TBC: a clustering algorithm based on prokaryotic taxonomy

Application of Machine Learning to Solid Particle Erosion of APS-TBC and EB-PVD TBC at Elevated Temperatures

Machine learning (ML) and deep learning (DL) for big data (BD) management are currently viable approaches that can significantly help in high-temperature materials design and development. ML-DL can accumulate knowledge by learning from existing data generated through multi-physics modelling (MPM) and experimental tests (ETs). DL mainly involves analyzing nonlinear correlations and high-dimensional datasets implemented through specifically designed numerical algorithms. DL also makes it possible to learn from new data and modify predictive models over time, identifying anomalies, signatures, and trends in machine performance, develop an understanding of patterns of behaviour, and estimate efficiencies in a machine. Machine learning was implemented to investigate the solid particle erosion of both APS (air plasma spray) and EB-PVD (electron beam physical vapour deposition) TBCs of hot section components. Several ML models and algorithms were used such as neural networks (NNs), gradient boosting regression (GBR), decision tree regression (DTR), and random forest regression (RFR). It was found that the test data are strongly associated with five key factors as identifiers. Following test data collection, the dataset is subjected to sorting, filtering, extracting, and exploratory analysis. The training and testing, and prediction results are analysed. The results suggest that neural networks using the BR model and GBR have better prediction capability.

Effects of the Brown Seaweed Laminaria japonica Supplementation on Serum Concentrations of IgG, Triglycerides, and Cholesterol, and Intestinal Microbiota Composition in Rats

The intestinal microbial communities play critical roles in various aspects of body function of the host. Prebiotics, such as dietary fiber, can affect health of the host by altering the composition of intestinal microbiota. Although brown seaweed Laminaria japonica is rich in dietary fiber, studies on its prebiotic potential are quite rare. In this study, basal diet (control), basal diet supplemented with dried L. japonica (DLJ), heat-treated dried L. japonica (HLJ), or heated dried L. japonica with added fructooligosaccharide (FHLJ) was fed to rats for 16 weeks. Serum concentrations of IgG, triglyceride, and cholesterol were measured. In addition, the intestinal microbiota composition was analyzed by high-throughput sequencing of 16S rRNA gene. As compared to the control group, DLJ, HLJ, and FHLJ groups showed significantly higher serum IgG concentration, but had lower weight gain and serum triglyceride concentration. Moreover, DLJ, HLJ, and FHLJ groups showed lower Fimicutes to Bacteroidetes ratio when compared with the control group. As compared with the control group, obesity-associated bacterial genera (Allobaculum, Turicibacter, Coprobacillus, Mollicute, and Oscilibacter), and the genera with pathogenic potentials (Mollicute, Bacteroides, Clostridium, Escherichia, and Prevotella) decreased while leanness-associated genera (Alistipes, Bacteroides, and Prevotella), and lactic acid bacterial genera (Subdoligranulum, Streptococcus, Lactobacillus, Enterococcus, and Bifidobacterium) increased in all treatment groups. On the contrary, butyric acid producing genera including Subdoligranulum, Roseburia, Eubacterium, Butyrivibrio, and Anaerotruncus increased significantly only in FHLJ group. The overall results support multiple prebiotic effects of seaweed L. japonica on rats as determined by body weight reduction, enhanced immune response, and desirable changes in intestinal microbiota composition, suggesting the great potential of L. japonica as an effective prebiotic for promotion of host metabolism and reduction of obesity in humans.

hc-OTU: A Fast and Accurate Method for Clustering Operational Taxonomic Units Based on Homopolymer Compaction

To assess the genetic diversity of an environmental sample in metagenomics studies, the amplicon sequences of 16s rRNA genes need to be clustered into operational taxonomic units (OTUs). Many existing tools for OTU clustering trade off between accuracy and computational efficiency. We propose a novel OTU clustering algorithm, hc-OTU, which achieves high accuracy and fast runtime by exploiting homopolymer compaction and k-mer profiling to significantly reduce the computing time for pairwise distances of amplicon sequences. We compare the proposed method with other widely used methods, including UCLUST, CD-HIT, MOTHUR, ESPRIT, ESPRIT-TREE, and CLUSTOM, comprehensively, using nine different experimental datasets and many evaluation metrics, such as normalized mutual information, adjusted Rand index, measure of concordance, and F-score. Our evaluation reveals that the proposed method achieves a level of accuracy comparable to the respective accuracy levels of MOTHUR and ESPRIT-TREE, two widely used OTU clustering methods, while delivering orders-of-magnitude speedups.

Cloacal Microbiome Structure in a Long-Distance Migratory Bird Assessed Using Deep 16sRNA Pyrosequencing

Effects of vertebrate-associated microbiota on physiology and health are of significant interest in current biological research. Most previous studies have focused on host-microbiota interactions in captive-bred mammalian models. These interactions and their outcomes are still relatively understudied, however, in wild populations and non-mammalian taxa. Using deep pyrosequencing, we described the cloacal microbiome (CM) composition in free living barn swallows Hirundo rustica, a long-distance migratory passerine bird. Barn swallow CM was dominated by bacteria of the Actinobacteria, Proteobacteria and Firmicutes phyla. Bacteroidetes, which represent an important proportion of the digestive tract microbiome in many vertebrate species, was relatively rare in barn swallow CM (< 5%). CM composition did not differ between males and females. A significant correlation of CM within breeding pair members is consistent with the hypothesis that cloacal contact during within-pair copulation may promote transfer of bacterial assemblages. This effect on CM composition had a relatively low effect size, however, possibly due to the species’ high level of sexual promiscuity.

Metagenomic insights into the bioaerosols in the indoor and outdoor environments of childcare facilities

Airborne microorganisms have significant effects on human health, and children are more vulnerable to pathogens and allergens than adults. However, little is known about the microbial communities in the air of childcare facilities. Here, we analyzed the bacterial and fungal communities in 50 air samples collected from five daycare centers and five elementary schools located in Seoul, Korea using culture-independent high-throughput pyrosequencing. The microbial communities contained a wide variety of taxa not previously identified in child daycare centers and schools. Moreover, the dominant species differed from those reported in previous studies using culture-dependent methods. The well-known fungi detected in previous culture-based studies (Alternaria, Aspergillus, Penicillium, and Cladosporium) represented less than 12% of the total sequence reads. The composition of the fungal and bacterial communities in the indoor air differed greatly with regard to the source of the microorganisms. The bacterial community in the indoor air appeared to contain diverse bacteria associated with both humans and the outside environment. In contrast, the fungal community was largely derived from the surrounding outdoor environment and not from human activity. The profile of the microorganisms in bioaerosols identified in this study provides the fundamental knowledge needed to develop public health policies regarding the monitoring and management of indoor air quality.

Clinical microbiology informatics

The clinical microbiology laboratory has responsibilities ranging from characterizing the causative agent in a patient's infection to helping detect global disease outbreaks. All of these processes are increasingly becoming partnered more intimately with informatics. Effective application of informatics tools can increase the accuracy, timeliness, and completeness of microbiology testing while decreasing the laboratory workload, which can lead to optimized laboratory workflow and decreased costs. Informatics is poised to be increasingly relevant in clinical microbiology, with the advent of total laboratory automation, complex instrument interfaces, electronic health records, clinical decision support tools, and the clinical implementation of microbial genome sequencing. This review discusses the diverse informatics aspects that are relevant to the clinical microbiology laboratory, including the following: the microbiology laboratory information system, decision support tools, expert systems, instrument interfaces, total laboratory automation, telemicrobiology, automated image analysis, nucleic acid sequence databases, electronic reporting of infectious agents to public health agencies, and disease outbreak surveillance. The breadth and utility of informatics tools used in clinical microbiology have made them indispensable to contemporary clinical and laboratory practice. Continued advances in technology and development of these informatics tools will further improve patient and public health care in the future.

Performance and bacterial communities of successive alkalinity-producing systems (SAPSs) in passive treatment processes treating mine drainages differing in acidity and metal levels

Successive alkalinity-producing systems (SAPSs) is a key unit process in the passive treatment of acidic mine drainage. Physico-chemistry and pyrosequencing-based bacterial communities of two passive treatment processes in Gapjung (GJ) and Seokbong (SB) were analyzed. The influent of SB harbored higher levels of acidity and metals than that of GJ. SAPS-SB demonstrated better performance of acidity neutralization and metal removal than SAPS-GJ, despite its shorter hydraulic retention time and higher acidity. System diagnosis revealed that the capacities of SAPSs were not well predicted in the design steps. Bacterial diversity indices and composition were compared at the same sequence read number for fair evaluation. Most of the bacterial sequences were affiliated with uncultured species. A notable difference was observed in the bacterial community compositions of the SAPSs in GJ and SB. Classes of putative sulfate-reducing bacteria, Clostridia (8.3 %) and Deltaproteobacteria (6.1 %), were detected in SAPS-GJ, and Clostridia (14.6 %) was detected in SAPS-SB. Bacilli, which is not a known sulfate-reducing bacterial group, was the second largest class (12.8 %) in SAPS-GJ and the largest class (51.1 %) in SAPS-SB, suggesting that Bacilli may have a prominent role in SAPS. One hundred ninety operational taxonomic units were shared, which occupied ~10 % of each number of total operational taxonomic units in SAPS-GJ and SAPS-SB, respectively. Bacilli and Clostridia were the major shared classes, and Bacillus, Lysinibacillus, and Ureibacillus were the major shared genera. Rarefaction analysis, richness estimates, diversity estimates, and abundance rank analysis show that the sediment bacterial community of SAPS-GJ was more diverse and more evenly distributed than that of SAPS-SB.

Unraveling the outcome of 16S rDNA-based taxonomy analysis through mock data and simulations

MOTIVATION

16S rDNA pyrosequencing is a powerful approach that requires extensive usage of computational methods for delineating microbial compositions. Previously, it was shown that outcomes of studies relying on this approach vastly depend on the choice of pre-processing and clustering algorithms used. However, obtaining insights into the effects and accuracy of these algorithms is challenging due to difficulties in generating samples of known composition with high enough diversity. Here, we use in silico microbial datasets to better understand how the experimental data are transformed into taxonomic clusters by computational methods.

RESULTS

We were able to qualitatively replicate the raw experimental pyrosequencing data after rigorously adjusting existing simulation software. This allowed us to simulate datasets of real-life complexity, which we used to assess the influence and performance of two widely used pre-processing methods along with 11 clustering algorithms. We show that the choice, order and mode of the pre-processing methods have a larger impact on the accuracy of the clustering pipeline than the clustering methods themselves. Without pre-processing, the difference between the performances of clustering methods is large. Depending on the clustering algorithm, the most optimal analysis pipeline resulted in significant underestimations of the expected number of clusters (minimum: 3.4%; maximum: 13.6%), allowing us to make quantitative estimations of the bacterial complexity of real microbiome samples.

Unveiling abundance and distribution of planktonic Bacteria and Archaea in a polynya in Amundsen Sea, Antarctica

Polynyas, areas of open water surrounded by sea ice, are sites of intense primary production and ecological hotspots in the Antarctic Ocean. This study determined the spatial variation in communities of prokaryotes in a polynya in the Amundsen Sea using 454 pyrosequencing technology, and the results were compared with biotic and abiotic environmental factors. The bacterial abundance was correlated with that of phytoplankton, Phaeocystis spp. and diatoms. A cluster analysis indicated that the bacterial communities in the surface waters of the polynya were distinct from those under the sea ice. Overall, two bacterial clades, Polaribacter (20-64%) and uncultivated Oceanospirillaceae (7-34%), dominated the surface water in the polynya while the Pelagibacter clade was abundant at all depths (7-42%). The archaeal communities were not as diverse as the bacterial communities in the polynya, and marine group I was dominant (> 80%). Canonical correspondence analysis indicated that the oceanographic properties facilitated the development of distinct prokaryotic assemblages in the polynya. This analysis of the diversity and composition of the psychrophilic prokaryotes associated with high phytoplankton production provides new insights into the roles of prokaryotes in biogeochemical cycles in high-latitude polynyas.

Analytical tools and databases for metagenomics in the next-generation sequencing era

Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.

CLUSTOM: a novel method for clustering 16S rRNA next generation sequences by overlap minimization

The recent nucleic acid sequencing revolution driven by shotgun and high-throughput technologies has led to a rapid increase in the number of sequences for microbial communities. The availability of 16S ribosomal RNA (rRNA) gene sequences from a multitude of natural environments now offers a unique opportunity to study microbial diversity and community structure. The large volume of sequencing data however makes it time consuming to assign individual sequences to phylotypes by searching them against public databases. Since ribosomal sequences have diverged across prokaryotic species, they can be grouped into clusters that represent operational taxonomic units. However, available clustering programs suffer from overlap of sequence spaces in adjacent clusters. In natural environments, gene sequences are homogenous within species but divergent between species. This evolutionary constraint results in an uneven distribution of genetic distances of genes in sequence space. To cluster 16S rRNA sequences more accurately, it is therefore essential to select core sequences that are located at the centers of the distributions represented by the genetic distance of sequences in taxonomic units. Based on this idea, we here describe a novel sequence clustering algorithm named CLUSTOM that minimizes the overlaps between adjacent clusters. The performance of this algorithm was evaluated in a comparative exercise with existing programs, using the reference sequences of the SILVA database as well as published pyrosequencing datasets. The test revealed that our algorithm achieves higher accuracy than ESPRIT-Tree and mothur, few of the best clustering algorithms. Results indicate that the concept of an uneven distribution of sequence distances can effectively and successfully cluster 16S rRNA gene sequences. The algorithm of CLUSTOM has been implemented both as a web and as a standalone command line application, which are available at http://clustom.kribb.re.kr.

Bacterial diversity in ornithogenic soils compared to mineral soils on King George Island, Antarctica

In the Narębski Point area of King George Island of Antarctica, ornithogenic soils form on land under Chinstrap and Gentoo Penguin rookeries. The purpose of this study was to compare the bacterial community compositions in the gradient of contamination by penguin feces; mineral soil with no contamination, and soils with medium or high contamination. The discrimination between mineral soils and ornithogenic soils by characterization of physicochemical properties and bacterial communities was notable. Physicochemical analyses of soil properties showed enrichment of carbon and nitrogen in ornithogenic soils. Firmicutes were present abundantly in active ornithogenic soils, Bacteroidetes and Proteobacteria in a formerly active one, and several diverse phyla such as Proteobacteria, Actinobacteria, and Acidobacteria in mineral soils. Some predominant species belonging to the Firmicutes and Gammaproteobacteria may play an important role for the mineralization of nutrients in ornithogenic soils. Results of this study indicate that dominant species may play an important role in mineralization of nutrients in these ecosystems.