Genome sequence of Microbacterium foliorum bacteriophage DumpQuist isolated from soil in Clarksville, Tennessee.

Bacteriophage DumpQuist was isolated from soil collected in Clarksville, TN, using the bacterium Microbacterium foliorum. Electron microscopy revealed that DumpQuist has a podovirus morphology. DumpQuist has a 53,924-bp genome that contains 54 predicted protein-coding genes and is most similar to phages in actinobacteriophage cluster EK1.

Complete Genome Sequence of Microbacterium foliorum Bacteriophage Librie.

Bacteriophage Librie was isolated from a soil sample from Clarksville, TN, using the bacterium Microbacterium foliorum. Librie has a 39,941 bp genome with 62 predicted protein-coding genes and 1 predicted gene for tRNA. Based on its gene content similarity to actinobacteriophages, Librie is grouped with phages in cluster EA5.

Complete Genome Sequences of Microbacterium Bacteriophages Danno, Otwor, and Scumberland, Isolated in Clarksville, Tennessee.

This paper reports the genome sequences of bacteriophages isolated from soil samples using Microbacterium foliorum Phages Danno and Otwor (cluster EE) have genomes of 17,452 bp and 17,454 bp, respectively, and 25 predicted genes. The phage Scumberland (cluster EC) has a genome of 53,276 bp with 92 predicted genes.

Extracting biological age from biomedical data via deep learning: too much of a good thing?

Age-related physiological changes in humans are linearly associated with age. Naturally, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biological age. In this work, we used one-week long physical activity records from a 2003-2006 National Health and Nutrition Examination Survey (NHANES) to compare three increasingly accurate biological age models: the unsupervised Principal Components Analysis (PCA) score, a multivariate linear regression, and a state-of-the-art deep convolutional neural network (CNN). We found that the supervised approaches produce better chronological age estimations at the expense of a loss of the association between the aging acceleration and all-cause mortality. Consequently, we turned to the NHANES death register directly and introduced a novel way to train parametric proportional hazards models suitable for out-of-the-box implementation with any modern machine learning software. As a demonstration, we produced a separate deep CNN for mortality risks prediction that outperformed any of the biological age or a simple linear proportional hazards model. Altogether, our findings demonstrate the emerging potential of combined wearable sensors and deep learning technologies for applications involving continuous health risk monitoring and real-time feedback to patients and care providers.

Bioreactors for H2 production by purple nonsulfur bacteria.

Two types of laboratory-scale bioreactors were designed for H(2) production by purple nonsulfur bacteria. The bioreactors employed a unique type of hydrogenase activity found in some photosynthetic bacteria that functions in darkness to shift CO (and H2O) into H(2) (and CO2). The mass transport of gaseous CO into an aqueous bacterial suspension was the rate-limiting step and the main challenge for bioreactor design. Hollow-fiber and bubble-train bioreactors employing immobilized and free-living bacteria have proven effective for enhancing the mass transfer of CO. The hollow-fiber bioreactor was designed so that both a growth medium and CO (10% in N(2)) passed from the inside of the fibers to the outside within the bioreactor. Bacteria were immobilized on the outer surface of the hollow fibers. Hydrogen production from CO at an average rate of 125 ml g cdw(-1) h(-1) (maximum rate of 700 ml g cdw(-1) h(-1)) was observed for more than 8 months. The bubble-train bioreactor was built using polyvinyl chloride (PVC) tubing, wound helically on a vertical cylindrical supporting structure. Small bubbles containing CO were injected continuously through a needle/septum connection from the gas reservoir (20% CO). Up to 140 ml g cdw(-1) h(-1) of H(2) production activity was observed using this bioreactor for more than 10 days.

Recruitment of RNA polymerase II in the Ifng gene promoter correlates with the nuclear matrix association in activated T helper cells.

Recruitment of the RNA polymerase II transcription complex to the promoter of the Ifng gene has been studied by chromatin immunoprecipitation (ChIP) in activated functionally different CD4+ T helper (Th) cell subsets. In parallel, analysis of association of the nuclear scaffold/matrix with the Ifng gene promoter has been carried out. The RNA polymerase II (RNA pol II) interacted with the Ifng gene promoter in analyzed activated neutral Th cells, IFN-gamma producing Th1 cells and IFN-gamma silent Th2 cells. However, the interaction of the Ifng gene promoter with the nuclear matrix occurred differentially in a lineage-specific manner. The pattern of the nuclear matrix interaction correlated directly with the gene expression. Strong association of the promoter with the nuclear matrix was observed only in the Th1 cell subset where the Ifng gene was actively transcribed. We propose that it is the interaction of the Ifng gene promoter with the nuclear matrix that may set off transcription in activated Th cells by promoter-associated RNA pol II.