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Network theory analysis has emerged as a powerful approach for investigating the complex behavior of dynamic and interactive systems, including proteomic systems. One key application of these methods is the study of long-range signaling dynamics in proteins, a phenomenon known as allostery. In this study, we applied computational models using network theory analysis to explore long-range electrostatic interactions and allosteric drug rescue mechanisms in the DNA-binding domain (DBD) of the p53 protein, a critical tumor suppressor whose dysfunction, often caused by missense mutations, is implicated in over 50% of human cancers. Using heat kernel and Wasserstein distance-based analyses, we explored the allosteric behavior of p53-DBD constructs with the Y220C mutation in the presence or absence of allosteric effector drugs. Our results demonstrated that these network theory-based protocols effectively detected the differential efficacies of small molecule allosteric effector drug compounds in restoring long-range electrostatic dynamics in the Y220C mutant. Furthermore, our approach identified key long-range electrostatic interactions critical to both the nominal and drug-rescued functionality of the p53-DBD, providing valuable insights into allosteric modulation and its therapeutic potential. 

The link between p53 tumor suppressive functions and organismal lifespan is multifaceted. Its DNA-repair mechanism is longevity-enhancing while its role in cellular senescence pathways induces pro-aging phenotypes. To understand how p53 may regulate organismal lifespan, cross-species genotype-phenotype (GP) studies of the p53 DNA-binding domain (DBD) have been used to assess the correlation of amino acid changes to lifespan. Amino acid changes in non-DNA-binding regions such as the transactivation (TAD), proline-rich (PRD), regulatory (REG), and tetramerization (TET) are largely unexplored. In addition, existing GP correlation tools such as SigniSite do not account for phylogenetic relationships between aligned sequences in correlating genotypic differences to phenotypes such as lifespan. To identify phylogenetically significant, longevity-correlated residues in full-length p53 alignments, we developed a Python- and R-based workflow, Relative Evolutionary Scoring (RES). While RES-predicted longevity-associated residues (RPLARs) are concentrated primarily in the DBD, the PRD, TET, and REG domains also house RPLARs. While yeast functional assay enrichment reveals that RPLARs may be dispensable for p53-mediated transactivation, PEPPI and Rosetta-based protein-protein interaction prediction suggests a role for RPLARs in p53 stability and interaction interfaces of tumor suppressive protein-protein complexes. With experimental validation of the RPLARs’ roles in p53 stability, transactivation, and involvement in senescence-regulatory pathways, we can gain crucial insights into mechanisms underlying dysregulated tumor suppression and accelerated aging. 

Introductory biology for majors is one of the most consequential courses in STEM, with annual enrollments of several hundred thousand students in the United States alone. To support increased student success and meet current and projected needs for qualified STEM professionals, it will be crucial to redesign majors biology by using explicit learning objectives (LOs) that can be aligned with assessments and active learning exercises. When a course is designed in this way, students have opportunities for the practice and support they need to learn, and instructors can collect the evidence they need to evaluate whether students have mastered key concepts and skills. Following an iterative process of review, revision, and evaluation, which included input from over 800 biology instructors around the country, we produced a nationally endorsed set of lesson-level LOs for a year-long introductory biology for major’s course. These LOs are granular enough to support individual class sessions and provide instructors with a framework for course design that is directly connected to the broad themes inVision and Changeand the general statements in the BioCore and BioSkills Guides. Instructors can implement backward course design by aligning these community endorsed LOs with daily and weekly learning activities and with formative and summative assessments. 

Evaluating two‐terminal network reliability is a classical problem with numerous applications. Because this problem is #P‐Complete, practical studies involving large systems commonly resort to approximating or estimating system reliability rather than evaluating it exactly. Researchers have characterized signatures, such as the destruction spectrum and survival signature, which summarize the system's structure and give rise to procedures for evaluating or approximating network reliability. These procedures are advantageous if the signature can be computed efficiently; however, computing the signature is challenging for complex systems. With this motivation, we consider the use of Monte Carlo (MC) simulation to estimate the survival signature of a two‐terminal network in which there are two classes of i.i.d. components. In this setting, we prove that each MC replication to estimate the signature of a multi‐class system entails solving a multi‐objective maximum capacity path problem. For the case of two classes of components, we adapt a Dijkstra's‐like bi‐objective shortest path algorithm from the literature for the purpose of solving the resulting bi‐objective maximum capacity path problem. We perform computational experiments to compare our method's efficiency against intuitive benchmark approaches. Our computational results demonstrate that the bi‐objective optimization approach consistently outperforms the benchmark approaches, thereby enabling a larger number of MC replications and improved accuracy of the reliability estimation. Furthermore, the efficiency gains versus benchmark approaches appear to become more significant as the network increases in size. 

Abstract This field study examined how sediment macroinfauna change patterns of sediment oxygen demand (SOD) throughout a diel oxygen cycle. Sediments with a greater faunal presence would be expected to have greater overall SOD, and at night may alter their behavior and influence SOD depending on their response to low-oxygen stress. Dynamic faunal bioturbation or bioirrigation behavior would also result in corresponding variation in SOD values on short time scales. In situ flow-through benthic metabolism chambers were used to measure SOD at a high temporal resolution in discrete sediment patches. Sediments with more macroinfauna had greater average SOD over the diel cycle, consistent with previous studies. Where more macroinfauna were present, they drove greater SOD during nightly low oxygen, presumably by enhancing their burrowing and irrigation activities. SOD was also more variable on a sub-diel timescale in sediments with more macroinfauna. Sediment oxygen demand is dynamic and highly sensitive both temporally, on very short timescales, and spatially, in terms of resident fauna, and their interaction produces heretofore unaccounted complexity in patterns of SOD particularly in shallow coastal systems. Extrapolations of temporally and spatially limited SOD measurements to a system-wide scale that do not account for the short-term and spatially variable effects of fauna may produce imprecise and misleading estimates of this critical ecosystem function. 

Abstract Muddy marine sediments are elastic materials in which bubbles grow and worms extend their burrows by fracture. Bubble growth and burrowing behavior are dependent on the stiffness and fracture toughness (K_Ic) of these muds. This article describes a custom laboratory apparatus to measure the fracture toughness of muddy, cohesive sediments using a bubble injection method. The system induces fracture in sediment samples by incrementally injecting air through a needle inserted into the sediment. The increasing pneumatic pressure is monitored until it drops abruptly, indicating bubble formation. Fracture toughness is then calculated from the peak pressure at which fracture occurred, following cavitation rheology methods developed for soft gels. The system has produced measurements that compare well to previous data but with better spatial resolution, allowing for characterization of spatial heterogeneity on small scales. 

Data-driven many-body simulations provide the first realistic view of water harvesting in metal–organic frameworks as a function of relative humidity.