Search for: All records

Objective: This paper demonstrates computational modeling’s value as a tool for mapping the impact of hidden variables and evaluating the accuracy of statistical methods in bioarchaeology. Materials: As a working example, this paper presents an agent-based model of a 1,000-person cohort of in- dividuals who can form an unspecified skeletal lesion at any age between birth and ten years and enter a simulated cemetery at the end of their lives. Skeletal lesions either have no effect on mortality risk (scenario 1) or are associated with doubled mortality risk (scenario 2). Methods: The agent-based model simulates data on individual age at death and lesion status. Kaplan-Meier survival analysis is run on each simulated dataset, comparing survival estimates for individuals with and without lesions. Results: Survival analyses underestimate the true value of lesion-associated mortality risk in early life in scenario 2 and produce a false lesion-associated survival advantage under the null conditions of scenario 1. Conclusions: Researchers should account for the ages of a skeletal lesion’s developmental window, where known, when assessing lesion-associated mortality. Survival analyses return accurate results when they exclude in- dividuals in the ages of active lesion formation. Significance: Modeling experiments can identify which archaeologically unmeasurable variables have the greatest impact on estimates of population health and outline the ways in which they bias estimates of past health from the skeletal record. Limitations: The only limits on modeling are limits of imagination and common sense. Suggestions for future research: Many other archaeologically hidden variables remain to be explored with this approach. 

Abstract Changes in brain mitochondrial metabolism are coincident with functional decline; however, direct links between the two have not been established. Here, we show that mitochondrial targeting via the adiponectin receptor activator AdipoRon (AR) clears neurofibrillary tangles (NFTs) and rescues neuronal tauopathy-associated defects. AR reduced levels of phospho-tau and lowered NFT burden by a mechanism involving the energy-sensing kinase AMPK and the growth-sensing kinase GSK3b. The transcriptional response to AR included broad metabolic and functional pathways. Induction of lysosomal pathways involved activation of LC3 and p62, and restoration of neuronal outgrowth required the stress-responsive kinase JNK. Negative consequences of NFTs on mitochondrial activity, ATP production, and lipid stores were corrected. Defects in electrophysiological measures (e.g., resting potential, resistance, spiking profiles) were also corrected. These findings reveal a network linking mitochondrial function, cellular maintenance processes, and electrical aspects of neuronal function that can be targeted via adiponectin receptor activation. 

The impact of wildfire on soil properties is difficult to predict, partially due to a shortage of field observations. To help address this need, we have assembled a unique dataset of soil properties (moisture, infiltration rate, and water drop penetration time) at over 100 individual locations within Yosemite National Park. These locations cover a wide range of fire history, soil texture, vegetation cover, and topography. Measurements span May 2022 through July 2023, capturing both a dry year and a wet year. A subset of sites burned in late summer 2022, allowing for pre- and post-fire measurements. Each individual site was measured 1-3 separate times. Water drop penetration time was measured at the soil surface, 1cm, and 3cm depths. Infiltration rate was measured at the soil surface and at 3cm depth using a mini disk infiltrometer. 

Abstract The circumgalactic medium (CGM) is a reservoir of metals and star-forming fuel. Most baryons in the Universe are in the CGM or the intergalactic medium (IGM). The baryon cycle—how mass and metals reach the CGM from the inner regions of the galaxy and how gas from the CGM replenishes star-forming activity in the inner regions—is an essential question in galaxy evolution. In this paper, we study the flow of mass and metals in a stacked sample of 2770 isolated halos from the IllustrisTNG100 cosmological hydrodynamic simulation. The mean gas flow as a function of radius and angle is similar across a large galactic mass range when accounting for different feedback modes. Although both star formation and black holes cause powerful outflows, the flows from star formation are more angularly restricted. Black hole feedback dominates mass flow throughout the halo, while star formation feedback mainly affects the inner region. When scaling by virial radius (R_v), large dynamical changes occur at 0.2R_vfor most halos, suggesting a characteristic size for the inner galaxy. Despite kinetic-mode feedback from black holes being the primary quenching mechanism in IllustrisTNG, a small population of high-mass kinetic-mode disks are able to form stars. 

Graphs are powerful tools for modeling and analyzing molecular interaction networks. Graphs typically represent either undirected physical interactions or directed regulatory relationships, which can obscure a particular protein’s functional context. Graphlets can describe local topologies and patterns within graphs, and combining physical and regulatory interactions offer new graphlet configurations that can provide biological insights. We present GRPhIN, a tool for characterizing graphlets and protein roles within graphlets in mixed physical and regulatory interaction networks. We describe the graphlets of mixed networks in B. subtilis, C. elegans, D. melanogaster, D. rerio, and S. cerevisiae and examine local topologies of proteins and subnetworks related to the oxidative stress response pathway. We found a number of graphlets that were abundant in all species, specific node positions (orbits) within graphlets that were over-represented in stress-associated proteins, and rarely-occurring graphlets that were over-represented in oxidative stress subnetworks. These results showcase the potential for using graphlets in mixed physical and regulatory interaction networks to identify new patterns beyond a single interaction type.