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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. 

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. 

Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies. Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic Pseudomonas aeruginosa infections. 

We describe approaches, results and insights from multi-year hackathons to enable their use in soft matter training and innovation. 

Abstract The femoral neck axis serves as a critical parameter in evaluating hip joint health, particularly in the pediatric population. Commonly used metrics for evaluating femoral torsion, such as the femoral neck-shaft and femoral anteversion angles, rely heavily on precise definitions of the position and orientation of the femoral neck axis. Current measurement methods employing radiographs and performing two-dimensional (2D) measurements on computed tomography (CT) scans are susceptible to errors due to their reliance on reader experience and the inherent limitations in 2D measurements. We hypothesized that utilizing volumetric data would mitigate these errors and enable more accurate and reproducible measurements of the femoral neck axis using the femoral anteversion and femoral neck-shaft angles. To test this hypothesis, we analyzed a historical collection of postmortem infant femoral and pelvic bones (28 hips) aged 0 to 6.5 months, with an average estimated age of 4.68 ± 1.80 months. Our findings revealed an average neck-shaft angle of 128.00 ± 4.92 deg and femoral anteversion angle of 35.56 ± 11.68 deg across all femurs, consistent with literature values. These measurements obtained from volumetric image data were found to be repeatable and reliable compared to conventional methods. Our study suggests that the proposed methodology offers a standardized approach for obtaining repeatable and reproducible measurements, thus potentially enhancing diagnostic accuracy and clinical decision-making in assessing hip developmental conditions in pediatric patients.