Sequence Annotation Research Articles

Annotating otoliths with a deep generative model

Abstract Otoliths are a central information source for fish ecology and stock management, conveying important data about age and other life history for individual fish. Traditionally, interpretation of otoliths has required skilled expert readers, but recently deep learning classification and regression models have been trained to extract fish age from images of otoliths from a variety of species. Despite high accuracy in many cases, the adoption of such models in fisheries management has been slow. One reason may be that the underlying mechanisms the model uses to derive its results from the data are opaque, and this lack of legibility makes it challenging to build sufficient trust in the results. Here, we implement a deep learning model that instead of age predicts the location of annotation marks for each of the annuli. This allows an expert to evaluate the model’s performance in detail. The quality of the annotations was judged by a panel of four expert otolith readers in a double-blinded randomized survey. Using a scale from 1 to 5, the generated marks received an average quality score of 4.22, whereas expert annotations received an average score of 4.33. By counting the marks to determine fish age, we obtained an agreement between expert and model annotations of 64% on our test set, which running the model stochastically increased to 69%. Stochastic sampling yields further benefits, including an explicit measure of the model’s uncertainty, the post hoc likelihood of the different age classes for each otolith, and a set of alternative annotation sequences that highlight the structure of the annuli.

Plastic bio-mitigation by Pseudomonas mendocina ABF786 and simultaneous conversion of its CO2 byproduct to microalgal biodiesel

Bio-mitigation of plastics by microorganisms generates carbon dioxide (CO2) that can be utilized for algal biomass generation. Pseudomonas mendocina ABF786, reportedly the most efficient plastic-degrading bacteria, was screened using the modified most probable number technique. This study highlights the use of an integrative prototype for the production of microalgal biomass (Chlorella vulgaris) in combination with bio-mitigation of plastics, which serves a dual purpose: (i) increased plastic-degradation capability by microorganisms (53%–85% increase in plastic weight loss) due to removal of CO2 feedback inhibition and (ii) increased algal biomass generation (200%–237%) due to supply of extra CO2 from plastic degradation to the algal cultivation flask. Whole-genome sequencing and functional annotation confirmed that all the genes involved in the mineralization of plastic to CO2 are present within the genome of P. mendocina ABF786. Using two or more microbial cultures for remediation may increase the process efficiency.

Metagenomic insights into microbial contamination in critical healthcare environments and the efficacy of a novel "HLE" disinfectant.

Bacterial contamination on inanimate clinical surfaces is directly linked to severe health problems, especially those caused by multidrug resistant (MDR) pathogens. Here, we evaluated the microbial burden in these environments and tested the efficacy of a novel HLE disinfectant solution. Microbial contamination of healthcare surfaces [Intensive Care Unit (ICU), Long Period Hospitalization Room (LPHR) and Otolaryngology Consultation (OC)] and the efficacy of HLE disinfectant solution were determined analyzing the viable counts on general and selective media, and also by molecular studies focused on metagenomic and specific qPCR. Different contamination loads were detected with LPHR showing the highest contamination. Treatment with the HLE disinfectant solution curbed the spread of well-adapted pathogens on touched surfaces (ICU, LPHR and OC). Metagenomic analysis of microbial diversity of the Patient Table (most contaminated surface in LPHR) revealed the presence of mainly A. johnsonii and P. putida. Furthermore, functional annotation of toxin, virulence and antibiotic resistance sequences showed a high diversity of Acinetobacter spp. and Pseudomonas spp. In this context, specific qPCR analysis confirmed the efficacy of HLE disinfectant solution against the most prevalent and critical pathogens Pseudomonas sp. and Acinetobacter sp. achieving their complete eradication. Given the persistence of detrimental resistant pathogens, the application of HLE disinfection solution could be a highly beneficial and effective option -used either alone or in combination-for infection prevention and control with the aim to eliminate microbial pathogens and their genes from contaminated contact-surfaces and thus limit the spread to humans and other ecological niches.

RVdb: a comprehensive resource and analysis platform for rhinovirus research.

Rhinovirus (RV), a prominent causative agent of both upper and lower respiratory diseases, ranks among the most prevalent human respiratory viruses. RV infections are associated with various illnesses, including colds, asthma exacerbations, croupand pneumonia, imposing significant and extended societal burdens. Characterized by a high mutation rate and genomic diversity, RV displays a diverse serological landscape, encompassing a total of 174 serotypes identified to date. Understanding RV genetic diversity is crucial for epidemiological surveillance and investigation of respiratory diseases. This study introduces a comprehensive and high-quality RV data resource, designated RVdb (http://rvdb.mgc.ac.cn), covering 26909 currently identified RV strains, along with RV-related sequences, 3D protein structuresand publications. Furthermore, this resource features a suite of web-based utilities optimized for easy browsing and searching, as well as automatic sequence annotation, multiple sequence alignment (MSA), phylogenetic tree construction, RVdb BLAST and a serotyping pipeline. Equipped with a user-friendly interface and integrated online bioinformatics tools, RVdb provides a convenient and powerful platform on which to analyse the genetic characteristics of RVs. Additionally, RVdb also supports the efforts of virologists and epidemiologists to monitor and trace both existing and emerging RV-related infectious conditions in a public health context.

PlantPAN 4.0: updated database for identifying conserved non-coding sequences and exploring dynamic transcriptional regulation in plant promoters.

PlantPAN 4.0 (http://PlantPAN.itps.ncku.edu.tw/) is an integrative resource for constructing transcriptional regulatory networks for diverse plant species. In this release, the gene annotation and promoter sequences were expanded to cover 115 species. PlantPAN 4.0 can help users characterize the evolutionary differences and similarities among cis-regulatory elements; furthermore, this system can now help in identification of conserved non-coding sequences among homologous genes. The updated transcription factor binding site repository contains 3428 nonredundant matrices for 18305 transcription factors; this expansion helps in exploration of combinational and nucleotide variants of cis-regulatory elements in conserved non-coding sequences. Additionally, the genomic landscapes of regulatory factors were manually updated, and ChIP-seq data sets derived from a single-cell green alga (Chlamydomonas reinhardtii) were added. Furthermore, the statistical review and graphical analysis components were improved to offer intelligible information through ChIP-seq data analysis. These improvements included easy-to-read experimental condition clusters, searchable gene-centered interfaces for the identification of promoter regions' binding preferences by considering experimental condition clusters and peak visualization for all regulatory factors, and the 20 most significantly enriched gene ontology functions for regulatory factors. Thus, PlantPAN 4.0 can effectively reconstruct gene regulatory networks and help compare genomic cis-regulatory elements across plant species and experiments.

Discovery of a Novel Intron in US10/US11/US12 of HSV-1 Strain 17.

Herpes Simplex Virus type 1 (HSV-1) infects humans and causes a variety of clinical manifestations. Many HSV-1 genomes have been sequenced with high-throughput sequencing technologies and the annotation of these genome sequences heavily relies on the known genes in reference strains. Consequently, the accuracy of reference strain annotation is critical for future research and treatment of HSV-1 infection. In this study, we analyzed RNA-Seq data of HSV-1 from NCBI databases and discovered a novel intron in the overlapping coding sequence (CDS) of US10 and US11, and the 3' UTR of US12 in strain 17, a commonly used HSV-1 reference strain. To comprehensively understand the shared US10/US11/US12 intron structure, we used US11 as a representative and surveyed all US11 gene sequences from the NCBI nt/nr database. A total of 193 high-quality US11 sequences were obtained, of which 186 sequences have a domain of uninterrupted tandemly repeated RXP (Arg-X-Pro) in the C-terminus half of the protein. In total, 97 of the 186 sequences encode US11 protein with the same length of the mature US11 in strain 17:26 of them have the same structure of US11 and can be spliced as in strain 17; 71 of them have transcripts that are the same as mature US11 mRNA in strain 17. In total, 76 US11 gene sequences have either canonical or known noncanonical intron border sequences and may be spliced like strain 17 and obtain mature US11 CDS with the same length. If not spliced, they will have extra RXP repeats. A tandemly repeated RXP domain was proposed to be essential for US11 to bind with RNA and other host factors. US10 protein sequences from the same strains have also been studied. The results of this study show that even a frequently used reference organism may have errors in widely used databases. This study provides accurate annotation of the US10, US11, and US12 gene structure, which will build a more solid foundation to study expression regulation of the function of these genes.

Isolation and genome sequence annotation of KillerTomato, a newly discovered cluster EE bacteriophage infecting Microbacterium.

Discovered in Pocatello, Idaho, soil near a tomato garden, siphovirus KillerTomato infects Microbacterium foliorum NRRL B-24224. KillerTomato is a lytic cluster EE phage with a 17,442-bp genome and 68.6% GC content. Of 25 genes, 20 were assigned putative functions, including a putative tail assembly chaperone protein with a programmed frameshift and an endolysin.

Biochemical and molecular characterization of the SBiP1 chaperone from Symbiodinium microadriaticum CassKB8 and light parameters that modulate its phosphorylation.

The coding and promoter region sequences from the BiP-like protein SBiP1 from Symbiodinium microadriaticum CassKB8 were obtained by PCR, sequenced and compared with annotated sequences. The nucleotides corresponding to the full sequence were correctly annotated and the main SBiP1 features determined at the nucleotide and amino acid level. The translated protein was organized into the typical domains of the BiP/HSP70 family including a signal peptide, a substrate- and a nucleotide-binding domain, and an ER localization sequence. Conserved motifs included a highly conserved Thr513 phosphorylation site and two ADP-ribosylation sites from eukaryotic BiP's. Molecular modeling showed the corresponding domain regions and main exposed post-translational target sites in its three-dimensional structure, which also closely matched Homo sapiens BiP further indicating that it indeed corresponds to a BiP/HSP70 family protein. The gene promoter region showed at least eight light regulation-related sequences consistent with the molecule being highly phosphorylated in Thr under dark conditions and dephosphorylated upon light stimuli. We tested light parameter variations that could modulate the light mediated phosphorylation effect and found that SBiP1 Thr dephosphorylation was only significantly detected after 15-30 min light stimulation. Such light-induced dephosphorylation was observed even when dichlorophenyl dimethyl urea, a photosynthesis inhibitor, was also present in the cells during the light stimulation. Dephosphorylation occurred indistinctly under red, yellow, blue or the full visible light spectra. In additon, it was observed at a light intensity of as low as 1 μmole photon m-2 s-1. Our results indicate that: a) SBiP1 is a chaperone belonging to the BiP/HSP70 family proteins; b) its light-modulated phosphorylation/dephosphorylation most likely functions as an activity switch for the chaperone; c) this light-induced modulation occurs relatively slow but is highly sensitive to the full spectrum of visible light; and d) the light induced Thr dephosphorylation is independent of photosynthetic activity in these cells.

Evolutionary insights on critically endangered Kashmir red deer or hangul (Cervus hanglu hanglu) through a mitogenomic lens.

The Kashmir red deer or Hangul (Cervus hanglu hanglu) is the only Tarim red deer species endemic to India. With a current estimated population size of fewer than 200 individuals, this critically endangered species is confined to the greater Dachigam landscape in Jammu and Kashmir. Poaching, habitat loss and fragmentation, resource competition with livestock, and small population size are the major conservation challenges for this species. Blood sampling was conducted from two wild Hangul individuals during radio-collaring operations at Dachigam National Park, Kashmir in 2013 and 2020, respectively. Using next-generation sequencing approach, we sequenced the 16,351 bp long mitogenome of two wild-caught Hangul individuals (1 M:1 F at ~14× and ~10× coverage, respectively) from Dachigam National Park. The annotated sequences were identical with an AT-rich composition, including 13 protein-coding genes (11,354 bp), 22 tRNA genes (1,515 bp), two ribosomal genes (2,526 bp) and a non-coding control region (917 bp) in a conserved order like other red deer species. Bayesian phylogenetic reconstruction of the red deer complex revealed two major groups: the elaphoid and the wapitoid clades. Hangul formed a distinct clade with its other subspecies C. hanglu yarkandensis and is sister to the Hungarian red deer (C. elaphus hippelaphus). Divergence time analyses suggested that the Tarim deer species group separated ~1.55 Mya from their common ancestors and Hangul diverged ~0.75 Mya from closely related C. yarkandensis, corroborating with the known paleobiogeographic events related to refugia during glaciations in the Pleistocene era. This study provides baseline information on Hangul mitogenome for further research on phylogeography and other population parameters and helps in developing suitable conservation plans for this species.

Uncovering the hidden threat: The widespread presence of chromosome-borne accessory genetic elements and novel antibiotic resistance genetic environments in Aeromonas

ABSTRACT The emergence of antibiotic-resistant Aeromonas strains in clinical settings has presented an escalating burden on human and public health. The dissemination of antibiotic resistance in Aeromonas is predominantly facilitated by chromosome-borne accessory genetic elements, although the existing literature on this subject remains limited. Hence, the primary objective of this study is to comprehensively investigate the genomic characteristics of chromosome-borne accessory genetic elements in Aeromonas. Moreover, the study aims to uncover novel genetic environments associated with antibiotic resistance on these elements. Aeromonas were screened from nonduplicated strains collected from two tertiary hospitals in China. Complete sequencing and population genetics analysis were performed. BLAST analysis was employed to identify related elements. All newly identified elements were subjected to detailed sequence annotation, dissection, and comparison. We identified and newly designated 19 chromosomal elements, including 18 integrative and mobilizable elements (IMEs) that could be classified into four categories: Tn6737-related, Tn6836-related, Tn6840-related, and Tn6844a-related IMEs. Each class exhibited a distinct pattern in the types of resistance genes carried by the IMEs. Several novel antibiotic resistance genetic environments were uncovered in these elements. Notably, we report the first identification of the bla OXA-10 gene and bla VEB-1 gene in clinical A. veronii genome, the first presence of a tetA(E)–tetR(E) resistance gene environment within the backbone region in IMEs, and a new mcr-3.15 resistance gene environment. The implications of these findings are substantial, as they provide new insights into the evolution, structure, and dissemination of chromosomal-borne accessory elements.

RIZ2 at the crossroad of the EGF/EGFR signaling in colorectal cancer

BackgroundColorectal cancer (CRC) is the third most deadly and fourth most diagnosed cancer worldwide. Despite the progress in early diagnosis and advanced therapeutic options, CRC shows a poor prognosis with a 5 year survival rate of ~ 45%. PRDM2/RIZ, a member of PR/SET domain family (PRDM), expresses two main molecular variants, the PR-plus isoform (RIZ1) and the PR-minus (RIZ2). The imbalance in their expression levels in favor of RIZ2 is observed in many cancer types. The full length RIZ1 has been extensively investigated in several cancers where it acts as a tumor suppressor, whereas few studies have explored the RIZ2 oncogenic properties. PRDM2 is often target of frameshift mutations and aberrant DNA methylation in CRC. However, little is known about its role in CRC.MethodsWe combined in-silico investigation of The Cancer Genome Atlas (TCGA) CRC datasets, cellular and molecular assays, transcriptome sequencing and functional annotation analysis to assess the role of RIZ2 in human CRC.ResultsOur in-silico analysis on TCGA datasets confirmed that PRDM2 gene is frequently mutated and transcriptionally deregulated in CRC and revealed that a RIZ2 increase is highly correlated with a significant RIZ1 downregulation. Then, we assayed several CRC cell lines by qRT-PCR analysis for the main PRDM2 transcripts and selected DLD1 cell line, which showed the lowest RIZ2 levels. Therefore, we overexpressed RIZ2 in these cells to mimic TCGA datasets analysis results and consequently to assess the PRDM2/RIZ2 role in CRC. Data from RNA-seq disclosed that RIZ2 overexpression induced profound changes in CRC cell transcriptome via EGF pathway deregulation, suggesting that RIZ2 is involved in the EGF autocrine regulation of DLD1 cell behavior. Noteworthy, the forced RIZ2 expression increased cell viability, growth, colony formation, migration and organoid formation. These effects could be mediated by the release of high EGF levels by RIZ2 overexpressing DLD1 cells.ConclusionsOur findings add novel insights on the putative RIZ2 tumor-promoting functions in CRC, although additional efforts are warranted to define the underlying molecular mechanism.

Transcriptome analysis of Haemaphysalis flava female using Illumina HiSeq 4000 sequencing: de novo assembly, functional annotation and discovery of SSR markers

BackgroundTicks are ectoparasites capable of directly damaging their hosts and transmitting vector-borne diseases. The ixodid tick Haemaphysalis flava has a broad distribution that extends from East to South Asia. This tick is a reservoir of severe fever with thrombocytopenia syndrome virus (SFTSV) that causes severe hemorrhagic disease, with cases reported from China, Japan and South Korea. Recently, the distribution of H. flava in South Korea was found to overlap with the occurrence of SFTSV.MethodsThis study was undertaken to discover the molecular resources of H. flava female ticks using the Illumina HiSeq 4000 system, the Trinity de novo sequence assembler and annotation against public databases. The locally curated Protostome database (PANM-DB) was used to screen the putative adaptation-related transcripts classified to gene families, such as angiotensin-converting enzyme, aquaporin, adenylate cyclase, AMP-activated protein kinase, glutamate receptors, heat shock proteins, molecular chaperones, insulin receptor, mitogen-activated protein kinase and solute carrier family proteins. Also, the repeats and simple sequence repeats (SSRs) were screened from the unigenes using RepeatMasker (v4.0.6) and MISA (v1.0) software tools, followed by the designing of SSRs flanking primers using BatchPrimer 3 (v1.0) software.ResultsThe transcriptome produced a total of 69,822 unigenes, of which 46,175 annotated to the homologous proteins in the PANM-DB. The unigenes were also mapped to the EuKaryotic Orthologous Groups (KOG), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) specializations. Promiscuous presence of protein kinase, zinc finger (C2H2-type), reverse transcriptase, and RNA recognition motif domains was observed in the unigenes. A total of 3480 SSRs were screened, of which 1907 and 1274 were found as tri- and dinucleotide repeats, respectively. A list of primer sequences flanking the SSR motifs was detailed for validation of polymorphism in H. flava and the related tick species.ConclusionsThe reference transcriptome information on H. flava female ticks will be useful for an enriched understanding of tick biology, its competency to act as a vector and the study of species diversity related to disease transmission.Graphical abstract

Reassembling Fragmented Entity Names: A Novel Model for Chinese Compound Noun Processing

In the process of classifying intelligent assets, we encountered challenges with a limited dataset dominated by complex compound noun phrases. Training classifiers directly on this dataset posed risks of overfitting and potential misinterpretations due to inherent ambiguities in these phrases. Recognizing the gap in the current literature for tailored methods addressing this challenge, this paper introduces a refined approach for the accurate extraction of entity names from such structures. We leveraged the Chinese pre-trained BERT model combined with an attention mechanism, ensuring precise interpretation of each token’s significance. This was followed by employing both a multi-layer perceptron (MLP) and an LSTM-based Sequence Parsing Model, tailored for sequence annotation and rule-based parsing. With the aid of a rule-driven decoder, we reconstructed comprehensive entity names. Our approach adeptly extracts structurally coherent entity names from fragmented compound noun phrases. Experiments on a manually annotated dataset of compound noun phrases demonstrate that our model consistently outperforms rival methodologies. These results compellingly validate our method’s superiority in extracting entity names from compound noun phrases.

The multidrug-resistant Pseudomonas fluorescens strain: a hidden threat in boar semen preservation.

Although the bacterial composition of boar ejaculate has been extensively studied, the bacterial composition of extended boar semen is often overlooked, despite the potential risks these microorganisms may pose to the long-term preservation of extended boar semen at 15-17°C. In this study, we characterized the bacterial community composition of extended semen and discovered that Pseudomonas spp. was the dominant flora. The dominant strains were further isolated and identified as a potential new species in the Pseudomonas fluorescens group and named GXZC strain, which had adverse effects on sperm quality and was better adapted to growth at 17°C. Antimicrobial susceptibility testing showed that the GXZC strain was resistant to all commonly used veterinary antibiotics. Whole-genome sequencing (WGS) and genome annotation revealed the large genetic structure and function [7,253,751 base pairs and 6,790 coding sequences (CDSs)]. Comparative genomic analysis with the closest type strains showed that the GXZC strain predicted more diversity of intrinsic and acquired resistance genes to multi-antimicrobial agents. Taken together, our study highlights a problem associated with the long-term storage of extended boar semen caused by a P. fluorescens group strain with unique biological characteristics. It is essential to develop a new antibacterial solution for the long-term preservation of boar semen.

Predicting enzymatic function of protein sequences with attention.

There is a growing number of available protein sequences, but only a limited amount has been manually annotated. For example, only 0.25% of all entries of UniProtKB are reviewed by human annotators. Further developing automatic tools to infer protein function from sequence alone can alleviate part of this gap. In this article, we investigate the potential of Transformer deep neural networks on a specific case of functional sequence annotation: the prediction of enzymatic classes. We show that our EnzBert transformer models, trained to predict Enzyme Commission (EC) numbers by specialization of a protein language model, outperforms state-of-the-art tools for monofunctional enzyme class prediction based on sequences only. Accuracy is improved from 84% to 95% on the prediction of EC numbers at level two on the EC40 benchmark. To evaluate the prediction quality at level four, the most detailed level of EC numbers, we built two new time-based benchmarks for comparison with state-of-the-art methods ECPred and DeepEC: the macro-F1 score is respectively improved from 41% to 54% and from 20% to 26%. Finally, we also show that using a simple combination of attention maps is on par with, or better than, other classical interpretability methods on the EC prediction task. More specifically, important residues identified by attention maps tend to correspond to known catalytic sites. Quantitatively, we report a max F-Gain score of 96.05%, while classical interpretability methods reach 91.44% at best. Source code and datasets are respectively available at https://gitlab.inria.fr/nbuton/tfpc and https://doi.org/10.5281/zenodo.7253910.

An Overview of Protein Function Prediction Methods: A Deep Learning Perspective

Abstract: Predicting the function of proteins is a major challenge in the scientific community, particularly in the post-genomic era. Traditional methods of determining protein functions, such as experiments, are accurate but can be resource-intensive and time-consuming. The development of Next Generation Sequencing (NGS) techniques has led to the production of a large number of new protein sequences, which has increased the gap between available raw sequences and verified annotated sequences. To address this gap, automated protein function prediction (AFP) techniques have been developed as a faster and more cost-effective alternative, aiming to maintain the same accuracy level. : Several automatic computational methods for protein function prediction have recently been developed and proposed. This paper reviews the best-performing AFP methods presented in the last decade and analyzes their improvements over time to identify the most promising strategies for future methods. : Identifying the most effective method for predicting protein function is still a challenge. The Critical Assessment of Functional Annotation (CAFA) has established an international standard for evaluating and comparing the performance of various protein function prediction methods. In this study, we analyze the best-performing methods identified in recent editions of CAFA. These methods are divided into five categories based on their principles of operation: sequence-based, structure-based, combined-based, ML-based and embeddings-based. : After conducting a comprehensive analysis of the various protein function prediction methods, we observe that there has been a steady improvement in the accuracy of predictions over time, mainly due to the implementation of machine learning techniques. The present trend suggests that all the bestperforming methods will use machine learning to improve their accuracy in the future. : We highlight the positive impact that the use of machine learning (ML) has had on protein function prediction. Most recent methods developed in this area use ML, demonstrating its importance in analyzing biological information and making predictions. Despite these improvements in accuracy, there is still a significant gap compared with experimental evidence. The use of new approaches based on Deep Learning (DL) techniques will probably be necessary to close this gap, and while significant progress has been made in this area, there is still more work to be done to fully realize the potential of DL.

Rhodoalgimonas zhirmunskyi gen. nov., sp. nov., a Marine Alphaproteobacterium Isolated from the Pacific Red Alga Ahnfeltia tobuchiensis: Phenotypic Characterization and Pan-Genome Analysis.

A novel Gram-staining negative, strictly aerobic, rod-shaped, and non-motile bacterium, designated strain 10Alg 79T, was isolated from the red alga Ahnfeltia tobuchiensis. A phylogenetic analysis based on 16S rRNA gene sequences placed the novel strain within the family Roseobacteraceae, class Alphaproteobacteria, phylum Pseudomonadota, where the nearest neighbor was Shimia sediminis ZQ172T (97.33% of identity). However, a phylogenomic study clearly showed that strain 10Alg 79T forms a distinct evolutionary lineage at the genus level within the family Roseobacteraceae combining with strains Aquicoccus porphyridii L1 8-17T, Marimonas arenosa KCTC 52189T, and Lentibacter algarum DSM 24677T. The ANI, AAI, and dDDH values between them were 75.63-78.15%, 67.41-73.08%, and 18.8-19.8%, respectively. The genome comprises 3,754,741 bp with a DNA GC content of 62.1 mol%. The prevalent fatty acids of strain 10Alg 79T were C18:1 ω7c and C16:0. The polar lipid profile consisted of phosphatidylethanolamine, phosphatidylglycerol, phosphatidylcholine, an unidentified aminolipid, an unidentified phospholipid and an unidentified lipid. A pan-genome analysis showed that the unique part of the 10Alg 79T genome consists of 13 genus-specific clusters and 413 singletons. The annotated singletons were more often related to transport protein systems, transcriptional regulators, and enzymes. A functional annotation of the draft genome sequence revealed that this bacterium could be a source of a new phosphorylase, which may be used for phosphoglycoside synthesis. A combination of the genotypic and phenotypic data showed that the bacterial isolate represents a novel species and a novel genus, for which the name Rhodoalgimonas zhirmunskyi gen. nov., sp. nov. is proposed. The type strain is 10Alg 79T (=KCTC 72611T = KMM 6723T).

DdERT: Research on Named Entity Recognition for Mine Hoist Using a Chinese BERT Model

This study aims to solve the problem of named entity recognition of complex mechanical equipment faults, especially the problems of many professional terms, long sentences, fuzzy entity boundaries, entity nesting, and abbreviation ambiguity, in mine hoist fault text. Therefore, this study proposes a named entity recognition method based on domain dictionary embedding. The method first uses the fault domain knowledge of the mine hoist to construct a domain-specialized dictionary and generate a word vector of characteristic words. Secondly, the BERT pre-trained language model is used to obtain dynamic word vectors, and a dictionary adapter is loaded to obtain contextual domain lexical features to improve recognition accuracy. Finally, the conditional random field (CRF) is the model classifier to output the annotation sequence with the highest score. The experimental results show that this model achieves better than several baseline models and effectively improves the accuracy of fault named entity identification for mine hoists. The innovation of this study is the combination of domain dictionary embedding and a BERT pre-trained language model, which improves the accuracy and robustness of named entity recognition. Therefore, the results of this study have essential research significance for improving the accuracy of fault named entity identification of mine hoists and the construction of fault knowledge maps.

Biodegradation of Inosine and Guanosine by Bacillus paranthracis YD01.

Both inosine and guanosine are precursors of uric acid that may cause the diseases of hyperuricemia and gout in humans. Here, a promising bacterial strain for efficiently biodegrading both inosine and guanosine was successfully isolated from a healthy human intestine and identified as Bacillus paranthracis YD01 with 16S rRNA analysis. An initial amount of 49.6 mg·L-1 of inosine or 49.9 mg·L-1 of guanosine was completely removed by YD01 within 12 h, which showed that YD01 had a strong ability to biodegrade inosine and guanosine. Furthermore, the initial amount of 49.2 mg·L-1 of inosine or 49.5 mg·L-1 of guanosine was totally catalyzed by the intracellular crude enzymes of YD01 within 6 h, and the initial inosine amount of 49.6 mg·L-1 or guanosine of 49.7 mg·L-1 was biodegraded by the extracellular crude enzymes of YD01 within 9 h. Illumina Hiseq sequencing and database gene annotation were used to elucidate the genomic characteristics of B. paranthracis YD01. Purine nucleoside phosphorylase, encoded by gene 1785, gene 3933, and gene 4403, was found in the KEEG database, which played a crucial role in the biodegradation of inosine and guanosine. The results of this study provide valuable insights into the mechanisms for biodegrading inosine and guanosine using B. paranthracis YD01.

Genetic diversity and characterization of rhinoviruses from Chinese clinical samples with a global perspective.

Rhinovirus (RV) is a significant pathogen causing upper and lower respiratory diseases and is one of the most prevalent human respiratory viruses. However, research on the genetic diversity and evolutionary dynamics of RVs in China and worldwide remains limited. To address this knowledge gap, we utilized Chinese clinical RV samples as a starting point and detected and reported 22 types, including A9, B3, and C13, in China for the first time. Among these, A67, A76, and A106 were also detected and reported in Asia for the first time, characterizing their genetic diversity. We also identified A110 as a novel type of RVA and reported its distinctive characteristics in phylogeny, secondary structure, and capsid protein structure. Furthermore, by mining, refining, and annotating RV sequence data available worldwide, four previously unreported novel types, B107, C58, C59, and C60, were identified, and their genetic diversity was revealed. Furthermore, we observed variations in the guanine-cytosine (GC) content among different serotypes and clades. Also of note was that, based on a complete and refined VP1 data set, the evolutionary dynamics of RV were analyzed systematically and on a large scale for the first time. IMPORTANCE Based on clinical samples collected in China, we detected and reported 22 types for the first time in China, as well as three types for the first time in Asia, and reported their genetic characteristics and diversity. We identified a novel type of Rhinovirus (RV), A110, highlighting its unique genetic features. We annotated the genomic structure and serotype of all the existing RV sequences in the database, and four novel RV types were identified and their genetic diversity reported. Combined with the sequence annotation, we constructed a complete VP1 data set of RV and conducted the first large-scale evolutionary dynamics analysis of RV. Based on a high-quality data set, we conducted a comprehensive analysis of the guanine-cytosine (GC) content variations among serotypes of RVs. This study provides crucial theoretical support and valuable data for understanding RV's genetic diversity and developing antiviral strategies.