Exploring the energy landscape of a SAM-I riboswitch

SAM-I riboswitches regulate gene expression through transcription termination upon binding a S-adenosyl-L-methionine (SAM) ligand. In previous work, we characterized the conformational energy landscape of the full-length Bacillus subtilis yitJ SAM-I riboswitch as a function of Mg2+ and SAM ligand concentrations. Here, we have extended this work with measurements on a structurally similar ligand, S-adenosyl-l-homocysteine (SAH), which has, however, a much lower binding affinity. Using single-molecule Förster resonance energy transfer (smFRET) microscopy and hidden Markov modeling (HMM) analysis, we identified major conformations and determined their fractional populations and dynamics. At high Mg2+ concentration, FRET analysis yielded four distinct conformations, which we assigned to two terminator and two antiterminator states. In the same solvent, but with SAM added at saturating concentrations, four states persisted, although their populations, lifetimes and interconversion dynamics changed. In the presence of SAH instead of SAM, HMM revealed again four well-populated states and, in addition, a weakly populated ‘hub’ state that appears to mediate conformational transitions between three of the other states. Our data show pronounced and specific effects of the SAM and SAH ligands on the RNA conformational energy landscape. Interestingly, both SAM and SAH shifted the fractional populations toward terminator folds, but only gradually, so the effect cannot explain the switching action. Instead, we propose that the noticeably accelerated dynamics of interconversion between terminator and antiterminator states upon SAM binding may be essential for control of transcription.

Deriving Effective Decision-Making Strategies of Prosthetists: Using Hidden Markov Modeling and Qualitative Analysis to Compare Experts and Novices

Objective This research focuses on studying the clinical decision-making strategies of expert and novice prosthetists for different case complexities. Background With an increasing global amputee population, there is an urgent need for improved amputee care. However, current prosthetic prescription standards are based on subjective expertise, making the process challenging for novices, specifically during complex patient cases. Hence, there is a need for studying the decision-making strategies of prosthetists. Method An interactive web-based survey was developed with two case studies of varying complexities. Navigation between survey pages and time spent were recorded for 28 participants including experts ( n = 20) and novices ( n = 8). Using these data, decision-making strategies, or patterns of decisions, during prosthetic prescription were derived using hidden Markov modeling. A qualitative analysis of participants’ rationale regarding decisions was used to add a deep contextualized understanding of decision-making strategies derived from the quantitative analysis. Results Unique decision-making strategies were observed across expert and novice participants. Experts tended to focus on the personal details, activity level, and state of the residual limb prior to prescription, and this strategy was independent of case complexity. Novices tended to change strategies dependent upon case complexity, fixating on certain factors when case complexity was high. Conclusion The decision-making strategies of experts stayed the same across the two cases, whereas the novices exhibited mixed strategies. Application By modeling the decision-making strategies of experts and novices, this study builds a foundation for development of an automated decision-support tool for prosthetic prescription, advancing novice training, and amputee care.

Decision-Feedback Stages Revealed by Hidden Markov Modeling of EEG

Decision response and feedback in gambling are interrelated. Different decisions lead to different ranges of feedback, which in turn influences subsequent decisions. However, the mechanism underlying the continuous decision-feedback process is still left unveiled. To fulfill this gap, we applied the hidden Markov model (HMM) to the gambling electroencephalogram (EEG) data to characterize the dynamics of this process. Furthermore, we explored the differences between distinct decision responses (i.e. choose large or small bets) or distinct feedback (i.e. win or loss outcomes) in corresponding phases. We demonstrated that the processing stages in decision-feedback process including strategy adjustment and visual information processing can be characterized by distinct brain networks. Moreover, time-varying networks showed, after decision response, large bet recruited more resources from right frontal and right center cortices while small bet was more related to the activation of the left frontal lobe. Concerning feedback, networks of win feedback showed a strong right frontal and right center pattern, while an information flow originating from the left frontal lobe to the middle frontal lobe was observed in loss feedback. Taken together, these findings shed light on general principles of natural decision-feedback and may contribute to the design of biologically inspired, participant-independent decision-feedback systems.

Multi-parameter photon-by-photon hidden Markov modeling

Single molecule FRET (smFRET) is a useful tool for studying biomolecular sub-populations and their dynamics. Advanced smFRET-based techniques often track multiple parameters simultaneously, increasing the information content of the measurement. Photon-by-photon hidden Markov modelling (H2MM) is a smFRET analysis tool that quantifies FRET dynamics of single biomolecules, even if they occur in sub-milliseconds. However, sub-populations can be characterized by additional experimentally-derived parameters other than the FRET efficiency. We introduce multi-parameter H2MM (mpH2MM) that identifies sub-populations and their transition dynamics based on multiple experimentally-derived parameters, simultaneously. We show the use of this tool in deciphering the number of underlying sub-populations, their mean characteristics and the rate constants of their transitions for a DNA hairpin exhibiting milliseconds FRET dynamics, and for the RNA polymerase promoter open complex exhibiting sub-millisecond FRET dynamics of the transcription bubble. Overall, we show that using mpH2MM facilitates the identification and quantification of biomolecular sub-populations in smFRET measurements that are otherwise difficult to identify. Finally we provide the means to use mpH2MM in analyzing FRET dynamics in advanced multi-color smFRET-based measurements.

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales

State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. We addressed this issue by using simultaneous MEG/EEG recordings at rest and comparing the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differed both spatially and temporally. Microstates tend to exhibit spatio-temporal locality, whereas power envelope HMM states disclose network-level activity with 100–200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.