Carter T. Butts’s research while affiliated with University of California, Irvine and other places

International sanctions have been an increasingly common tool for enforcinginternational norms of behavior, among other goals. There is continuing debateon what drives sanctioning behavior: while IR theories of sanctions have a longempirical history, more recent studies identify a need to extend these theoriesto better account for the endogenous nature of state sanction networks. Using acombination of network and IR-based insights, we aim to build a theory-driven,interpretable model of international sanctions that has high predictive utility.Using a separable version of dynamic network logistic regression, we test networktheories of “Matthew effects,” reciprocity, and previous state-level sanctioningactivity alongside traditional IR theories regarding the democratic peace, culturalor institutional similarity, power imbalance, and trade. Though we find thatmechanisms from established IR theories largely hold with the inclusion of networkendogeneity, endogenous network effects are more powerful than traditionalIR concepts for predictive accuracy of the sanctioning network. We also find considerabledifferences for factors driving the imposition of sanctions (tie formation)versus persistence of sanction regimes (persistence), pointing to the importanceof treating such effects separately.

Protein aggregation can produce a wide range of states, ranging from fibrillar structures and oligomers to unstructured and semistructured gel phases. Recent work has shown that many of these states can be recapitulated by relatively simple, topological models specified in terms of multibody interaction energies, providing a direct connection between aggregate intermolecular forces and aggregation products. Here, we examine a low-dimensional network Hamiltonian model (NHM) based on four basic multibody interactions found in any aggregate system. We characterize the phase behavior of this NHM family, showing that fibrils arise from a balance between elongation-inducing and contact-inhibiting forces. Complex oligomers (including annular oligomers resembling those thought to be toxic species in Alzheimer’s disease) also form distinct phases in this regime, controlled in part by closure-inducing forces. We show that phase structure is largely independent of system size, and provide evidence of a rich structure of minor oligomeric phases that can arise from appropriate conditions. We characterize the phase behavior of this NHM family, demonstrating the range of ordered and disordered aggregation states possible with this set of interactions. As we show, fibrils arise from a balance between elongation-inducing and contact-inhibiting forces, existing in a regime bounded by gel-like and disaggregated phases; complex oligomers (including annular oligomers resembling those thought to be toxic species in Alzheimer’s disease) also form distinct phases in this regime, controlled in part by closure-inducing forces. We show that phase structure is largely independent of system size, allowing generalization to macroscopic systems, and provide evidence of a rich structure of minor oligomeric phases that can arise from appropriate conditions.

Prolyl oligopeptidases from psychrophilic, mesophilic, and thermophilic organisms found in a range of natural environments are studied using a combination of protein structure prediction, atomistic molecular dynamics, and trajectory analysis to determine how the S9 protease family adapts to extreme thermal conditions. We compare our results with hypotheses from the literature regarding structural adaptations that allow proteins to maintain structure and function at extreme temperatures, and we find that, in the case of prolyl oligopeptidases, only a subset of proposed adaptations are employed for maintaining stability. The catalytic and propeller domains are highly structured, limiting the range of mutations that can be made to enhance hydrophobicity or form disulfide bonds without disrupting the formation of necessary secondary structure. Rather, we observe a pattern in which overall prevalence of bound interactions (salt bridges and hydrogen bonds) is conserved by using increasing numbers of increasingly short-lived interactions as temperature increases. This suggests a role for an entropic rather than energetic strategy for thermal adaptation in this protein family.

The biased net paradigm was the first general and empirically tractable scheme for parameterizing complex patterns of dependence in networks, expressing deviations from uniform random graph structure in terms of latent ``bias events,'' whose realizations enhance reciprocity, transitivity, or other structural features. Subsequent developments have introduced local specifications of biased nets, which reduce the need for approximations required in early specifications based on tracing processes. Here, we show that while one such specification leads to inconsistencies, a closely related Markovian specification both evades these difficulties and can be extended to incorporate new types of effects. We introduce the notion of inhibitory bias events, with satiation as an example, which are useful for avoiding degeneracies that can arise from closure bias terms. Although our approach does not lead to a computable likelihood, we provide a strategy for approximate Bayesian inference using random forest prevision. We demonstrate our approach on a network of friendship ties among college students, recapitulating a relationship between the sibling bias and tie strength posited in earlier work by Fararo.