Noninvasive Risk Prediction Models for Heart Failure Using Proportional Jaccard Indices and Comorbidity Patterns.

Background: In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF). Methods: Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF. Results: Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878. Conclusions: Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.

Progression to myocardial infarction short-term death based on interval sequential pattern mining.

BACKGROUND: Myocardial infarction (MI) is one of the significant cardiovascular diseases (CVDs). According to Taiwanese health record analysis, the hazard rate reaches a peak in the initial year after diagnosis of MI, drops to a relatively low value, and maintains stable for the following years. Therefore, identifying suspicious comorbidity patterns of short-term death before the diagnosis may help achieve prolonged survival for MI patients. METHODS: Interval sequential pattern mining was applied with odds ratio to the hospitalization records from the Taiwan National Health Insurance Research Database to evaluate the disease progression and identify potential subjects at the earliest possible stage. RESULTS: Our analysis resulted in five disease pathways, including "diabetes mellitus," "other disorders of the urethra and urinary tract," "essential hypertension," "hypertensive heart disease," and "other forms of chronic ischemic heart disease" that led to short-term death after MI diagnosis, and these pathways covered half of the cohort. CONCLUSION: We explored the possibility of establishing trajectory patterns to identify the high-risk population of early mortality after MI.

Identification of fish species through tRNA-based primer design.

BACKGROUND: The correct establishment of the barcode classification system for fish can facilitate biotaxonomists to distinguish fish species, and it can help the government to verify the authenticity of the ingredients of fish products or identify unknown fish related samples. The Cytochrome c oxidation I (COI) gene sequence in the mitochondria of each species possesses unique characteristics, which has been widely used as barcodes in identifying species in recent years. Instead of using COI gene sequences for primer design, flanking tRNA segments of COI genes from 2618 complete fish mitochondrial genomes were analyzed to discover suitable primers for fish classification at taxonomic family level. The minimal number of primer sets is designed to effectively distinguish various clustered groups of fish species for identification applications. Sequence alignment analysis and cross tRNA segment comparisons were applied to check and ensure the primers for each cluster group are exclusive. RESULTS: Two approaches were applied to improve primer design and re-cluster fish species. The results have shown that exclusive primers for 2618 fish species were successfully discovered through in silico analysis. In addition, we applied sequence alignment analysis to confirm that each pair of primers can successfully identify all collected fish species at the taxonomic family levels. CONCLUSIONS: This study provided a practical strategy to discover unique primers for each fishery species and a comprehensive list of exclusive primers for extracting COI barcode sequences of all known fishery species. Various applications of verification of fish products or identification of unknown fish species could be effectively achieved.

Conformational epitope matching and prediction based on protein surface spiral features.

BACKGROUND: A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. RESULTS: We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy. CONCLUSIONS: The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.

A voting mechanism-based linear epitope prediction system for the host-specific Iridoviridae family.

BACKGROUND: The Iridoviridae family is categorized into five genera and clustered into two subfamilies: Alphairidovirinae includes Lymphocystivirus, Ranavirus (GIV), and Megalocystivirus (TGIV), which infect vertebrate hosts and Betairidovirinae includes Iridovirus and Chloriridovirus, which infect invertebrate hosts. Clustered Iridoviridae subfamilies possess host-specific characteristics, which can be considered as exclusive features for in-silico prediction of effective epitopes for vaccine development. A voting mechanism-based linear epitope (LE) prediction system was applied to identify and endorse LE candidates with a minimum length requirement for each clustered subfamily RESULTS: The experimental results showed that four conserved epitopes among the Iridovirideae family, one exclusive epitope for invertebrate subfamily and two exclusive epitopes for vertebrate family were predicted. These predicted LE candidates were further validated by ELISA assays for evaluating the strength of antigenicity and cross antigenicity. The conserved LEs for Iridoviridae family reflected high antigenicity responses for the two subfamilies, while exclusive LEs reflected high antigenicity responses only for the host-specific subfamily CONCLUSIONS: Host-specific characteristics are important features and constraints for effective epitope prediction. Our proposed voting mechanism based system provides a novel approach for in silico LE prediction prior to vaccine development, and it is especially powerful for analyzing antigen sequences with exclusive features between two clustered groups.

Multiple model species selection for transcriptomics analysis of non-model organisms.

BACKGROUND: Transcriptomic sequencing (RNA-seq) related applications allow for rapid explorations due to their high-throughput and relatively fast experimental capabilities, providing unprecedented progress in gene functional annotation, gene regulation analysis, and environmental factor verification. However, with increasing amounts of sequenced reads and reference model species, the selection of appropriate reference species for gene annotation has become a new challenge. METHODS: We proposed a novel approach for finding the most effective reference model species through taxonomic associations and ultra-conserved orthologous (UCO) gene comparisons among species. An online system for multiple species selection (MSS) for RNA-seq differential expression analysis was developed, and comprehensive genomic annotations from 291 reference model eukaryotic species were retrieved from the RefSeq, KEGG, and UniProt databases. RESULTS: Using the proposed MSS pipeline, gene ontology and biological pathway enrichment analysis can be efficiently achieved, especially in the case of transcriptomic analysis of non-model organisms. The results showed that the proposed method solved problems related to limitations in annotation information and provided a roughly twenty-fold reduction in computational time, resulting in more accurate results than those of traditional approaches of using a single model reference species or the large non-redundant reference database. CONCLUSIONS: Selection of appropriate reference model species helps to reduce missing annotation information, allowing for more comprehensive results than those obtained with a single model reference species. In addition, adequate model species selection reduces the computational time significantly while retaining the same order of accuracy. The proposed system indeed provides superior performance by selecting appropriate multiple species for transcriptomic analysis compared to traditional approaches.

Complete chloroplast genome of Gracilaria firma (Gracilariaceae, Rhodophyta), with discussion on the use of chloroplast phylogenomics in the subclass Rhodymeniophycidae.

BACKGROUND: The chloroplast genome of Gracilaria firma was sequenced in view of its role as an economically important marine crop with wide industrial applications. To date, there are only 15 chloroplast genomes published for the Florideophyceae. Apart from presenting the complete chloroplast genome of G. firma, this study also assessed the utility of genome-scale data to address the phylogenetic relationships within the subclass Rhodymeniophycidae. The synteny and genome structure of the chloroplast genomes across the taxa of Eurhodophytina was also examined. RESULTS: The chloroplast genome of Gracilaria firma maps as a circular molecule of 187,001 bp and contains 252 genes, which are distributed on both strands and consist of 35 RNA genes (3 rRNAs, 30 tRNAs, tmRNA and a ribonuclease P RNA component) and 217 protein-coding genes, including the unidentified open reading frames. The chloroplast genome of G. firma is by far the largest reported for Gracilariaceae, featuring a unique intergenic region of about 7000 bp with discontinuous vestiges of red algal plasmid DNA sequences interspersed between the nblA and cpeB genes. This chloroplast genome shows similar gene content and order to other Florideophycean taxa. Phylogenomic analyses based on the concatenated amino acid sequences of 146 protein-coding genes confirmed the monophyly of the classes Bangiophyceae and Florideophyceae with full nodal support. Relationships within the subclass Rhodymeniophycidae in Florideophyceae received moderate to strong nodal support, and the monotypic family of Gracilariales were resolved with maximum support. CONCLUSIONS: Chloroplast genomes hold substantial information that can be tapped for resolving the phylogenetic relationships of difficult regions in the Rhodymeniophycidae, which are perceived to have experienced rapid radiation and thus received low nodal support, as exemplified in this study. The present study shows that chloroplast genome of G. firma could serve as a key link to the full resolution of Gracilaria sensu lato complex and recognition of Hydropuntia as a genus distinct from Gracilaria sensu stricto.

Comparison of grouper infection with two different iridoviruses using transcriptome sequencing and multiple reference species selection.

Due to high-density aquafarming in Taiwan, groupers are commonly infected with two different iridoviruses: Megalocytivirus (grouper iridovirus of Taiwan, TGIV) and Ranavirus (grouper iridovirus, GIV). Iridoviral diseases cause mass mortality, and surviving fish retain these pathogens, which can then be horizontally transferred. These viruses have therefore become a major challenge for grouper aquaculture. In this study, comparisons of the biological responses of groupers to infection with these two different iridoviruses were performed. A novel approach for transcriptomic analysis was proposed to enhance the discovery of differentially expressed genes and associated biological pathways. In this method, suitable and available reference species are selected from the NCBI taxonomy tree and the Ensembl and KEGG databases instead of either choosing only one model species or adopting the NCBI non-redundant dataset as references. Our results show that selection of multiple appropriate model species as references increases the efficiency and performance of analyses compared to those of traditional approaches. Using this method, 17 shared pathways and 5 specific pathways were found to be significantly differentially expressed following infection with the two iridoviruses, among which 11 pathways were additionally identified based on the proposed method of multiple reference species selection. Among the pathways responsive to infection with a specific iridovirus, the spliceosomal pathway (ko03040; p-value = 0.0011) was exclusively associated with TGIV infection, while the glycolysis/gluconeogenesis pathway (ko00010; p-value = 0.0032) was associated with GIV infection. These findings and designed corresponding biological experiments may facilitate a deeper understanding of the mechanisms by which both TGIV and GIV cause fatal infections, as well as the ways in which they induce different pathologies and symptoms. We believe that the proposed novel mechanism for de novo transcriptomic analysis provides superior and comprehensive functional annotations and that the resulting shared and specific pathways identified may help immunologists develop specific vaccines against various types of iridovirus in the near future.

Ecological genomics in Xanthomonas: the nature of genetic adaptation with homologous recombination and host shifts.

BACKGROUND: Comparative genomics provides insights into the diversification of bacterial species. Bacterial speciation usually takes place with lasting homologous recombination, which not only acts as a cohering force between diverging lineages but brings advantageous alleles favored by natural selection, and results in ecologically distinct species, e.g., frequent host shift in Xanthomonas pathogenic to various plants. RESULTS: Using whole-genome sequences, we examined the genetic divergence in Xanthomonas campestris that infected Brassicaceae, and X. citri, pathogenic to a wider host range. Genetic differentiation between two incipient races of X. citri pv. mangiferaeindicae was attributable to a DNA fragment introduced by phages. In contrast to most portions of the genome that had nearly equivalent levels of genetic divergence between subspecies as a result of the accumulation of point mutations, 10% of the core genome involving with homologous recombination contributed to the diversification in Xanthomonas, as revealed by the correlation between homologous recombination and genomic divergence. Interestingly, 179 genes were under positive selection; 98 (54.7%) of these genes were involved in homologous recombination, indicating that foreign genetic fragments may have caused the adaptive diversification, especially in lineages with nutritional transitions. Homologous recombination may have provided genetic materials for the natural selection, and host shifts likely triggered ecological adaptation in Xanthomonas. To a certain extent, we observed positive selection nevertheless contributed to ecological divergence beyond host shifting. CONCLUSION: Altogether, mediated with lasting gene flow, species formation in Xanthomonas was likely governed by natural selection that played a key role in helping the deviating populations to explore novel niches (hosts) or respond to environmental cues, subsequently triggering species diversification.

Gene Ontology based housekeeping gene selection for RNA-seq normalization.

RNA-seq analysis provides a powerful tool for revealing relationships between gene expression level and biological function of proteins. In order to identify differentially expressed genes among various RNA-seq datasets obtained from different experimental designs, an appropriate normalization method for calibrating multiple experimental datasets is the first challenging problem. We propose a novel method to facilitate biologists in selecting a set of suitable housekeeping genes for inter-sample normalization. The approach is achieved by adopting user defined experimentally related keywords, GO annotations, GO term distance matrices, orthologous housekeeping gene candidates, and stability ranking of housekeeping genes. By identifying the most distanced GO terms from query keywords and selecting housekeeping gene candidates with low coefficients of variation among different spatio-temporal datasets, the proposed method can automatically enumerate a set of functionally irrelevant housekeeping genes for pratical normalization. Novel and benchmark testing RNA-seq datasets were applied to demostrate that different selections of housekeeping gene lead to strong impact on differential gene expression analysis, and compared results have shown that our proposed method outperformed other traditional approaches in terms of both sensitivity and specificity. The proposed mechanism of selecting appropriate houskeeping genes for inter-dataset normalization is robust and accurate for differential expression analyses.

A support vector machine approach for truncated fingerprint image detection from sweeping fingerprint sensors.

A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS), successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM), based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates.

A local average distance descriptor for flexible protein structure comparison.

BACKGROUND: Protein structures are flexible and often show conformational changes upon binding to other molecules to exert biological functions. As protein structures correlate with characteristic functions, structure comparison allows classification and prediction of proteins of undefined functions. However, most comparison methods treat proteins as rigid bodies and cannot retrieve similarities of proteins with large conformational changes effectively. RESULTS: In this paper, we propose a novel descriptor, local average distance (LAD), based on either the geodesic distances (GDs) or Euclidean distances (EDs) for pairwise flexible protein structure comparison. The proposed method was compared with 7 structural alignment methods and 7 shape descriptors on two datasets comprising hinge bending motions from the MolMovDB, and the results have shown that our method outperformed all other methods regarding retrieving similar structures in terms of precision-recall curve, retrieval success rate, R-precision, mean average precision and F1-measure. CONCLUSIONS: Both ED- and GD-based LAD descriptors are effective to search deformed structures and overcome the problems of self-connection caused by a large bending motion. We have also demonstrated that the ED-based LAD is more robust than the GD-based descriptor. The proposed algorithm provides an alternative approach for blasting structure database, discovering previously unknown conformational relationships, and reorganizing protein structure classification.

Identification of conserved and polymorphic STRs for personal genomes.

BACKGROUND: Short tandem repeats (STRs) are abundant in human genomes. Numerous STRs have been shown to be associated with genetic diseases and gene regulatory functions, and have been selected as genetic markers for evolutionary and forensic analyses. High-throughput next generation sequencers have fostered new cutting-edge computing techniques for genome-scale analyses, and cross-genome comparisons have facilitated the efficient identification of polymorphic STR markers for various applications. RESULTS: An automated and efficient system for detecting human polymorphic STRs at the genome scale is proposed in this study. Assembled contigs from next generation sequencing data were aligned and calibrated according to selected reference sequences. To verify identified polymorphic STRs, human genomes from the 1000 Genomes Project were employed for comprehensive analyses, and STR markers from the Combined DNA Index System (CODIS) and disease-related STR motifs were also applied as cases for evaluation. In addition, we analyzed STR variations for highly conserved homologous genes and human-unique genes. In total 477 polymorphic STRs were identified from 492 human-unique genes, among which 26 STRs were retrieved and clustered into three different groups for efficient comparison. CONCLUSIONS: We have developed an online system that efficiently identifies polymorphic STRs and provides novel distinguishable STR biomarkers for different levels of specificity. Candidate polymorphic STRs within a personal genome could be easily retrieved and compared to the constructed STR profile through query keywords, gene names, or assembled contigs.

RETRACTED: Cheng et al. Using Comorbidity Pattern Analysis to Detect Reliable Methylated Genes in Colorectal Cancer Verified by Stool DNA Test. Genes 2021, 12, 1539.

The Genes journal retracts the article "Using Comorbidity Pattern Analysis to Detect Reliable Methylated Genes in Colorectal Cancer Verified by Stool DNA Test" [...].

Prediction of conformational epitopes with the use of a knowledge-based energy function and geometrically related neighboring residue characteristics.

BACKGROUND: A conformational epitope (CE) in an antigentic protein is composed of amino acid residues that are spatially near each other on the antigen's surface but are separated in sequence; CEs bind their complementary paratopes in B-cell receptors and/or antibodies. CE predication is used during vaccine design and in immuno-biological experiments. Here, we develop a novel system, CE-KEG, which predicts CEs based on knowledge-based energy and geometrical neighboring residue contents. The workflow applied grid-based mathematical morphological algorithms to efficiently detect the surface atoms of the antigens. After extracting surface residues, we ranked CE candidate residues first according to their local average energy distributions. Then, the frequencies at which geometrically related neighboring residue combinations in the potential CEs occurred were incorporated into our workflow, and the weighted combinations of the average energies and neighboring residue frequencies were used to assess the sensitivity, accuracy, and efficiency of our prediction workflow. RESULTS: We prepared a database containing 247 antigen structures and a second database containing the 163 non-redundant antigen structures in the first database to test our workflow. Our predictive workflow performed better than did algorithms found in the literature in terms of accuracy and efficiency. For the non-redundant dataset tested, our workflow achieved an average of 47.8% sensitivity, 84.3% specificity, and 80.7% accuracy according to a 10-fold cross-validation mechanism, and the performance was evaluated under providing top three predicted CE candidates for each antigen. CONCLUSIONS: Our method combines an energy profile for surface residues with the frequency that each geometrically related amino acid residue pair occurs to identify possible CEs in antigens. This combination of these features facilitates improved identification for immuno-biological studies and synthetic vaccine design. CE-KEG is available at http://cekeg.cs.ntou.edu.tw.

Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration.

BACKGROUND: Protein-ligand interactions are key processes in triggering and controlling biological functions within cells. Prediction of protein binding regions on the protein surface assists in understanding the mechanisms and principles of molecular recognition. In silico geometrical shape analysis plays a primary step in analyzing the spatial characteristics of protein binding regions and facilitates applications of bioinformatics in drug discovery and design. Here, we describe the novel software, PLB-SAVE, which uses parallel processing technology and is ideally suited to extract the geometrical construct of solid angles from surface atoms. Representative clusters and corresponding anchors were identified from all surface elements and were assigned according to the ranking of their solid angles. In addition, cavity depth indicators were obtained by proportional transformation of solid angles and cavity volumes were calculated by scanning multiple directional vectors within each selected cavity. Both depth and volume characteristics were combined with various weighting coefficients to rank predicted potential binding regions. RESULTS: Two test datasets from LigASite, each containing 388 bound and unbound structures, were used to predict binding regions using PLB-SAVE and two well-known prediction systems, SiteHound and MetaPocket2.0 (MPK2). PLB-SAVE outperformed the other programs with accuracy rates of 94.3% for unbound proteins and 95.5% for bound proteins via a tenfold cross-validation process. Additionally, because the parallel processing architecture was designed to enhance the computational efficiency, we obtained an average of 160-fold increase in computational time. CONCLUSIONS: In silico binding region prediction is considered the initial stage in structure-based drug design. To improve the efficacy of biological experiments for drug development, we developed PLB-SAVE, which uses only geometrical features of proteins and achieves a good overall performance for protein-ligand binding region prediction. Based on the same approach and rationale, this method can also be applied to predict carbohydrate-antibody interactions for further design and development of carbohydrate-based vaccines. PLB-SAVE is available at http://save.cs.ntou.edu.tw.

Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics.

The multifaceted nature and swift progression of Amyotrophic Lateral Sclerosis (ALS) pose considerable challenges to our understanding of its evolution and interplay with comorbid conditions. This study seeks to elucidate the temporal dynamics of ALS progression and its interaction with associated diseases. We employed a principal tree-based model to decipher patterns within clinical data derived from a population-based database in Taiwan. The disease progression was portrayed as branched trajectories, each path representing a series of distinct stages. Each stage embodied the cumulative occurrence of co-existing diseases, depicted as nodes on the tree, with edges symbolizing potential transitions between these linked nodes. Our model identified eight distinct ALS patient trajectories, unveiling unique patterns of disease associations at various stages of progression. These patterns may suggest underlying disease mechanisms or risk factors. This research re-conceptualizes ALS progression as a migration through diverse stages, instead of the perspective of a sequence of isolated events. This new approach illuminates patterns of disease association across different progression phases. The insights obtained from this study hold the potential to inform doctors regarding the development of personalized treatment strategies, ultimately enhancing patient prognosis and quality of life.

Interferon-stimulated gene 15 polymorphisms are associated with spontaneous preterm birth in Taiwanese women.

PROBLEM: Immune and inflammatory responses are known to be major causes of preterm birth (PTB). The maternal genetic background plays an important role in the development of PTB. Interferon-stimulated gene 15 (ISG15) is an interferon-induced protein which can modulate immune cell activation and function. We aim to study if polymorphisms in the ISG15 gene are associated with spontaneous PTB (sPTB) risk in Taiwanese women. METHOD OF STUDY: ISG15 rs4615788 C/G, rs1921 G/A, and rs8997 A/G polymorphisms were genotyped in a hospital-based study of 112 women with sPTB and 1120 term controls. The plasma concentrations of ISG15 were determined by enzyme-linked immunosorbent assay. RESULTS: We found the ISG15 rs1921 G-rs8997 A haplotype was associated with decreased risk for PTB (χ2  = 6.26, p = .01, pc  = .04). The A/G genotype of ISG15 rs8997 polymorphism might have the potential to confer reduced risk of PTB women (χ2  = 4.09, p = .04, pc  = .08). Spontaneous PTB women displayed higher plasma ISG15 levels compared to term controls (p < .001). The plasma ISG15 levels among pregnant women with rs8997 A/G genotype were found significantly lower compared to G/G genotype (p = .03). CONCLUSIONS: Women with the ISG15 rs1921 G-rs8997 A haplotype may associate with spontaneous PTB. These findings provide new insights into the etiology of preterm birth.

Distinctive microbial community and genome structure in coastal seawater from a human-made port and nearby offshore island in northern Taiwan facing the Northwestern Pacific Ocean.

Pollution in human-made fishing ports caused by petroleum from boats, dead fish, toxic chemicals, and effluent poses a challenge to the organisms in seawater. To decipher the impact of pollution on the microbiome, we collected surface water from a fishing port and a nearby offshore island in northern Taiwan facing the Northwestern Pacific Ocean. By employing 16S rRNA gene amplicon sequencing and whole-genome shotgun sequencing, we discovered that Rhodobacteraceae, Vibrionaceae, and Oceanospirillaceae emerged as the dominant species in the fishing port, where we found many genes harboring the functions of antibiotic resistance (ansamycin, nitroimidazole, and aminocoumarin), metal tolerance (copper, chromium, iron and multimetal), virulence factors (chemotaxis, flagella, T3SS1), carbohydrate metabolism (biofilm formation and remodeling of bacterial cell walls), nitrogen metabolism (denitrification, N2 fixation, and ammonium assimilation), and ABC transporters (phosphate, lipopolysaccharide, and branched-chain amino acids). The dominant bacteria at the nearby offshore island (Alteromonadaceae, Cryomorphaceae, Flavobacteriaceae, Litoricolaceae, and Rhodobacteraceae) were partly similar to those in the South China Sea and the East China Sea. Furthermore, we inferred that the microbial community network of the cooccurrence of dominant bacteria on the offshore island was connected to dominant bacteria in the fishing port by mutual exclusion. By examining the assembled microbial genomes collected from the coastal seawater of the fishing port, we revealed four genomic islands containing large gene-containing sequences, including phage integrase, DNA invertase, restriction enzyme, DNA gyrase inhibitor, and antitoxin HigA-1. In this study, we provided clues for the possibility of genomic islands as the units of horizontal transfer and as the tools of microbes for facilitating adaptation in a human-made port environment.

Deep-learning based breast cancer detection for cross-staining histopathology images.

Hematoxylin and eosin (H&E) staining is the gold standard for tissue characterization in routine pathological diagnoses. However, these visible light dyes do not exclusively label the nuclei and cytoplasm, making clear-cut segmentation of staining signals challenging. Currently, fluorescent staining technology is much more common in clinical research for analyzing tissue morphology and protein distribution owing to its advantages of channel independence, multiplex labeling, and the possibility of enabling 3D tissue labeling. Although both H&E and fluorescent dyes can stain the nucleus and cytoplasm for representative tissue morphology, color variation between these two staining technologies makes cross-analysis difficult, especially with computer-assisted artificial intelligence (AI) algorithms. In this study, we applied color normalization and nucleus extraction methods to overcome the variation between staining technologies. We also developed an available workflow for using an H&E-stained segmentation AI model in the analysis of fluorescent nucleic acid staining images in breast cancer tumor recognition, resulting in 89.6% and 80.5% accuracy in recognizing specific tumor features in H&E- and fluorescent-stained pathological images, respectively. The results show that the cross-staining inference maintained the same precision level as the proposed workflow, providing an opportunity for an expansion of the application of current pathology AI models.