A strain with a substantially smaller genome retains the core metabolic versatility of its genus.

are a group of phototrophic microbes with a marked metabolic versatility and flexibility that underpins their potential use in the production of value-added products, bioremediation, and plant growth promotion. Members of this group have an average genome size of about 5.5 Mb, but two closely related strains have genome sizes of about 4.

Investigating the Consistency of Negative BOLD Responses to Combinations of Visual, Auditory, and Somatosensory Stimuli and Their Modulation by the Level of Task Demand.

Negative BOLD fMRI responses (NBR) occur commonly in sensory cortex and default mode network regions but remain poorly utilized as a marker of brain function due to an incomplete understanding. To better understand how NBR manifest across the brain, compare between different sensory stimuli and how they are modulated by changes in task demand, we recorded fMRI during trials of visual, auditory, or somatosensory stimulation, delivered either alone or in concurrent pairs. Twenty young-adult participants were cued to attend to a single modality and detect targets in each trial.

Two DRB3 residues predictively associate with the progression to type 1 diabetes among DR3 carriers.

HLA-DR genes are associated with the progression from stage 1 and stage 2 to onset of stage 3 type 1 diabetes (T1D), after accounting HLA-DQ genes with which they are in high linkage-disequilibrium. Based on an integrated cohort of participants from two completed clinical trials, this investigation finds that sharing a haplotype with the DRB1*03:01 (DR3) allele, DRB3*01:01:02 and *02:02:01 have respectively negative and positive associations with the progression. Further, we uncovered two residues (β11, β26, participating in pockets 6 and 4, respectively) on the DRB3 molecule responsible for the progression among DR3 carriers, i.

Environmental matrix and moisture influence soil microbial phenotypes in a simplified porous media incubation.

Soil moisture and porosity regulate microbial metabolism by influencing factors, such as system chemistry, substrate availability, and soil connectivity. However, accurately representing the soil environment and establishing a tractable microbial community that limits confounding variables is difficult. Here, we use a reduced-complexity microbial consortium grown in a glass bead porous media amended with chitin to test the effects of moisture and a structural matrix on microbial phenotypes.

Coupling flux balance analysis with reactive transport modeling through machine learning for rapid and stable simulation of microbial metabolic switching.

Integrating genome-scale metabolic networks with reactive transport models (RTMs) provides a detailed description of the dynamic changes in microbial growth and metabolism. Despite promising demonstrations in the past, computational inefficiency has been pointed out as a critical issue to overcome because it requires repeated application of linear programming (LP) to obtain flux balance analysis (FBA) solutions in every time step and spatial grid. To address this challenge, we propose a new simulation method where we train and validate artificial neural networks (ANNs) using randomly sampled FBA solutions and incorporate the resulting surrogate FBA model (represented as algebraic equations) into RTMs as source/sink terms.