Getting it right with discrete choice experiments: Are we hot or cold?

Discrete Choice Experiments (DCEs) are widely employed survey-based methods to assess preferences for healthcare services and products. While they offer an experimental way to represent health-related decisions, the stylized representation of scenarios in DCEs may overlook contextual factors that could influence decision-making. The aim of this paper was to evaluate the predictive validity of preferences elicited through a DCE in decisions likely influenced by a hot-cold empathy gap, and compare it to another commonly used method, a direct-elicitation question. We focused on preferences for pain-relief modalities, especially for an epidural during childbirth - a context where direct-elicitation questions have shown a preference for or intention to have a natural birth (representing the "cold" state), yet individuals often opt for an epidural during labor (representing the "hot" state). Leveraging a unique dataset collected from 248 individuals, we incorporated both the stated preferences collected through a survey administered upon hospital admission for childbirth and the actual pain-relief modality usage data documented in medical records. The DCE allowed for the evaluation of scenarios outside of those expected by respondents to simulate decision-making during childbirth. When we compared the predicted epidural use with the actual epidural use during labor, we observed a choice concordance of 71-60%, depending on the model specification. The concordance rate between the predicted and actual choices increased to 77-76% when accounting for the initial use of other ineffective modalities. In contrast, the direct-elicitation choices, relying solely on respondents' baseline expectations, yielded a lower concordance rate of 58% with actual epidural use. These findings highlight the flexibility of the DCE method in simulating complex decision contexts, including those involving hot-cold empathy gaps. The DCE proves valuable in assessing nuanced preferences, providing a more accurate representation of the decision-making processes in healthcare scenarios.

Binding Position Dependent Modulation of Smoothened Activity by Cyclopamine.

Cyclopamine is a natural alkaloid that is known to act as an agonist when it binds to the Cysteine Rich Domain (CRD) of the Smoothened receptor and as an antagonist when it binds to the Transmembrane Domain (TMD). To study the effect of cyclopamine binding to each binding site experimentally, mutations in the other site are required. Hence, simulations are critical for understanding the WT activity due to binding at different sites. Additionally, there is a possibility that cyclopamine could bind to both sites simultaneously especially at high concentration, the implications of which remain unknown. We performed three independent sets of simulations to observe the receptor activation with cyclopamine bound to each site independently (CRD, TMD) and bound to both sites simultaneously. Using multi-milliseconds long aggregate MD simulations combined with Markov state models and machine learning, we explored the dynamic behavior of cyclopamine's interactions with different domains of WT SMO. A higher population of the active state at equilibrium, a lower activation free energy barrier of ~ 2 kcal/mol, and expansion of the hydrophobic tunnel to facilitate cholesterol transport agrees with the cyclopamine's agonistic behavior when bound to the CRD of SMO. A higher population of the inactive state at equilibrium, a higher free energy barrier of ~ 4 kcal/mol and restricted the hydrophobic tunnel to impede cholesterol transport showed cyclopamine's antagonistic behavior when bound to TMD. With cyclopamine bound to both sites, there was a slightly larger inactive population at equilibrium and an increased free energy barrier (~ 3.5 kcal/mol). The tunnel was slightly larger than when solely bound to TMD, and showed a balance between agonism and antagonism with respect to residue movements exhibiting an overall weak antagonistic effect.

Activation mechanism of the human Smoothened receptor.

Smoothened (SMO) is a membrane protein of the class F subfamily of G protein-coupled receptors (GPCRs) and maintains homeostasis of cellular differentiation. SMO undergoes conformational change during activation, transmitting the signal across the membrane, making it amenable to bind to its intracellular signaling partner. Receptor activation has been studied at length for class A receptors, but the mechanism of class F receptor activation remains unknown. Agonists and antagonists bound to SMO at sites in the transmembrane domain (TMD) and the cysteine-rich domain have been characterized, giving a static view of the various conformations SMO adopts. Although the structures of the inactive and active SMO outline the residue-level transitions, a kinetic view of the overall activation process remains unexplored for class F receptors. We describe SMO's activation process in atomistic detail by performing 300 µs of molecular dynamics simulations and combining it with Markov state model theory. A molecular switch, conserved across class F and analogous to the activation-mediating D-R-Y motif in class A receptors, is observed to break during activation. We also show that this transition occurs in a stage-wise movement of the transmembrane helices: TM6 first, followed by TM5. To see how modulators affect SMO activity, we simulated agonist and antagonist-bound SMO. We observed that agonist-bound SMO has an expanded hydrophobic tunnel in SMO's core TMD, whereas antagonist-bound SMO shrinks this tunnel, further supporting the hypothesis that cholesterol travels through a tunnel inside Smoothened to activate it. In summary, this study elucidates the distinct activation mechanism of class F GPCRs and shows that SMO's activation process rearranges the core TMD to open a hydrophobic conduit for cholesterol transport.

Modelling the propagation of infectious disease via transportation networks.

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention.

Understanding Supramolecular Assembly of Supercharged Proteins.

Ordered supramolecular assemblies have recently been created using electrostatic interactions between oppositely charged proteins. Despite recent progress, the fundamental mechanisms governing the assembly of oppositely supercharged proteins are not fully understood. Here, we use a combination of experiments and computational modeling to systematically study the supramolecular assembly process for a series of oppositely supercharged green fluorescent protein variants. We show that net charge is a sufficient molecular descriptor to predict the interaction fate of oppositely charged proteins under a given set of solution conditions (e.g., ionic strength), but the assembled supramolecular structures critically depend on surface charge distributions. Interestingly, our results show that a large excess of charge is necessary to nucleate assembly and that charged residues not directly involved in interprotein interactions contribute to a substantial fraction (∼30%) of the interaction energy between oppositely charged proteins via long-range electrostatic interactions. Dynamic subunit exchange experiments further show that relatively small, 16-subunit assemblies of oppositely charged proteins have kinetic lifetimes on the order of ∼10-40 min, which is governed by protein composition and solution conditions. Broadly, our results inform how protein supercharging can be used to create different ordered supramolecular assemblies from a single parent protein building block.

Preferences for using the London Underground during the COVID-19 pandemic.

The COVID-19 pandemic has drastically impacted people's travel behaviour and introduced uncertainty in the demand for public transport. To investigate user preferences for travel by London Underground during the pandemic, we conducted a stated choice experiment among its pre-pandemic users (N = 961). We analysed the collected data using multinomial and latent class logit models. Our discrete choice analysis provides two sets of results. First, we derive the crowding multiplier estimate of travel time valuation (i.e., the ratio of the value of travel time in uncrowded and crowded situations) for London underground users. The results indicate that travel time valuation of Underground users increases by 73% when it operates at technical capacity. Second, we estimate the sensitivity of the preference for the London Underground relative to the epidemic situation (confirmed new COVID-19 cases) and interventions (vaccination rates and mandatory face masks). The sensitivity analysis suggests that making face masks mandatory is a main driver for recovering the demand for the London underground. The latent class model reveals substantial preference heterogeneity. For instance, while the average effect of mandatory face masks is positive, the preferences of 30% of pre-pandemic users for travel by the Underground are negatively affected. The positive effect of mandatory face masks on the likelihood of taking the Underground is less pronounced among males with age below 40 years, and a monthly income below 10,000 GBP. The estimated preference sensitivities and crowding multipliers are relevant for supply-demand management in transit systems and the calibration of advanced epidemiological models.

COVID-19 vaccine preferences in India.

India's mass vaccination efforts have been slow due to high levels of vaccine hesitancy. This study uses data from an online discrete choice experiment with 1371 respondents to rigorously examine the factors shaping vaccine preference in the country. We find that vaccine efficacy, presence of side effects, protection duration, distance to vaccination centre and vaccination rates within social network play a critical role in determining vaccine demand. We apply a non-parametric model to uncover heterogeneity in the effects of these factors. We derive two novel insights from this analysis. First, even though, on average, domestically developed vaccines are preferred, around 30% of the sample favours foreign-developed vaccines. Second, vaccine preference of around 15% of the sample is highly sensitive to the presence of side effects and vaccination uptake among their peer group. These results provide insights for the ongoing policy debate around vaccine adoption in India.

A new spatial count data model with Bayesian additive regression trees for accident hot spot identification.

The identification of accident hot spots is a central task of road safety management. Bayesian count data models have emerged as the workhorse method for producing probabilistic rankings of hazardous sites in road networks. Typically, these methods assume simple linear link function specifications, which, however, limit the predictive power of a model. Furthermore, extensive specification searches are precluded by complex model structures arising from the need to account for unobserved heterogeneity and spatial correlations. Modern machine learning (ML) methods offer ways to automate the specification of the link function. However, these methods do not capture estimation uncertainty, and it is also difficult to incorporate spatial correlations. In light of these gaps in the literature, this paper proposes a new spatial negative binomial model which uses Bayesian additive regression trees to endogenously select the specification of the link function. Posterior inference in the proposed model is made feasible with the help of the Pólya-Gamma data augmentation technique. We test the performance of this new model on a crash count data set from a metropolitan highway network. The empirical results show that the proposed model performs at least as well as a baseline spatial count data model with random parameters in terms of goodness of fit and site ranking ability.