Deep-sea hydrothermal vent chimneys harbor a high diversity of largely unknown microorganisms. Although the phylogenetic diversity of these microorganisms has been described previously, the adaptation and metabolic potential of the microbial communities is only beginning to be revealed. A pyrosequencing approach was used to directly obtain sequences from a fosmid library constructed from a black smoker chimney 4143-1 in the Mothra hydrothermal vent field at the Juan de Fuca Ridge. A total of 308 034 reads with an average sequence length of 227 bp were generated. Comparative genomic analyses of metagenomes from a variety of environments by two-way clustering of samples and functional gene categories demonstrated that the 4143-1 metagenome clustered most closely with that from a carbonate chimney from Lost City. Both are highly enriched in genes for mismatch repair and homologous recombination, suggesting that the microbial communities have evolved extensive DNA repair systems to cope with the extreme conditions that have potential deleterious effects on the genomes. As previously reported for the Lost City microbiome, the metagenome of chimney 4143-1 exhibited a high proportion of transposases, implying that horizontal gene transfer may be a common occurrence in the deep-sea vent chimney biosphere. In addition, genes for chemotaxis and flagellar assembly were highly enriched in the chimney metagenomes, reflecting the adaptation of the organisms to the highly dynamic conditions present within the chimney walls. Reconstruction of the metabolic pathways revealed that the microbial community in the wall of chimney 4143-1 was mainly fueled by sulfur oxidation, putatively coupled to nitrate reduction to perform inorganic carbon fixation through the Calvin-Benson-Bassham cycle. On the basis of the genomic organization of the key genes of the carbon fixation and sulfur oxidation pathways contained in the large genomic fragments, both obligate and facultative autotrophs appear to be present and contribute to biomass production.
BackgroundAfter the 2009 influenza A(H1N1)pdm09 pandemic, China established its first severe acute respiratory infections (SARI) sentinel surveillance system.MethodsWe analyzed data from SARI cases in 10 hospitals in 10 provinces in China from February 2011 to October 2013.ResultsAmong 5,644 SARI cases, 330 (6%) were influenza-positive. Among these, 62% were influenza A and 38% were influenza B. Compared with influenza-negative cases, influenza-positive SARI cases had a higher median age (20.0 years vs.11.0, p = 0.003) and were more likely to have at least one underlying chronic medical condition (age adjusted percent: 28% vs. 25%, p < 0.001). The types/subtypes of dominant strains identified by SARI surveillance was almost always among dominant strains identified by the influenza like illness (ILI) surveillance system and influenza activity in both systems peaked at the same time.ConclusionsData from China’s first SARI sentinel surveillance system suggest that types/subtypes of circulating influenza strains and epidemic trends among SARI cases were similar to those among ILI cases.
This study intends to evaluate the utilization potential of the combined Raman spectroscopy and machine learning approach to quickly identify the rainbow trout adulteration in Atlantic salmon. The adulterated samples contained various concentrations (0–100% w/w at 10% intervals) of rainbow trout mixed into Atlantic salmon. Spectral preprocessing methods, such as first derivative, second derivative, multiple scattering correction (MSC), and standard normal variate, were employed. Unsupervised algorithms, such as recursive feature elimination, genetic algorithm (GA), and simulated annealing, and supervised K-means clustering (KM) algorithm were used for selecting important spectral bands to reduce the spectral complexity and improve the model stability. Finally, the performances of various machine learning models, including linear regression, nonlinear regression, regression tree, and rule-based models, were verified and compared. The results denoted that the developed GA–KM–Cubist machine learning model achieved satisfactory results based on MSC preprocessing. The determination coefficient (R2) and root mean square error of prediction sets (RMSEP) in the test sets were 0.87 and 10.93, respectively. These results indicate that Raman spectroscopy can be used as an effective Atlantic salmon adulteration identification method; further, the developed model can be used for quantitatively analyzing the rainbow trout adulteration in Atlantic salmon.
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