Emerging Patterns of Microbial Functional Traits

Functional traits are measurable characteristics that affect an organism's fitness under certain environmental conditions. The use of functional traits in microbial ecology holds great promise for improving our ability to develop biogeochemical models and predict ecosystem responses to global changes. Notably, functional traits could be decoupled from taxonomic relatedness, owing to horizontal gene transfer among microorganisms and adaptive evolution. In recent years, our knowledge about microbial functional traits has been substantially enhanced, thereby revealing the multitude of ecological processes in driving community assembly and dynamics. Here, I summarize the emerging patterns of how microbial functional traits respond to changing environments, which considerably differ from better-studied microbial taxonomy. I use niche and neutral theories to explain microbial functional traits. Finally, I highlight future challenges to analyze, elucidate, and utilize functional traits in microbial ecology.

Accurate modeling of temporal correlations in rapidly sampled fMRI time series

Rapid imaging techniques are increasingly used in functional MRI studies because they allow a greater number of samples to be acquired per unit time, thereby increasing statistical power. However, temporal correlations limit the increase in functional sensitivity and must be accurately accounted for to control the false‐positive rate. A common approach to accounting for temporal correlations is to whiten the data prior to estimating fMRI model parameters. Models of white noise plus a first‐order autoregressive process have proven sufficient for conventional imaging studies, but more elaborate models are required for rapidly sampled data. Here we show that when the “FAST” model implemented in SPM is used with a well‐controlled number of parameters, it can successfully prewhiten 80% of grey matter voxels even with volume repetition times as short as 0.35 s. We further show that the temporal signal‐to‐noise ratio (tSNR), which has conventionally been used to assess the relative functional sensitivity of competing imaging approaches, can be augmented to account for the temporal correlations in the time series. This amounts to computing the t‐score testing for the mean signal. We show in a visual perception task that unlike the tSNR weighted by the number of samples, the t‐score measure is directly related to the t‐score testing for activation when the temporal correlations are correctly modeled. This score affords a more accurate means of evaluating the functional sensitivity of different data acquisition options.

Co-Receptor CD8-Mediated Modulation of T-Cell Receptor Functional Sensitivity and Epitope Recognition Degeneracy

The interaction between T-cell receptors (TCRs) and peptide epitopes is highly degenerate: a TCR is capable of interacting productively with a wide range of different peptide ligands, involving not only cross-reactivity proper (similar epitopes elicit strong responses), but also polyspecificity (ligands with distinct physicochemical properties are capable of interacting with the TCR). Degeneracy does not gainsay the fact that TCR recognition is fundamentally specific: for the vast majority of ligands, the functional sensitivity of a given TCR is virtually null whereas this TCR has an appreciable functional sensitivity only for a minute fraction of all possible ligands. Degeneracy can be described mathematically as the probability that the functional sensitivity, of a given TCR to a randomly selected ligand, exceeds a set value. Variation of this value generates a statistical distribution that characterizes TCR degeneracy. This distribution can be modeled on the basis of a Gaussian distribution for the TCR/ligand dissociation energy. The kinetics of the TCR and the MHCI molecule can be used to transform this underlying Gaussian distribution into the observed distribution of functional sensitivity values. In the present paper, the model is extended by accounting explicitly for the kinetics of the interaction between the co-receptor and the MHCI molecule. We show that T-cells can modulate the level of degeneracy by varying the density of co-receptors on the cell surface. This could allow for an analog of avidity maturation during incipient T-cell responses.