, remains a significant challenge. To address these issues, we introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT), an innovative cell-type annotation method adept at integrating multiple reference datasets and uncovering previously unseen cell types. scDOT introduces two key innovations. First, by incorporating distance metric learning and optimal transport, it presents a novel optimization framework. This framework effectively learns the predictive power of each reference dataset for new query data and simultaneously establishes a probabilistic mapping between cells in the query data and reference-defined cell types. Secondly, scDOT develops an interpretable scoring system based on the acquired probabilistic mapping, enabling the precise identification of previously unseen cell types within the data. To rigorously assess scDOT's capabilities, we systematically evaluate its performance using two diverse collections of benchmark datasets encompassing various tissues, sequencing technologies and diverse cell types. Our experimental results consistently affirm the superior performance of scDOT in cell-type annotation and the identification of previously unseen cell types. These advancements provide researchers with a potent tool for precise cell-type annotation, ultimately enriching our understanding of complex biological tissues. [ABSTRACT FROM AUTHOR]

Motivation Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable. Results We herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL. Availability An implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch. Contact dengmh@pku.edu.cn Supplementary information Supplementary data are available at Journal Name online. [ABSTRACT FROM AUTHOR]

The evolution in single-cell RNA sequencing (scRNA-seq) technology has opened a new avenue for researchers to inspect cellular heterogeneity with single-cell precision. One crucial aspect of this technology is cell-type annotation, which is fundamental for any subsequent analysis in single-cell data mining. Recently, the scientific community has seen a surge in the development of automatic annotation methods aimed at this task. However, these methods generally operate at a steady-state total cell-type capacity, significantly restricting the cell annotation systems'capacity for continuous knowledge acquisition. Furthermore, creating a unified scRNA-seq annotation system remains challenged by the need to progressively expand its understanding of ever-increasing cell-type concepts derived from a continuous data stream. In response to these challenges, this paper presents a novel and challenging setting for annotation, namely cell-type incremental annotation. This concept is designed to perpetually enhance cell-type knowledge, gleaned from continuously incoming data. This task encounters difficulty with data stream samples that can only be observed once, leading to catastrophic forgetting. To address this problem, we introduce our breakthrough methodology termed scEVOLVE, an incremental annotation method. This innovative approach is built upon the methodology of contrastive sample replay combined with the fundamental principle of partition confidence maximization. Specifically, we initially retain and replay sections of the old data in each subsequent training phase, then establish a unique prototypical learning objective to mitigate the cell-type imbalance problem, as an alternative to using cross-entropy. To effectively emulate a model that trains concurrently with complete data, we introduce a cell-type decorrelation strategy that efficiently scatters feature representations of each cell type uniformly. We constructed the scEVOLVE framework with simplicity and ease of integration into most deep softmax-based single-cell annotation methods. Thorough experiments conducted on a range of meticulously constructed benchmarks consistently prove that our methodology can incrementally learn numerous cell types over an extended period, outperforming other strategies that fail quickly. As far as our knowledge extends, this is the first attempt to propose and formulate an end-to-end algorithm framework to address this new, practical task. Additionally, scEVOLVE, coded in Python using the Pytorch machine-learning library, is freely accessible at https://github.com/aimeeyaoyao/scEVOLVE. [ABSTRACT FROM AUTHOR]

Liquid chromatography coupled with high-resolution mass spectrometry data-independent acquisition (LC-HRMS/DIA), including MSE, enable comprehensive metabolomics analyses though they pose challenges for data processing with automatic annotation and molecular networking (MN) implementation. This motivated the present proposal, in which we introduce DIA-IntOpenStream, a new integrated workflow combining open-source software to streamline MSE data handling. It provides 'in-house' custom database construction, allows the conversion of raw MSE data to a universal format (.mzML) and leverages open software (MZmine 3 and MS-DIAL) all advantages for confident annotation and effective MN data interpretation. This pipeline significantly enhances the accessibility, reliability and reproducibility of complex MSE/DIA studies, overcoming previous limitations of proprietary software and non-universal MS data formats that restricted integrative analysis. We demonstrate the utility of DIA-IntOpenStream with two independent datasets: dataset 1 consists of new data from 60 plant extracts from the Ocotea genus; dataset 2 is a publicly available actinobacterial extract spiked with authentic standard for detailed comparative analysis with existing methods. This user-friendly pipeline enables broader adoption of cutting-edge MS tools and provides value to the scientific community. Overall, it holds promise for speeding up metabolite discoveries toward a more collaborative and open environment for research. [ABSTRACT FROM AUTHOR]

Somatic copy number alterations (SCNAs) are a predominant type of oncogenomic alterations that affect a large proportion of the genome in the majority of cancer samples. Current technologies allow high-throughput measurement of such copy number aberrations, generating results consisting of frequently large sets of SCNA segments. However, the automated annotation and integration of such data are particularly challenging because the measured signals reflect biased, relative copy number ratios. In this study, we introduce labelSeg , an algorithm designed for rapid and accurate annotation of CNA segments, with the aim of enhancing the interpretation of tumor SCNA profiles. Leveraging density-based clustering and exploiting the length–amplitude relationships of SCNA, our algorithm proficiently identifies distinct relative copy number states from individual segment profiles. Its compatibility with most CNA measurement platforms makes it suitable for large-scale integrative data analysis. We confirmed its performance on both simulated and sample-derived data from The Cancer Genome Atlas reference dataset, and we demonstrated its utility in integrating heterogeneous segment profiles from different data sources and measurement platforms. Our comparative and integrative analysis revealed common SCNA patterns in cancer and protein-coding genes with a strong correlation between SCNA and messenger RNA expression, promoting the investigation into the role of SCNA in cancer development. [ABSTRACT FROM AUTHOR]

Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq. [ABSTRACT FROM AUTHOR]

Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of challenging immune cells and reduce the impact of label noise by facilitating mutual reinforcement between cell type annotation and protein prediction tasks. Furthermore, we introduced logarithmic weighting and label smoothing mechanisms to enhance the recognition ability of rare cell types and prevent model overconfidence. Through comprehensive evaluations on multiple public datasets, scMMT has demonstrated state-of-the-art performance in various aspects including cell type annotation, rare cell identification, dropout and label noise resistance, protein expression prediction and low-dimensional embedding representation. [ABSTRACT FROM AUTHOR]

The process of identification and annotation of evaluation has received a lot of attention in recent years. However, given the complexity of the topic, the discussion of some of the central issues is still ongoing. The present article contributes to this debate by presenting an annotation scheme that is designed for the identification and annotation of evaluative stance in a corpus of four English genres, namely, newspaper discourse, political discourse, newspaper scientific popularization and fora. A 4,862-word corpus was sampled from a larger 400,000-word corpus compiled within the research project STANCEDISC on the study of stance in discourse varieties. The scheme posits a series of ad hoc categories designed to optimise the transparency, reliability and replicability of the identification, annotation and analysis of evaluative stance. The categories are as follows: parts of speech (Noun Phrase, NP; Adjectival Phrase, AP; Adverbial Phrase, ADVP; Verbal Phrase, VP), function (classifying, predicational and attitude), metaphoricity (metaphoric and non-metaphoric), and value (positive and negative). The aim of this paper is to explain the scheme together with the theoretical justification of the categories and the methodological procedure adopted, and to illustrate the implementation of the scheme by discussing examples taken from different genres. [ABSTRACT FROM AUTHOR]

• The giant milkweed Calotropis procera (Apocynaceae) is a wild xerophytic shrub that is well adapted to extreme desert environments and has a broad gene reserve for stress tolerance. • De novo genome assembly and its associated analyses were carried out to decode its genetic structure. • The assembly of the genome comprised 221 mb of 24,374 scaffolds with an N50 of 655 kb. • A gene prediction analysis performed using an ab initio gene annotation pipeline identified 20,660 coding sequences. • The functional annotation reported genes involved in tolerance, photoacclimation, and nitrogenous compound detoxification. Abiotic stresses can intensely affect plant survival and productivity. The comprehension of the molecular mechanisms underlying stress responses in tolerant plant species is critical for the breeding of stress-adapted plant cultivars. The giant milkweed Calotropis procera (Apocynaceae) is a wild xerophytic shrub that is well adapted to extreme desert environments and has a broad gene reserve for stress tolerance. De novo genome assembly and its associated analyses were carried out in this highly tolerant but underexploited xerophyte, to decode its genetic structure. Paired-end (PE) and mate-pair (MP) libraries with an insert size of 350 bp and 5 kb, respectively, were constructed. An estimated genome size of 0.238 Gb was calculated based on the k-mer frequency of the read data. A high-quality draft assembly was generated via the short reads obtained using the Illumina sequencing technology and the ∼68.5 GB of genomic sequencing data that were acquired utilizing the two libraries with insert sizes of 350 bp and 5 kb. The assembly of the genome comprised 221 Mb of 24,374 scaffolds with an N50 of 655 kb. A gene prediction analysis performed using an ab initio gene annotation pipeline identified 20,660 coding sequences, including those related to transposable elements. The analysis of the functional annotation of the genome coding sequences reported genes involved in tolerance, photoacclimation, vacuole membrane transport, and nitrogenous compound detoxification. This is the first draft of the genome of C. procera , which will provide a deeper understanding of abiotic stress tolerance and contribute to the investigation of the biosynthesis of secondary metabolites particular to this milkweed. [ABSTRACT FROM AUTHOR]

Parelaphostrongylus tenuis causes ungulate morbidity and mortality in eastern and central North America, but no reference genome sequence exists to facilitate research. Here, we present a P. tenuis genome assembly and annotation, generated with PacBio and Illumina technologies. The assembly is 491 Mbp, with 7285 scaffolds and 185 kb N50. [ABSTRACT FROM AUTHOR]

Measuring parental care behaviour in the wild is central to the study of animal ecology and evolution, but it is often labour‐ and time‐intensive. Efficient open‐source tools have recently emerged that allow animal behaviour to be quantified from videos using machine learning and computer vision techniques, but there is limited appraisal of how these tools perform compared to traditional methods. To gain insight into how different methods perform in extracting data from videos taken in the field, we compared estimates of the parental provisioning rate of wild house sparrows Passer domesticus from video recordings. We compared four methods: manual annotation by experts, crowd‐sourcing, automatic detection based on the open‐source software DeepMeerkat, and a hybrid annotation method. We found that the data collected by the automatic method correlated with expert annotation (r = 0.62) and further show that these data are biologically meaningful as they predict brood survival. However, the automatic method produced largely biased estimates due to the detection of non‐visitation events, while the crowd‐sourcing and hybrid annotation produced estimates that are equivalent to expert annotation. The hybrid annotation method takes approximately 20% of annotation time compared to manual annotation, making it a more cost‐effective way to collect data from videos. We provide a successful case study of how different approaches can be adopted and evaluated with a pre‐existing dataset, to make informed decisions on the best way to process video datasets. If pre‐existing frameworks produce biased estimates, we encourage researchers to adopt a hybrid approach of first using machine learning frameworks to preprocess videos, and then to do manual annotation to save annotation time. As open‐source machine learning tools are becoming more accessible, we encourage biologists to make use of these tools to cut annotation time but still get equally accurate results without the need to develop novel algorithms from scratch. [ABSTRACT FROM AUTHOR]

In the last two decades, four Portuguese syntactically annotated corpora were built along the lines initially defined for the Penn Parsed Historical Corpora (Santorini, 2016). They cover the old, the middle, the classical and the modern periods of European Portuguese, as well as the nineteenth and twentieth century Brazilian Portuguese, and include different textual genres and oral discourse excerpts. Together they provide a fundamental resource for the study of variation and change in Portuguese. In the last years, an effort was made to maximally unify the annotation scheme applied to those corpora, in such a way that the searches done on one corpus could be done in exactly the same manner on the others. This effort resulted in the Portuguese Syntactic Annotation Manual (Magro & Galves, 2019). In this paper, we present the syntactic annotation for the Portuguese Corpora. We describe the functioning of ParsPort, a rule-based parser which makes use of the revision mode of the query language Corpus Search (Randall, 2005–2015). We argue that ParsPort is more efficient to our annotation efforts than the probabilistic parser developed by Bikel (2004), previously used for the syntactic annotation of the Portuguese Corpora. Finally we mention recent advances towards more user-friendly tools for syntactic searches. [ABSTRACT FROM AUTHOR]

• Artificial intelligence in urology. • Artificial intelligence in robotic urological surgery. • Surgical intelligence identifying step annotations in urologic surgery. • Real-time artificial intelligence annotations in robotic surgery. Real-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for surgical education and qualitative initiatives. We demonstrate the first use of AI in urologic robotic surgery to capture live surgical video and annotate key surgical steps and safety milestones in real-time. While AI models possess the capability to generate automated annotations based on a collection of video images, the real-time implementation of such technology in urological robotic surgery to aid surgeon and training staff it is still pending to be studied. We conducted an educational symposium, which broadcasted 2 live procedures, a robotic-assisted radical prostatectomy (RARP) and a robotic-assisted partial nephrectomy (RAPN). A surgical AI platform system (Theator, Palo Alto, CA) generated real-time annotations and identified operative safety milestones. This was achieved through trained algorithms, conventional video recognition, and novel Video Transfer Network technology which captures clips in full context, enabling automatic recognition and surgical mapping in real-time. Real-time AI annotations for procedure #1, RARP, are found in Table 1. The safety milestone annotations included the apical safety maneuver and deliberate views of structures such as the external iliac vessels and the obturator nerve. Real-time AI annotations for procedure #2, RAPN, are found in Table 1. Safety milestones included deliberate views of structures such as the gonadal vessels and the ureter. AI annotated surgical events included intraoperative ultrasound, temporary clip application and removal, hemostatic powder application, and notable hemorrhage. For the first time, surgical intelligence successfully showcased real-time AI annotations of 2 separate urologic robotic procedures during a live telecast. These annotations may provide the technological framework for send automatic notifications to clinical or operational stakeholders. This technology is a first step in real-time intraoperative decision support, leveraging big data to improve the quality of surgical care, potentially improve surgical outcomes, and support training and education. [ABSTRACT FROM AUTHOR]

We present the first chromosome-level genome assembly and annotation of the pearly heath Coenonympha arcania , generated with a PacBio HiFi sequencing approach and complemented with Hi-C data. We additionally compare synteny, gene, and repeat content between C. arcania and other Lepidopteran genomes. This reference genome will enable future population genomics studies with Coenonympha butterflies, a species-rich genus that encompasses some of the most highly endangered butterfly taxa in Europe. [ABSTRACT FROM AUTHOR]

We report a high-quality genome draft assembly of the dark-branded bushbrown, Mycalesis mineus , a member of the Satyrinae subfamily of nymphalid butterflies. This species is emerging as a promising model organism for investigating the evolution and development of phenotypic plasticity. Using 45.99 Gb of long-read data (N50 = 11.11 kb), we assembled a genome size of 497.4 Mb for M. mineus. The assembly is highly contiguous and nearly complete (96.8% of Benchmarking Universal Single-Copy Orthologs lepidopteran genes were complete and single copy). The genome comprises 38.71% of repetitive elements and includes 20,967 predicted protein-coding genes. The assembled genome was super-scaffolded into 28 pseudo-chromosomes using a closely related species, Bicyclus anynana , with a chromosomal-level genome as a template. This valuable genomic tool will advance both ongoing and future research focused on this model organism. [ABSTRACT FROM AUTHOR]

Penstemon is the most speciose flowering plant genus endemic to North America. Penstemon species' diverse morphology and adaptation to various environments have made them a valuable model system for studying evolution. Here, we report the first full reference genome assembly and annotation for Penstemon davidsonii. Using PacBio long-read sequencing and Hi-C scaffolding technology, we constructed a de novo reference genome of 437,568,744 bases, with a contig N50 of 40 Mb and L50 of 5. The annotation includes 18,199 gene models, and both the genome and transcriptome assembly contain over 95% complete eudicot BUSCOs. This genome assembly will serve as a valuable reference for studying the evolutionary history and genetic diversity of the Penstemon genus. [ABSTRACT FROM AUTHOR]

Automatic image annotation (AIA) is a mechanism for describing the visual content of an image with a list of semantic labels. Typically, there is a massive imbalance between positive and negative tags in a picture—in other words, an image includes much fewer positive labels than negative ones. This imbalance can negatively affect the optimization process and diminish the emphasis on gradients from positive labels during training. Although traditional annotation models mainly focus on model structure design, we propose a novel unsymmetrical loss function for a deep convolutional neural network (CNN) that performs differently on positives and negatives, which leads to a reduction in the loss contribution from negative labels and also highlights the contribution of positive ones. During the annotation process, we specify a threshold for each label separately based on the Matthews correlation coefficient (MCC). Extensive experiments on high-vocabulary datasets like Corel 5k, IAPR TC-12, and Esp Game reveal that despite ignoring the semantic relationships between labels, our suggested approach achieves remarkable results compared to the state-of-the-art automatic image annotation models. [ABSTRACT FROM AUTHOR]

To address the limitations of existing methods of short-text entity disambiguation, specifically in terms of their insufficient feature extraction and reliance on massive training samples, we propose an entity disambiguation model called COLBERT, which fuses LDA-based topic features and BERT-based semantic features, as well as using contrastive learning, to enhance the disambiguation process. Experiments on a publicly available Chinese short-text entity disambiguation dataset show that the proposed model achieves an F1-score of 84.0%, which outperforms the benchmark method by 0.6%. Moreover, our model achieves an F1-score of 74.5% with a limited number of training samples, which is 2.8% higher than the benchmark method. These results demonstrate that our model achieves better effectiveness and robustness and can reduce the burden of data annotation as well as training costs. [ABSTRACT FROM AUTHOR]

Background: The Grand‐AID research project, consisting of GRANDEL‐The Beautyness Company, the dermatology department of Augsburg University Hospital and the Chair of IT Infrastructure for Translational Medical Research at Augsburg University, is currently researching the development of a digital skin consultation tool that uses artificial intelligence (AI) to analyze the user's skin and ultimately perform a personalized skin analysis and a customized skin care routine. Training the AI requires annotation of various skin features on facial images. The central question is whether videos are better suited than static images for assessing dynamic parameters such as wrinkles and elasticity. For this purpose, a pilot study was carried out in which the annotations on images and videos were compared. Materials and Methods: Standardized image sequences as well as a video with facial expressions were taken from 25 healthy volunteers. Four raters with dermatological expertise annotated eight features (wrinkles, redness, shine, pores, pigmentation spots, dark circles, skin sagging, and blemished skin) with a semi‐quantitative and a linear scale in a cross‐over design to evaluate differences between the image modalities and between the raters. Results: In the videos, most parameters tended to be assessed with higher scores than in the images, and in some cases significantly. Furthermore, there were significant differences between the raters. Conclusion: The present study shows significant differences between the two evaluation methods using image or video analysis. In addition, the evaluation of the skin analysis depends on subjective criteria. Therefore, when training the AI, we recommend regular training of the annotating individuals and cross‐validation of the annotation. [ABSTRACT FROM AUTHOR]

Bioinformatics resolves biological problems by predicting the structural-functional annotation of genes and substantially contributes to further downstream applications in the field of genomics. Most of the proteins from Colletotrichum gloeosporoides are uncharacterized yet, which could be of novel biological functions and help to understand the biological function. Hence, in this study, we aimed to annotate the zinc-finger dhhc domain containing uncharacterized protein A0A8H4C4Q8_COLGL of C. gloeosporoides. The uncharacterized protein was analyzed for subcellular localization, physicochemical characteristics, secretory nature, antigenicity, allergenicity determination, conserved domain identification, secondary structure prediction, 3D modeling, docking analysis, active site prediction, and protein-protein interaction. The structural-functional annotation predicted it as an effector protein possessing a role in pathogenicity. [ABSTRACT FROM AUTHOR]