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Whitebark pine (WBP, Pinus albicaulis) is a white pine of subalpine regions in the Western contiguous United States and Canada. WBP has become critically threatened throughout a significant part of its natural range due to mortality from the introduced fungal pathogen white pine blister rust (WPBR, Cronartium ribicola) and additional threats from mountain pine beetle (Dendroctonus ponderosae), wildfire, and maladaptation due to changing climate. Vast acreages of WBP have suffered nearly complete mortality. Genomic technologies can contribute to a faster, more cost-effective approach to the traditional practices of identifying disease-resistant, climate-adapted seed sources for restoration. With deep-coverage Illumina short reads of haploid megagametophyte tissue and Oxford Nanopore long reads of diploid needle tissue, followed by a hybrid, multistep assembly approach, we produced a final assembly containing 27.6 Gb of sequence in 92,740 contigs (N50 537,007 bp) and 34,716 scaffolds (N50 2.0 Gb). Approximately 87.2% (24.0 Gb) of total sequence was placed on the 12 WBP chromosomes. Annotation yielded 25,362 protein-coding genes, and over 77% of the genome was characterized as repeats. WBP has demonstrated the greatest variation in resistance to WPBR among the North American white pines. Candidate genes for quantitative resistance include disease resistance genes known as nucleotide-binding leucine-rich repeat receptors (NLRs). A combination of protein domain alignments and direct genome scanning was employed to fully describe the 3 subclasses of NLRs. Our high-quality reference sequence and annotation provide a marked improvement in NLR identification compared to previous assessments that leveraged de novo-assembled transcriptomes.

Field‐collected specimens were used to obtain nine high‐quality genome assemblies from a total of 10 insect species native to prairies and savannas of central Illinois (USA): Mellilla xanthometata (Lepidoptera: Geometridae), Stenolophus ochropezus (Coleoptera: Carabidae), Forcipata loca (Hemiptera: Cicadellidae), Coelinius sp. (Hymenoptera: Braconidae), Thaumatomyia glabra (Diptera: Chloropidae), Brachynemurus abdominalus (Neuroptera: Myrmeleontidae), Catonia carolina (Hemiptera: Achilidae), Oncometopia orbona (Hemiptera: Cicadellidae), Flexamia atlantica (Hemiptera: Cicadellidae) and Stictocephala bisonia (Hemiptera: Membracidae). Sequencing library preparation from single specimens was successful despite extremely small DNA yields (<0.1 μg) for some samples. Additional sequencing and assembly workflows were adapted to each sample depending on the initial DNA yield. PacBio circular consensus (CCS/HiFi) or continuous long reads (CLR) libraries were used to sequence DNA fragments up to 50 kb in length, with Illumina sequenced linked‐reads (TellSeq libraries) and Omni‐C libraries used for scaffolding and gap‐filling. Assembled genome sizes ranged from 135 MB to 3.2 GB. The number of assembled scaffolds ranged from 47 to >13,000, with the longest scaffold per assembly ranging from ~23 to 439 Mb. Genome completeness was high, with BUSCO scores ranging from 85.5% completeness for the largest genome (Stictocephala bisonia) to 98.8% completeness for the smallest genome (Coelinius sp.). The unique content was estimated using RepeatMasker and GenomeScope2, which ranged from 50.7% to 75.8% and roughly decreased with increasing genome size. Structural annotation predicted a range of 19,281–72,469 protein models for sequenced species. Sequencing costs per genome at the time ranged from US$3–5k, averaged ~1600 CPU‐hours on a high‐performance cluster and required approximately 14 h of bioinformatics analyses with samples using PacBio HiFi data. Most assemblies would benefit from further manual curation to correct possible scaffold misjoins and translocations suggested by off‐diagonal or depleted signals in Omni‐C contact maps. [ABSTRACT FROM AUTHOR]

Dental disorders are common worldwide, causing pain or infections and limiting mouth opening, so dental conditions impact productivity, work capability, and quality of life. Manual detection and classification of oral diseases is time-consuming and requires dentists' evaluation and examination. The dental disease detection and classification system based on machine learning and deep learning will aid in early dental disease diagnosis. Hence, this paper proposes a new diagnosis system for dental diseases using X-ray imaging. The framework includes a robust pre-processing phase that uses image normalization and adaptive histogram equalization to improve image quality and reduce variation. A dual-stream approach is used for feature extraction, utilizing the advantages of Swin Transformer for capturing long-range dependencies and global context and MobileNetV2 for effective local feature extraction. A thorough representation of dental anomalies is produced by fusing the extracted features. To obtain reliable and broadly applicable classification results, a bagging ensemble classifier is utilized in the end. We evaluate our model on a benchmark dental radiography dataset. The experimental results and comparisons show the superiority of the proposed system with 95.7% for precision, 95.4% for sensitivity, 95.7% for specificity, 95.5% for Dice similarity coefficient, and 95.6% for accuracy. The results demonstrate the effectiveness of our hybrid model integrating MoileNetv2 and Swin Transformer architectures, outperforming state-of-the-art techniques in classifying dental diseases using dental panoramic X-ray imaging. This framework presents a promising method for robustly and accurately diagnosing dental diseases automatically, which may help dentists plan treatments and identify dental diseases early on. [ABSTRACT FROM AUTHOR]

ABSTRACT The exponential growth of next‐generation sequencing (NGS) data requires innovative bioinformatics approaches to unravel the genetic underpinnings of diseases. Hemiplegic migraine (HM), a debilitating neurological disorder with a genetic basis, is one such condition that warrants further investigation. Notably, the genetic heterogeneity of HM is underscored by the fact that approximately two‐thirds of patients lack pathogenic variants in the known causal ion channel genes. In this context, we have developed PathVar, a novel bioinformatics algorithm that harnesses publicly available tools and software for pathogenic variant discovery in NGS data. PathVar integrates a suite of tools, including HaplotypeCaller from the Genome Analysis Toolkit (GATK) for variant calling, Variant Effect Predictor (VEP) and ANNOVAR for variant annotation, and TAPES for assigning the American College of Medical Genetics and Genomics (ACMG) pathogenicity labels. Applying PathVar to whole exome sequencing data from 184 HM patients, we detected 648 variants that are probably pathogenic in multiple patients. Moreover, we have identified several candidate genes for HM, many of which cluster around the Rho GTPases pathway. Future research can leverage PathVar to generate high quality, candidate pathogenic variants, which may enhance our understanding of HM and other complex diseases. [ABSTRACT FROM AUTHOR]

This study introduces a novel architecture for value, preference, and boundary alignment in large language models (LLMs) and generative AI systems, accompanied by an experimental implementation. It addresses the limitations in AI model trustworthiness stemming from insufficient comprehension of personal context, preferences, and cultural diversity, which can lead to biases and safety risks. Using an inductive, qualitative research approach, we propose a framework for personalizing AI models to improve model alignment through additional context and boundaries set by users. Our framework incorporates user-friendly tools for identification, annotation, and simulation across diverse contexts, utilizing prompt-driven semantic segmentation and automatic labeling. It aims to streamline scenario generation and personalization processes while providing accessible annotation tools. The study examines various components of this framework, including user interfaces, underlying tools, and system mechanics. We present a pilot study that demonstrates the framework's ability to reduce the complexity of value elicitation and personalization in LLMs. Our experimental setup involves a prototype implementation of key framework modules, including a value elicitation interface and a fine-tuning mechanism for language models. The primary goal is to create a token-based system that allows users to easily impart their values and preferences to AI systems, enhancing model personalization and alignment. This research contributes to the democratization of AI model fine-tuning and dataset generation, advancing efforts in AI value alignment. By focusing on practical implementation and user interaction, our study bridges the gap between theoretical alignment approaches and real-world applications in AI systems. [ABSTRACT FROM AUTHOR]

Bacteria of the genus Pseudomonas are ubiquitous on Earth due to their great metabolic versatility and adaptation to fluctuating environments and different hosts. Some groups are important animal/human and plant pathogens, whereas others are studied for their biotechnological applications, including bioremediation, biological control of phytopathogens and plant growth promotion. Notably, their adaptability is mediated by various signal transduction systems, with the post‐transcriptional Gac‐Rsm cascade playing a key role. This pervasive Pseudomonas pathway controls major transitions at the population level, such as motile/sessile lifestyle, primary/secondary metabolism or replicative/infective behaviour. A hallmark of the Gac‐Rsm cascade is the participation of small, regulatory, non‐coding RNAs of the Rsm clan. These RNAs are synthetised in response to cell‐density‐dependent autoinducer signals channelled through the GacS/GacA two‐component system, and they counteract, by molecular mimicry, the translational control that RNA‐binding proteins of the RsmA family exert over hundreds of mRNAs. Rsm RNAs have been investigated in a few Pseudomonas model species, evidencing the presence of a variable number and families of genes depending on the taxonomic clade. However, the global picture of the distribution of these riboregulators at the genus level was unknown until now. We have undertaken a comprehensive survey and annotation of the vast array of gene sequences encoding members of the Rsm RNA clan in 245 complete genomes that cover 28 phylogenomic clades across the entire genus. The properties of the different families of rsm genes, their phylogenetic radiation, as well as the features of their promoters and adjacent regions, are discussed. The novel insights presented in our manuscript will significantly boost research on the biology of these prevalent RNAs in understudied species of the genus Pseudomonas and closely related genera. [ABSTRACT FROM AUTHOR]

Background: Comprehensive genomic profiling (CGP) can aid the discovery of clinically useful, candidate antitumor agents; however, the variant annotations sometimes differ among the various types of CGP tests as well as the public database. The aim of this study is to clarify the genomic landscape of evaluating detected variants in patients with a malignant solid tumor. Methods: The present, cross-sectional study used data from 57,084 patients with a malignant solid tumor who underwent CGP at the Center for Cancer Genomics and Advanced Therapeutics (C-CAT) between June 1, 2019 and August 18, 2023. The pathogenicity of the variants was annotated using public databases. Results: As a result of re-annotation of the detected variants, 20.1% were pathogenic and 1.4% were benign. The mean number of pathogenic variants was 4.30 (95% confidence interval: 4.27–4.32) per patient. Of the entire cohort, 5.7% had no pathogenic variant. The co-occurrence of the genes depended on the tumor type. Germline findings were detected in 6.2%, 8.8%, and 15.8% of the patients using a tumor/normal panel, tumor-only panel, and liquid panel, respectively, with the most common gene being BRCA2 followed by TP53 and BRCA1. Conclusions: The detected variants should be re-annotated because several benign variants or variants of unknown significance were included in the CGP, and the genomic landscape derived from these results will help researchers and physicians interpret the results of CGP tests. The method of extracting presumptive, germline, pathogenic variants from patients using a tumor-only panel or circulating tumor DNA panel requires improvement. [ABSTRACT FROM AUTHOR]

The Sinocyclocheilus cyphotergous, belonging to the family Cyprinidae, is endemic to the Karst area of the Yunnan-Guizhou Plateau. Here, we determined the complete mitogenome of S. cyphotergous using an Illumina Hiseq X Ten sequencer. This mitogenome's structure is typical circular with 16,611 bp in length, consisting of 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes, and a control region. The overall base composition of S. cyphotergous is 31.33% A, 25.89% T, 26.49% C, and 16.29% G with a slight AT bias of 57.22%. Most mitochondrial genes except ND6 and eight tRNAs were encoded on the heavy strand. All tRNA genes fold into the typical cloverleaf secondary structures, except for tRNA-Ser (AGY) that lacked the dihydrouracil arm. 15 of 22 tRNA genes were found to have 29 G-U mismatches in their secondary structures, which formed a weak bond. In addition, mismatches of A-C, C-C, U-U, and A-A were also found in their tRNA secondary structures. Result of substitution rate estimation among the mitochondrial protein coding genes (PCGs) showed ATP8 had the largest average Ka and Ka/Ks, while COI had the lowest, which implied that ATP8 might evolve more quickly than the other mitochondrial protein coding genes. Phylogenetic analyses based on Bayesian inference (BI) and maximum likelihood (ML) revealed all Sinocyclocheilus species in this research formed a solid monophyletic group and grouped into two major clades with strong support excluded S. jii. Additionally, S. cyphotergous in this study was closely related to S. multipunctatus and S. punctatus. In summary, this study provided novel insights into the phylogeny of the Sinocyclocheilus fishes, conducive to the conservation genetics and cave adaptation for S. cyphotergous. [ABSTRACT FROM AUTHOR]

As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT's applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its comprehensive analysis of the existing literature, addressing a critical gap in understanding ChatGPT's adaptability, limitations, and optimal application. In this paper, we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to direct our search process and seek relevant studies. Our review reveals ChatGPT's significant potential in enhancing various NLP tasks. Its adaptability in information extraction tasks, sentiment analysis, and text classification showcases its ability to comprehend diverse contexts and extract meaningful details. Additionally, ChatGPT's flexibility in annotation tasks reduces manual efforts and accelerates the annotation process, making it a valuable asset in NLP development and research. Furthermore, GPT-4 and prompt engineering emerge as a complementary mechanism, empowering users to guide the model and enhance overall accuracy. Despite its promising potential, challenges persist. The performance of ChatGPT needs to be tested using more extensive datasets and diverse data structures. Subsequently, its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues. [ABSTRACT FROM AUTHOR]

Background: Emotion analysis of social media texts is an innovative method for gaining insight into the mental state of the public and understanding social phenomena. However, emotion is a complex psychological phenomenon, and there are various emotion classification schemes. Which one is suitable for textual emotion analysis? Methods: We proposed a framework for evaluating emotion classification schemes based on manual annotation experiments. Considering both the quality and efficiency of emotion analysis, we identified five criteria, which are solidity, coverage, agreement, compactness, and distinction. Qualitative and quantitative factors were synthesized using the AHP, where quantitative metrics were derived from annotation experiments. Applying this framework, 2848 Sina Weibo posts related to public events were used to evaluate the five emotion schemes: SemEval's four emotions, Ekman's six basic emotions, ancient China's Seven Emotions, Plutchik's eight primary emotions, and GoEmotions' 27 emotions. Results: The AHP evaluation result shows that Ekman's scheme had the highest score. The multi-dimensional scaling (MDS) analysis shows that Ekman, Plutchik, and the Seven Emotions are relatively similar. We analyzed Ekman's six basic emotions in relation to the emotion categories of the other schemes. The correspondence analysis shows that the Seven Emotions' joy aligns with Ekman's happiness, love demonstrates a significant correlation with happiness, but desire is not significantly correlated with any emotion. Compared to Ekman, Plutchik has two more positive emotions: trust and anticipation. Trust is somewhat associated with happiness, but anticipation is weakly associated with happiness. Each emotion of Ekman's corresponds to several similar emotions in GoEmotions. However, some emotions in GoEmotions are not clearly related to Ekman's, such as approval, love, pride, amusement, etc. Conclusion: Ekman's scheme performs best under the evaluation framework. However, it lacks sufficient positive emotion categories for the corpus. [ABSTRACT FROM AUTHOR]

Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption—two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research. [ABSTRACT FROM AUTHOR]

Abstract Dental disorders are common worldwide, causing pain or infections and limiting mouth opening, so dental conditions impact productivity, work capability, and quality of life. Manual detection and classification of oral diseases is time-consuming and requires dentists’ evaluation and examination. The dental disease detection and classification system based on machine learning and deep learning will aid in early dental disease diagnosis. Hence, this paper proposes a new diagnosis system for dental diseases using X-ray imaging. The framework includes a robust pre-processing phase that uses image normalization and adaptive histogram equalization to improve image quality and reduce variation. A dual-stream approach is used for feature extraction, utilizing the advantages of Swin Transformer for capturing long-range dependencies and global context and MobileNetV2 for effective local feature extraction. A thorough representation of dental anomalies is produced by fusing the extracted features. To obtain reliable and broadly applicable classification results, a bagging ensemble classifier is utilized in the end. We evaluate our model on a benchmark dental radiography dataset. The experimental results and comparisons show the superiority of the proposed system with 95.7% for precision, 95.4% for sensitivity, 95.7% for specificity, 95.5% for Dice similarity coefficient, and 95.6% for accuracy. The results demonstrate the effectiveness of our hybrid model integrating MoileNetv2 and Swin Transformer architectures, outperforming state-of-the-art techniques in classifying dental diseases using dental panoramic X-ray imaging. This framework presents a promising method for robustly and accurately diagnosing dental diseases automatically, which may help dentists plan treatments and identify dental diseases early on.

Abstract Variant annotations are crucial for efficient identification of pathogenic variants. In this study, we retrospectively analyzed the utility of four annotation tools (allele frequency, ClinVar, SpliceAI, and Phenomatcher) in identifying 271 pathogenic single nucleotide and small insertion/deletion variants (SNVs/small indels). Although variant filtering based on allele frequency is essential for narrowing down on candidate variants, we found that 13 de novo pathogenic variants in autosomal dominant or X-linked dominant genes are registered in gnomADv4.0 or 54KJPN, with an allele frequency of less than 0.001%, suggesting that very rare variants in large cohort data can be pathogenic de novo variants. Notably, 38.4% candidate SNVs/small indels are registered in the ClinVar database as pathogenic or likely pathogenic, which highlights the significance of this database. SpliceAI can detect candidate variants affecting RNA splicing, leading to the identification of four variants located 11 to 50 bp away from the exon–intron boundary. Prioritization of candidate genes by proband phenotype using the PhenoMatcher module revealed that approximately 95% of the candidate genes had a maximum PhenoMatch score ≥ 0.6, suggesting the utility of phenotype-based variant prioritization. Our results suggest that a combination of multiple annotation tools and appropriate evaluation can improve the diagnosis of rare diseases.

Abstract This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs). We focus our analysis on biases present in some of the most commonly used visual datasets for training person and vehicle detection systems. We introduce an annotation methodology and a specialised annotation tool, both designed to annotate protected attributes of agents in visual datasets. We validate our methodology through an inter-rater agreement analysis and provide the distribution of attributes across all datasets. These include annotations for the attributes age, sex, skin tone, group, and means of transport for more than 90K people, as well as vehicle type, colour, and car type for over 50K vehicles. Generally, diversity is very low for most attributes, with some groups, such as children, wheelchair users, or personal mobility vehicle users, being extremely underrepresented in the analysed datasets. The study contributes significantly to efforts to consider fairness in the evaluation of perception and prediction systems for AVs. This paper follows reproducibility principles. The annotation tool, scripts and the annotated attributes can be accessed publicly at https://github.com/ec-jrc/humaint_annotator .

Abstract Background Emotion analysis of social media texts is an innovative method for gaining insight into the mental state of the public and understanding social phenomena. However, emotion is a complex psychological phenomenon, and there are various emotion classification schemes. Which one is suitable for textual emotion analysis? Methods We proposed a framework for evaluating emotion classification schemes based on manual annotation experiments. Considering both the quality and efficiency of emotion analysis, we identified five criteria, which are solidity, coverage, agreement, compactness, and distinction. Qualitative and quantitative factors were synthesized using the AHP, where quantitative metrics were derived from annotation experiments. Applying this framework, 2848 Sina Weibo posts related to public events were used to evaluate the five emotion schemes: SemEval’s four emotions, Ekman’s six basic emotions, ancient China’s Seven Emotions, Plutchik’s eight primary emotions, and GoEmotions’ 27 emotions. Results The AHP evaluation result shows that Ekman’s scheme had the highest score. The multi-dimensional scaling (MDS) analysis shows that Ekman, Plutchik, and the Seven Emotions are relatively similar. We analyzed Ekman’s six basic emotions in relation to the emotion categories of the other schemes. The correspondence analysis shows that the Seven Emotions’ joy aligns with Ekman’s happiness, love demonstrates a significant correlation with happiness, but desire is not significantly correlated with any emotion. Compared to Ekman, Plutchik has two more positive emotions: trust and anticipation. Trust is somewhat associated with happiness, but anticipation is weakly associated with happiness. Each emotion of Ekman’s corresponds to several similar emotions in GoEmotions. However, some emotions in GoEmotions are not clearly related to Ekman’s, such as approval, love, pride, amusement, etc. Conclusion Ekman’s scheme performs best under the evaluation framework. However, it lacks sufficient positive emotion categories for the corpus.

Abstract Objectives Ants are ecologically dominant insects in most terrestrial ecosystems, with more than 14,000 extant species in about 340 genera recorded to date. However, genomic resources are still scarce for most species, especially for species endemic in East or Southeast Asia, limiting the study of phylogeny, speciation and adaptation of this evolutionarily successful animal lineage. Here, we assemble and annotate the genomes of Odontoponera transversa and Camponotus friedae, two ant species with a natural distribution in China, to facilitate future study of ant evolution. Data description We obtained a total of 16 Gb and 51 Gb PacBio HiFi data for O. transversa and C. friedae, respectively, which were assembled into the draft genomes of 339 Mb for O. transversa and 233 Mb for C. friedae. Genome assessments by multiple metrics showed good completeness and high accuracy of the two assemblies. Gene annotations assisted by RNA-seq data yielded a comparable number of protein-coding genes in the two genomes (10,892 for O. transversa and 11,296 for C. friedae), while repeat annotations revealed a remarkable difference of repeat content between these two ant species (149.4 Mb for O. transversa versus 49.7 Mb for C. friedae). Besides, complete mitochondrial genomes for the two species were assembled and annotated.

Background: As the number of households raising companion dogs increases, the pet genetic analysis market also continues to grow. However, most studies have focused on specific purposes or native breeds. This study aimed to collect genomic data through single nucleotide polymorphism (SNP) chip analysis of companion dogs in South Korea and perform genetic diversity analysis and SNP annotation. Methods: We collected samples from 95 dogs belonging to 26 breeds, including mixed breeds, in South Korea. The SNP genotypes were obtained for each sample using an Axiom™ Canine HD Array. Quality control (QC) was performed to enhance the accuracy of the analysis. A genetic diversity analysis was performed for each SNP. Results: QC initially selected SNPs, and after excluding non-diverse ones, 621,672 SNPs were identified. Genetic diversity analysis revealed minor allele frequencies, polymorphism information content, expected heterozygosity, and observed heterozygosity values of 0.220, 0.244, 0.301, and 0.261, respectively. The SNP annotation indicated that most variations had an uncertain or minimal impact on gene function. However, approximately 16,000 non-synonymous SNPs (nsSNPs) have been found to significantly alter gene function or affect exons by changing translated amino acids. Conclusions: This study obtained data on SNP genetic diversity and functional SNPs in companion dogs raised in South Korea. The results suggest that establishing an SNP set for individual identification could enable a gene-based registration system. Furthermore, identifying and researching nsSNPs related to behavior and diseases could improve dog care and prevent abandonment.

Abstract Giardia duodenalis, a major cause of waterborne infection, infects a wide range of mammalian hosts and is subdivided into eight genetically well-defined assemblages named A through H. However, fragmented genomes and a lack of comparative analysis within and between the assemblages render unclear the molecular mechanisms controlling host specificity and differential disease outcomes. To address this, we generated a near-complete de novo genome of AI assemblage using the Oxford Nanopore platform by sequencing the Be-2 genome. We generated 148,144 long-reads with quality scores of > 7. The final genome assembly consists of only nine contigs with an N50 of 3,045,186 bp. This assembly agrees closely with the assembly of another strain in the AI assemblage (WB-C6). However, a critical difference is that a region previously placed in the five-prime region of Chr5 belongs to Chr4 of Be-2. We find a high degree of conservation in the ploidy, homozygosity, and the presence of cysteine-rich variant-specific surface proteins (VSPs) within the AI assemblage. Our assembly provides a nearly complete genome of a member of the AI assemblage of G. duodenalis, aiding population genomic studies capable of elucidating Giardia transmission, host range, and pathogenicity.

The detection of toxic language in the Arabic language has emerged as an active area of research in recent years, and reviewing the existing datasets employed for training the developed solutions has become a pressing need. This paper offers a comprehensive survey of Arabic datasets focused on online toxic language. We systematically gathered a total of 54 available datasets and their corresponding papers and conducted a thorough analysis, considering 18 criteria across four primary dimensions: availability details, content, annotation process, and reusability. This analysis enabled us to identify existing gaps and make recommendations for future research works. For the convenience of the research community, the list of the analysed datasets is maintained in a GitHub repository. [ABSTRACT FROM AUTHOR]

Introduction: Communication is fundamental to effective surgical coaching. This can be challenging for training during image-guided procedures where coaches and trainees need to articulate technical details on a monitor. Telestration devices that annotate on monitors remotely could potentially overcome these limitations and enhance the coaching experience. This study aims to evaluate the value of a novel telestration device in surgical coaching. Methods: A randomized-controlled trial was designed. All participants watched a video demonstrating the task followed by a baseline performance assessment and randomization into either control group (conventional verbal coaching without telestration) or telestration group (verbal coaching with telestration). Coaching for a simulated laparoscopic small bowel anastomosis on a dry lab model was done by a faculty surgeon. Following the coaching session, participants underwent a post-coaching performance assessment of the same task. Assessments were recorded and rated by blinded reviewers using a modified Global Rating Scale of the Objective Structured Assessment of Technical Skills (OSATS). Coaching sessions were also recorded and compared in terms of mentoring moments; guidance misinterpretations, questions/clarifications by trainees, and task completion time. A 5-point Likert scale was administered to obtain feedback. Results: Twenty-four residents participated (control group 13, telestration group 11). Improvements in some elements of the OSATS scale were noted in the Telestration arm but there was no statistical significance in the overall score between the two groups. Mentoring moments were more in the telestration Group. Amongst the telestration Group, 55% felt comfortable that they could perform this task independently, compared to only 8% amongst the control group and 82% would recommend the use of telestration tools here. Conclusion: There is demonstrated educational value of this novel telestration device mainly in the non-technical aspects of the interaction by enhancing the coaching experience with improvement in communication and greater mentoring moments between coach and trainee. [ABSTRACT FROM AUTHOR]

Early skin cancer diagnosis can save lives; however, traditional methods rely on expert knowledge and can be time-consuming. This calls for automated systems using machine learning and deep learning. However, existing datasets often focus on flat skin surfaces, neglecting more complex cases on organs or with nearby lesions. This work addresses this gap by proposing a skin cancer diagnosis methodology using a dataset named ASAN that covers diverse skin cancer cases but suffers from noisy features. To overcome the noisy feature problem, a segmentation dataset named SASAN is introduced, focusing on Region of Interest (ROI) extraction-based classification. This allows models to concentrate on critical areas within the images while ignoring learning the noisy features. Various deep learning segmentation models such as UNet, LinkNet, PSPNet, and FPN were trained on the SASAN dataset to perform segmentation-based ROI extraction. Classification was then performed using the dataset with and without ROI extraction. The results demonstrate that ROI extraction significantly improves the performance of these models in classification. This implies that SASAN is effective in evaluating performance metrics for complex skin cancer cases. This study highlights the importance of expanding datasets to include challenging scenarios and developing better segmentation methods to enhance automated skin cancer diagnosis. The SASAN dataset serves as a valuable tool for researchers aiming to improve such systems and ultimately contribute to better diagnostic outcomes. [ABSTRACT FROM AUTHOR]

Data labeling systems are designed to facilitate the training and validation of machine learning algorithms under the umbrella of crowdsourcing practices. The current paper presents a novel approach for designing a customized data labeling system, emphasizing two key aspects: an innovative payment mechanism for users and an efficient configuration of output results. The main problem addressed is the labeling of datasets where golden items are utilized to verify user performance and assure the quality of the annotated outputs. Our proposed payment mechanism is enhanced through a modified skip-based golden-oriented function that balances user penalties and prevents spam activities. Additionally, we introduce a comprehensive reporting framework to measure aggregated results and accuracy levels, ensuring the reliability of the labeling output. Our findings indicate that the proposed solutions are pivotal in incentivizing user participation, thereby reinforcing the applicability and profitability of newly launched labeling systems. [ABSTRACT FROM AUTHOR]

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Purpose: The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience. Methods: We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images. Twelve radiologists with different years of experience independently annotated the lesions, and the performance changes were investigated by repeating the retraining of the CAD software twice, with the addition of cases annotated by each radiologist. Additionally, we investigated the effects of retraining using integrated annotations from multiple radiologists. Results: The performance of the CAD software after retraining differed among annotating radiologists. In some cases, the performance was degraded compared to that of the initial software. Retraining using integrated annotations showed different performance trends depending on the target CAD software, notably in cerebral aneurysm detection, where the performance decreased compared to using annotations from a single radiologist. Conclusions: Although the performance of the CAD software after retraining varied among the annotating radiologists, no direct correlation with their experience was found. The performance trends differed according to the type of CAD software used when integrated annotations from multiple radiologists were used. [ABSTRACT FROM AUTHOR]

Roads are one of the most critical infrastructures, which should be maintained at a high quality of service. For this purpose, road pavement should be assessed cost-effectively. In the past, image processing methods were used to analyze pavement conditions. In recent years, machine learning methods have been employed, while now deep learning methods are applied. Deep learning has outperformed other methods regarding the accuracy and speed of pavement distress evaluation. In this research, a deep learning algorithm called YOLOv5 is deployed to detect pavement block cracking and estimate its severity using images taken from the right of way via a road surface profiler. Two models are successfully trained and tested, one to detect block cracking and the other to predict its severity with a sufficient level of accuracy of 84.5% and 76.6%, respectively. It is concluded that the model not only can detect block cracking but also predict its severity. [ABSTRACT FROM AUTHOR]

The basal splits within Syndermata probably date back hundreds of millions of years, and extant syndermatans greatly differ in reproduction modes (parthenogenesis, metagenesis, heterosexuality) and lifestyles (free-living, epizoic, endoparasitic). Against this background, the present investigation aims to reconstruct genome and transcriptome metrics in the last common ancestor (LCA) of Syndermata. We especially annotated nuclear genome assemblies of one representative, each, of monogononts (Brachionus plicatilis), bdelloids (Adineta vaga), seisonids (Seison nebaliae), and acanthocephalans (Pomphorhynchus laevis), S1 and additionally considered previously published data. Present results suggest that the syndermatan LCA possessed a compact nuclear genome of 50–100 Mb with a coding portion of ≥ 40%. Its ~ 17,000 protein-coding genes probably had about 4–6 exons and 3–5 introns on average. Long interspersed nuclear elements (LINEs) should have occupied 4–5% of the ancestral genome. Based on this, independent evolutionary trajectories involving gene loss, genome duplication, and repeat proliferation resulted in greatly varying genomes within the clade. Nevertheless, we identified 2114 clusters containing proteins of all four syndermatan taxa. About 200 of these clusters lacked sufficiently similar counterparts in the platyhelminths Macrostomum lignano and Schmidtea mediterranea. Gene ontologies assigned to these clusters might reference the muscular pharynx, light perception, and transposition, amongst others. [ABSTRACT FROM AUTHOR]

Objectives: Ants are ecologically dominant insects in most terrestrial ecosystems, with more than 14,000 extant species in about 340 genera recorded to date. However, genomic resources are still scarce for most species, especially for species endemic in East or Southeast Asia, limiting the study of phylogeny, speciation and adaptation of this evolutionarily successful animal lineage. Here, we assemble and annotate the genomes of Odontoponera transversa and Camponotus friedae, two ant species with a natural distribution in China, to facilitate future study of ant evolution. Data description: We obtained a total of 16 Gb and 51 Gb PacBio HiFi data for O. transversa and C. friedae, respectively, which were assembled into the draft genomes of 339 Mb for O. transversa and 233 Mb for C. friedae. Genome assessments by multiple metrics showed good completeness and high accuracy of the two assemblies. Gene annotations assisted by RNA-seq data yielded a comparable number of protein-coding genes in the two genomes (10,892 for O. transversa and 11,296 for C. friedae), while repeat annotations revealed a remarkable difference of repeat content between these two ant species (149.4 Mb for O. transversa versus 49.7 Mb for C. friedae). Besides, complete mitochondrial genomes for the two species were assembled and annotated. [ABSTRACT FROM AUTHOR]

This study describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" (https://github.com/NIGMS/NIGMS-Sandbox). The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on de novo transcriptome assembly using Nextflow in an interactive format that uses appropriate cloud resources for data access and analysis. Cloud computing is a powerful new means by which biomedical researchers can access resources and capacity that were previously either unattainable or prohibitively expensive. To take advantage of these resources, however, the biomedical research community needs new skills and knowledge. We present here a cloud-based training module, developed in conjunction with Google Cloud, Deloitte Consulting, and the NIH STRIDES Program, that uses the biological problem of de novo transcriptome assembly to demonstrate and teach the concepts of computational workflows (using Nextflow) and cost- and resource-efficient use of Cloud services (using Google Cloud Platform). Our work highlights the reduced necessity of on-site computing resources and the accessibility of cloud-based infrastructure for bioinformatics applications. [ABSTRACT FROM AUTHOR]

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Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Despite its empirical vocation, RE research has given limited attention to replication of NLP for RE studies. Replication is hampered by several factors, including the context specificity of the studies, the heterogeneity of the tasks involving NLP, the tasks' inherent hairiness, and, in turn, the heterogeneous reporting structure. To address these issues, we propose a new artifact, referred to as ID-Card, whose goal is to provide a structured summary of research papers emphasizing replication-relevant information. We construct the ID-Card through a structured, iterative process based on design science. In this article: (i) we report on hands-on experiences of replication; (ii) we review the state-of-the-art and extract replication-relevant information: (iii) we identify, through focus groups, challenges across two typical dimensions of replication: data annotation and tool reconstruction; and (iv) we present the concept and structure of the ID-Card to mitigate the identified challenges. This study aims to create awareness of replication in NLP for RE. We propose an ID-Card that is intended to foster study replication but can also be used in other contexts, e.g., for educational purposes. [ABSTRACT FROM AUTHOR]

Emotion recognition systems are typically trained to classify a given psychophysiological state into emotion categories. Current platforms for emotion ground-truth collection show limitations for real-world scenarios of long-duration content (e.g. $> $ > 10 minutes), namely: 1) Real-time annotation tools are distracting and become exhausting; 2) Perform retrospective annotation of the whole content in bulk (providing highly coarse annotations); or 3) Are used by external experts (depending on the number of annotators and their subjective experience). We explore a novel approach, the EmotiphAI Annotator, that allows undisturbed content visualisation and simplifies the annotation process by using segmentation algorithms that select brief clips for emotional annotation retrospectively. We compare three methods for content segmentation based on physiological data (Electrodermal Activity (EDA), emotion-based), scene (time-based), and random (control) selection. The EmotiphAI Annotator attained a B+ System Usability Scale score and low-average mental workload as per the NASA Task Load Index (40%). The reliability of the self-report was analysed by the inter-rater agreement (STD $< $ < 0.75), coherence across time segmentation methods (STD $< $ < 0.17), comparison against the state-of-the-art ground truth (STD $< $ < 0.7), and correlation to EDA ($> $ > 0.3 to 0.8), where the EDA-based method obtained the overall best performance. [ABSTRACT FROM AUTHOR]

Annotation tools serve a critical role in the generation of datasets that fuel machine learning applications. With the advent of Foundation Models, particularly those based on Transformer architectures and expansive language models, the capacity for training on comprehensive, multimodal datasets has been substantially enhanced. This not only facilitates robust generalization across diverse data categories and knowledge domains but also necessitates a novel form of annotation—prompt engineering—for qualitative model fine-tuning. This advancement creates new avenues for machine intelligence to more precisely identify, forecast, and replicate human behavior, addressing historical limitations that contribute to algorithmic inequities. Nevertheless, the voluminous and intricate nature of the data essential for training multimodal models poses significant engineering challenges, particularly with regard to bias. No consensus has yet emerged on optimal procedures for conducting this annotation work in a manner that is ethically responsible, secure, and efficient. This historical literature review traces advancements in these technologies from 2018 onward, underscores significant contributions, and identifies existing knowledge gaps and avenues for future research pertinent to the development of Transformer-based multimodal Foundation Models. An initial survey of over 724 articles yielded 156 studies that met the criteria for historical analysis; these were further narrowed down to 46 key papers spanning the years 2018–2022. The review offers valuable perspectives on the evolution of best practices, pinpoints current knowledge deficiencies, and suggests potential directions for future research. The paper includes six figures and delves into the transformation of research landscapes in the realm of machine-assisted behavioral annotation, focusing on critical issues such as bias. [ABSTRACT FROM AUTHOR]

Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes. [ABSTRACT FROM AUTHOR]

Acoustic signals, especially those of biological source, remain unexplored in the Belgian part of the North Sea (BPNS). The BPNS, although dominated by anthrophony (sounds from human activities), is expected to be acoustically diverse given the presence of biodiverse sandbanks, gravel beds and artificial hard structures. Under the framework of the LifeWatch Broadband Acoustic Network, sound data have been collected since the spring of 2020. These recordings, encompassing both biophony, geophony and anthrophony, have been listened to and annotated for unknown, acoustically salient sounds. To obtain the acoustic features of these annotations, we used two existing automatic feature extractions: the Animal Vocalization Encoder based on Self-Supervision (AVES) and a convolutional autoencoder network (CAE) retrained on the data from this study. An unsupervised density-based clustering algorithm (HDBSCAN) was applied to predict clusters. We coded a grid search function to reduce the dimensionality of the feature sets and to adjust the hyperparameters of HDBSCAN. We searched the hyperparameter space for the most optimized combination of parameter values based on two selected clustering evaluation measures: the homogeneity and the density-based clustering validation (DBCV) scores. Although both feature sets produced meaningful clusters, AVES feature sets resulted in more solid, homogeneous clusters with relatively lower intracluster distances, appearing to be more advantageous for the purpose and dataset of this study. The 26 final clusters we obtained were revised by a bioacoustics expert. We were able to name and describe 10 unique sounds, but only clusters named as 'Jackhammer' and 'Tick' can be interpreted as biological with certainty. Although unsupervised clustering is conventional in ecological research, we highlight its practical use in revising clusters of annotated unknown sounds. The revised clusters we detailed in this study already define a few groups of distinct and recurring sounds that could serve as a preliminary component of a valid annotated training dataset potentially feeding supervised machine learning and classifier models. [ABSTRACT FROM AUTHOR]

In this study, the genome of an endemic Sulawesi's plant, Medinilla tapetemagicum was sequenced using Illumina NextSeq 500 and assembled the whole chloroplast genome. Results showed that the cpGenome is 155,602 bp in size with typical quadripartite structure of a large single copy (LSC) region (85,409 bp), a short single copy (SSC) region (16,629 bp), and a pair of inverted repeats (IRs) regions (26,782 bp). The cpGenome is composed of 132 genes, which consists of 87 protein coding genes, 37 tRNAs, and 8 rRNAs. The sliding window analyses showed that psbB-psbH and ndhF-rpl32 can potentially be used as markers. Microsatellite motifs of mononucleotide A and T dominated in the cpGenome. The phylogenetic trees from the concatenated 76 shared protein coding gene sequences showed the Medinilla clade was monophyletic and M. tapete-magicum is a sister species in the SE Asian clade which contain M. magnifica and M. speciosa. [ABSTRACT FROM AUTHOR]

Here, we describe a high-quality genome assembly and annotation of the convergent lady beetle, Hippodamia convergens (Coleoptera: Coccinellidae). The highest quality unmasked genome comprises 619 megabases (Mb) of chromosomal DNA, organized into 899 contigs, with a contig N50 score of 89 Mbps. The genome was assessed to be 96 % complete (BUSCO). Reconstruction of a whole-genome phylogeny resolved H. convergens as sister to the Harlequin lady beetle, Harmonia axyridis , and nested within a clade of several known agricultural pests. [ABSTRACT FROM AUTHOR]

Introduction: Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) provide valuable insights into the cellular states of kidney cells. However, the annotation of cell types often requires extensive domain expertise and time-consuming manual curation, limiting scalability and generalizability. To facilitate this process, we tested the performance of five supervised classification methods for automatic cell type annotation. Results: We analyzed publicly available sc/snRNA-seq datasets from five expert-annotated studies, comprising 62,120 cells from 79 kidney biopsy samples. Datasets were integrated by harmonizing cell type annotations across studies. Five different supervised machine learning algorithms (support vector machines, random forests, multilayer perceptrons, k-nearest neighbors, and extreme gradient boosting) were applied to automatically annotate cell types using four training datasets and one testing dataset. Performance metrics, including accuracy (F1 score) and rejection rates, were evaluated. All five machine learning algorithms demonstrated high accuracies, with a median F1 score of 0.94 and a median rejection rate of 1.8 %. The algorithms performed equally well across different datasets and successfully rejected cell types that were not present in the training data. However, F1 scores were lower when models trained primarily on scRNA-seq data were tested on snRNA-seq data. Conclusions: Despite limitations including the number of biopsy samples, our findings demonstrate that machine learning algorithms can accurately annotate a wide range of adult kidney cell types in scRNA-seq/snRNA-seq data. This approach has the potential to standardize cell type annotation and facilitate further research on cellular mechanisms underlying kidney disease.

Current approaches to biosignal handling in a medical environment increasingly rely on machine learning, which requires precise annotations. In multi-centric annotation scenarios, the diversity in annotation protocols leads to imprecise semantics of the annotations. This context shows the need for interoperable annotations, that can be reused with little cost across different protocols. The presented free and open-source software is an application for handling biosignals. It implements a novel method to add asynchronous and interoperable annotations with precise semantics. The annotations are stored in Allotrope Data Format (ADF), a structure based on the Hierarchical Data Format (HDF5). It allows for high performance I/O and aggregating of related patient data (e.g. different acquisition modalities). This research tool can be used solely in a web browser to facilitate integration in more complete environments.

In the sentiment analysis research process, there are problems when still using manual labeling methods by humans (expert annotation), which are related to subjectivity, long time, and expensive costs. Another way is to use computer assistance (machine annotator). However, the use of machine annotators also has the research problem of not being able to detect sarcastic sentences. Thus, the researcher proposed a sentiment labeling method using Semi-Supervised Learning. Semi-supervised learning is a labeling method that combines human labeling techniques (expert annotation) and machine labeling (machine annotation). This research uses machine annotators in the form of Deep Learning algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The word weighting method used in this research is Word2Vec Continuous Bag of Word (CBoW). The results showed that the GRU algorithm tends to have a better accuracy rate than the LSTM algorithm. The average accuracy of the training results of the LSTM and GRU algorithm models is 0.904 and 0.913. In contrast, the average accuracy of labeling by LSTM and GRU is 0.569 and 0.592, respectively.

Background: The cabbage webworm, Hellula undalis (Fabricius) (Lepidoptera: Pyralidae), is a significant pest of brassicas and other cruciferous plants in warm regions worldwide. Transcriptome analysis is valuable for investigation of molecular mechanisms underlying the insect development and reproduction. De novo assembly is particularly useful for acquiring complete transcriptome information of insect species when there is no reference genome available. In case of Hellula undalis, only 17 nucleotide records are currently available throughout NCBI nucleotide database. Genes associated with metabolic processes, general development, reproduction, defense and functional genomics were not previously predicted in the Hellula undalis at the genomic level. Methods & Results: To address this issue, we constructed Hellula undalis transcriptome using Illumina NovaSeq6000 technology. Approximately 48 million 150 bp paired-end reads were obtained from sequencing. A total of 30,451 contigs were generated by de novo assembly of sample and were compared with the sequences in the NCBI non-redundant protein database (Nr). In total, 71 % of contigs were matched to known proteins in public databases including Nr, Gene Ontology (GO), and Cluster Orthologous Gene Database (COG), and then, contigs were mapped to 123 via functional annotation against the Kyoto Encyclopedia of Genes and Genomes pathway database (KEGG). In addition, we compared the ortholog gene family of the Hullula undalis, transcriptome to Spodoptera frugiperda, spodotera litura and spodoptera littoralis and found that 391 orthologous gene families are specific to Hullula undalis. A total of 1,913 potential SSRs was discovered in Hullula undalis contigs. Conclusions: This study is the first transcriptome data for Hullula undalis. Additionally, it serves as a valuable resource for identifying target genes and developing effective and environmentally friendly strategies for pest control.