A Return to Biased Nets: New Specifications and Approximate Bayesian Inference.

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.

Production of Distinct Fibrillar, Oligomeric, and Other Aggregation States from Network Models of Multibody Interaction.

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.

Comparative analysis of thermal adaptations of extremophilic prolyl oligopeptidases.

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.

California Exodus? A Network Model of Population Redistribution in the United States.

Motivated by debates about California's net migration loss, we employ valued exponential-family random graph models to analyze the inter-county migration flow networks in the United States. We introduce a protocol that visualizes the complex effects of potential underlying mechanisms, and perform knockout experiments to quantify their contribution to the California Exodus. We find that racial dynamics contribute to the California Exodus, urbanization ameliorates it, and political climate and housing costs have little impact.

Continuous Time Graph Processes with Known ERGM Equilibria: Contextual Review, Extensions, and Synthesis.

Graph processes that unfold in continuous time are of obvious theoretical and practical interest. Particularly useful are those whose long-term behavior converges to a graph distribution of known form. Here, we review some of the conditions for such convergence, and provide examples of novel and/or known processes that do so.

Privacy by Projection: Federated Population Density Estimation by Projecting on Random Features.

We consider the problem of population density estimation based on location data crowdsourced from mobile devices, using kernel density estimation (KDE). In a conventional, centralized setting, KDE requires mobile users to upload their location data to a server, thus raising privacy concerns. Here, we propose a Federated KDE framework for estimating the user population density, which not only keeps location data on the devices but also provides probabilistic privacy guarantees against a server that tries to infer users' location.

Spatially resolved detection of small molecules from press-dried plant tissue using MALDI imaging.

Premise: Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a chemical imaging method that can visualize spatial distributions of particular molecules. Plant tissue imaging has so far mostly used cryosectioning, which can be impractical for the preparation of large-area imaging samples, such as full flower petals. Imaging unsectioned plant tissue presents its own difficulties in extracting metabolites to the surface due to the waxy cuticle.

Comparative Modeling and Analysis of Extremophilic D-Ala-D-Ala Carboxypeptidases.

Understanding the molecular adaptations of organisms to extreme environments requires a comparative analysis of protein structure, function, and dynamics across species found in different environmental conditions. Computational studies can be particularly useful in this pursuit, allowing exploratory studies of large numbers of proteins under different thermal and chemical conditions that would be infeasible to carry out experimentally. Here, we perform such a study of the MEROPS family S11, S12, and S13 proteases from psychophilic, mesophilic, and thermophilic bacteria.

Mutation Effects on Structure and Dynamics: Adaptive Evolution of the SARS-CoV-2 Main Protease.

The main protease of SARS-CoV-2 (M) plays a critical role in viral replication; although it is relatively conserved, M has nevertheless evolved over the course of the COVID-19 pandemic. Here, we examine phenotypic changes in clinically observed variants of M, relative to the originally reported wild-type enzyme. Using atomistic molecular dynamics simulations, we examine effects of mutation on protein structure and dynamics.

Network Hamiltonian Models for Unstructured Protein Aggregates, with Application to γD-Crystallin.

Network Hamiltonian models (NHMs) are a framework for topological coarse-graining of protein-protein interactions, in which each node corresponds to a protein, and edges are drawn between nodes representing proteins that are noncovalently bound. Here, this framework is applied to aggregates of γD-crystallin, a structural protein of the eye lens implicated in cataract disease. The NHMs in this study are generated from atomistic simulations of equilibrium distributions of wild-type and the cataract-causing variant W42R in solution, performed by Wong, E.

Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices.

The exponential family random graph modeling (ERGM) framework provides a highly flexible approach for the statistical analysis of networks (i.e., graphs).

Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts.

The Moderating Role of Context: Relationships between Individual Behaviors and Social Networks.

A social context can be viewed as an entity or unit around which a group of individuals organize their activities and interactions. Social contexts take such diverse forms as families, dwelling places, neighborhoods, classrooms, schools, workplaces, voluntary organizations, and sociocultural events or milieus. Understanding social contexts is essential for the study of individual behaviors, social networks, and the relationships between the two.

Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures.

Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail-an effect that is especially acute for topological representations such as protein structure networks (PSNs).

Bayesian estimation of the hydroxyl radical diffusion coefficient at low temperature and high pressure from atomistic molecular dynamics.

The hydroxyl radical is the primary reactive oxygen species produced by the radiolysis of water and is a significant source of radiation damage to living organisms. Mobility of the hydroxyl radical at low temperatures and/or high pressures is hence a potentially important factor in determining the challenges facing psychrophilic and/or barophilic organisms in high-radiation environments (e.g.

Bayesian analysis of static light scattering data for globular proteins.

Static light scattering is a popular physical chemistry technique that enables calculation of physical attributes such as the radius of gyration and the second virial coefficient for a macromolecule (e.g., a polymer or a protein) in solution.

Insight into Selecting Adolescents for Drinking Intervention Programs: a Simulation Based on Stochastic Actor-Oriented Models.

Adolescent drinking remains a prominent public health and socioeconomic issue in the USA with costly consequences. While numerous drinking intervention programs have been developed, there is little guidance whether certain strategies of participant recruitment are more effective than others. The current study aims at addressing this gap in the literature using a computer simulation approach, a more cost-effective method than employing actual interventions.

A cyclic peptide inhibitor of the SARS-CoV-2 main protease.

This paper presents the design and study of a first-in-class cyclic peptide inhibitor against the SARS-CoV-2 main protease (M). The cyclic peptide inhibitor is designed to mimic the conformation of a substrate at a C-terminal autolytic cleavage site of M. The cyclic peptide contains a [4-(2-aminoethyl)phenyl]-acetic acid (AEPA) linker that is designed to enforce a conformation that mimics a peptide substrate of M.

Cutting Through the Noise: Predictors of Successful Online Message Retransmission in the First 8 Months of the COVID-19 Pandemic.

In this paper, we investigate how message construction, style, content, and the textual content of embedded images impacted message retransmission over the course of the first 8 months of the coronavirus disease 2019 (COVID-19) pandemic in the United States. We analyzed a census of public communications (n = 372,466) from 704 public health agencies, state and local emergency management agencies, and elected officials posted on Twitter between January 1 and August 31, 2020, measuring message retransmission via the number of retweets (ie, a message passed on by others), an important indicator of engagement and reach. To assess content, we extended a lexicon developed from the early months of the pandemic to identify key concepts within messages, employing it to analyze both the textual content of messages themselves as well as text included within embedded images (n = 233,877), which was extracted via optical character recognition.

A multi-contextual examination of non-school friendships and their impact on adolescent deviance and alcohol use.

Despite decades of research on adolescent friendships, little is known about adolescents who are more likely to form ties outside of school. We examine multiple social and ecological contexts including parents, the school, social networks, and the neighborhood to understand the origins and health significance of out of school ties using survey data from the National Longitudinal Study of Adolescent to Adult Health (N = 81,674). Findings indicate that out of school (more than in-school) friendships drive adolescent deviance and alcohol use, and youth with such friends tend to be involved in school activities and are central among their peer group.

#Ebola: Emergency Risk Messages on Social Media.

Public health threats require effective communication. Evaluating effectiveness during a situation that requires emergency risk communication is difficult, however, because these events require an immediate response and collecting data may be secondary to more immediate needs. In this article, we draw on research analyzing the effectiveness of social media messages during times of imminent threat and research analyzing the emergency risk communication conceptual model in order to propose a method for evaluating emergency risk communication on social media.

The First 60 Days: American Public Health Agencies' Social Media Strategies in the Emerging COVID-19 Pandemic.

In this paper, we capture, identify, and describe the patterns of longitudinal risk communication from public health communicating agencies on Twitter during the first 60 days of the response to the novel coronavirus disease 2019 (COVID-19) pandemic. We collected 138,546 tweets from 696 targeted accounts from February 1 to March 31, 2020, employing term frequency-inverse document frequency to identify keyword hashtags that were distinctive on each day. Our team conducted inductive content analysis to identify emergent themes that characterize shifts in public health risk communication efforts.

Network Hamiltonian models reveal pathways to amyloid fibril formation.

Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer's disease and prion diseases. Despite an ever growing collection of amyloid fibril structures found in the Protein Data Bank (PDB) and numerous clinical trials, therapeutic strategies remain elusive. One contributing factor to the lack of progress on this challenging problem is incomplete understanding of the mechanisms by which these locally ordered protein aggregates self-assemble in solution.

COVID-19: Retransmission of official communications in an emerging pandemic.

As the most visible face of health expertise to the general public, health agencies have played a central role in alerting the public to the emerging COVID-19 threat, providing guidance for protective action, motivating compliance with health directives, and combating misinformation. Social media platforms such as Twitter have been a critical tool in this process, providing a communication channel that allows both rapid dissemination of messages to the public at large and individual-level engagement. Message dissemination and amplification is a necessary precursor to reaching audiences, both online and off, as well as inspiring action.

Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease.

The SARS-CoV-2 main protease (M) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new M mutations arising over time. Identification and structural characterization of M variants is thus critical for robust inhibitor design.