Search for: All records

Abstract Resource management in engineering design seeks to optimally allocate while maximizing the performance metrics of the final design. Bayesian optimization (BO) is an efficient design framework that judiciously allocates resources through heuristic-based searches, aiming to identify the optimal design region with minimal experiments. Upon recommending a series of experiments or tasks, the framework anticipates their completion to augment its knowledge repository, subsequently guiding its decisions toward the most favorable next steps. However, when confronted with time constraints or other resource challenges, bottlenecks can hinder the traditional BO’s ability to assimilate knowledge and allocate resources with efficiency. In this work, we introduce an asynchronous learning framework designed to utilize idle periods between experiments. This model adeptly allocates resources, capitalizing on lower fidelity experiments to gather comprehensive insights about the target objective function. Such an approach ensures that the system progresses uninhibited by the outcomes of prior experiments, as it provisionally relies on anticipated results as stand-ins for actual outcomes. We initiate our exploration by addressing a basic problem, contrasting the efficacy of asynchronous learning against traditional synchronous multi-fidelity BO. We then employ this method to a practical challenge: optimizing a specific mechanical characteristic of a dual-phase steel. 

A variety of cellular processes use liquid–liquid phase separation (LLPS) to create functional levels of organization, but the kinetic pathways by which it proceeds remain incompletely understood. Here in real time, we monitor the dynamics of LLPS of mixtures of segregatively phase-separating polymers inside all-synthetic, giant unilamellar vesicles. After dynamically triggering phase separation, we find that the ensuing relaxation—en route to the new equilibrium—is non-trivially modulated by a dynamic interplay between the coarsening of the evolving droplet phase and the interactive membrane boundary. The membrane boundary is preferentially wetted by one of the incipient phases, dynamically arresting the progression of coarsening and deforming the membrane. When the vesicles are composed of phase-separating mixtures of common lipids, LLPS within the vesicular interior becomes coupled to the membrane’s compositional degrees of freedom, producing microphase-separated membrane textures. This coupling of bulk and surface phase-separation processes suggests a physical principle by which LLPS inside living cells might be dynamically regulated and communicated to the cellular boundaries. 

Abstract Evolutionary processes driving physiological trait variation depend on the underlying genomic mechanisms. Evolution of these mechanisms depends on the genetic complexity (involving many genes) and how gene expression impacting the traits is converted to phenotype. Yet, genomic mechanisms that impact physiological traits are diverse and context dependent (e.g., vary by environment and tissues), making them difficult to discern. We examine the relationships between genotype, mRNA expression, and physiological traits to discern the genetic complexity and whether the gene expression affecting the physiological traits is primarily cis- or trans-acting. We use low-coverage whole genome sequencing and heart- or brain-specific mRNA expression to identify polymorphisms directly associated with physiological traits and expressed quantitative trait loci (eQTL) indirectly associated with variation in six temperature specific physiological traits (standard metabolic rate, thermal tolerance, and four substrate specific cardiac metabolic rates). Focusing on a select set of mRNAs belonging to co-expression modules that explain up to 82% of temperature specific traits, we identified hundreds of significant eQTL for mRNA whose expression affects physiological traits. Surprisingly, most eQTL (97.4% for heart and 96.7% for brain) were trans-acting. This could be due to higher effect size of trans- versus cis-acting eQTL for mRNAs that are central to co-expression modules. That is, we may have enhanced the identification of trans-acting factors by looking for single nucleotide polymorphisms associated with mRNAs in co-expression modules that broadly influence gene expression patterns. Overall, these data indicate that the genomic mechanism driving physiological variation across environments is driven by trans-acting heart- or brain-specific mRNA expression. 

Two new, cross-linkable, phosphobetaine zwitterionic amphiphiles were found to form a type II bicontinuous cubic (Q_II) phase with aq. NH₄Cl. Upon polymerization, they form 3D-nanoporous membrane materials that are not susceptible to ion exchange. 

Climate change risks like extreme temperatures and high variability in rainfall adversely affect livelihoods, particularly for farmers in Burkina Faso where the primary sector is agriculture. Decisions on whether to adapt to these risks depend on how farmers perceive each risk and the resources they have available. In this study, we examine how long-term changes in temperature and rainfall are perceived by farmers in Burkina Faso. We also compare the extent to which these perceptions align with actual recorded changes in temperature and rainfall for multiple periods between 1991 and 2014. We use a logistic regression model to analyze the role of resources, such as asset ownership and perceived standards of living, along with household size, age, and gender of the household head to explain differences in perception and ultimately the decision to adapt. Our results show that the vast majority of farmers in Burkina Faso perceive changes in temperature and rainfall; however, only about half of those individuals perceive changes in ways that align with recorded long-term trends in their local temperature or rainfall. The extent to which those perceptions align with recorded changes depends on the time frame selected. Older farmers and those with assets were less likely to perceive temperature and rainfall trends in ways that aligned with climate records; however, farmers' perceptions of temperature change aligning with records and their perceived standard of living were both associated with the decision to adapt. This misalignment of perceptions with records and resources has significant implications for efforts to inform and support climate risk mitigation and adaptation. 

Abstract Physiological trait variation underlies health, responses to global climate change, and ecological performance. Yet, most physiological traits are complex, and we have little understanding of the genes and genomic architectures that define their variation. To provide insight into the genetic architecture of physiological processes, we related physiological traits to heart and brain mRNA expression using a weighted gene co-expression network analysis. mRNA expression was used to explain variation in six physiological traits (whole animal metabolism (WAM), critical thermal maximum (CT max ), and four substrate specific cardiac metabolic rates (CaM)) under 12 °C and 28 °C acclimation conditions. Notably, the physiological trait variations among the three geographically close (within 15 km) and genetically similar F. heteroclitus populations are similar to those found among 77 aquatic species spanning 15–20° of latitude (~ 2,000 km). These large physiological trait variations among genetically similar individuals provide a powerful approach to determine the relationship between mRNA expression and heritable fitness related traits unconfounded by interspecific differences. Expression patterns explained up to 82% of metabolic trait variation and were enriched for multiple signaling pathways known to impact metabolic and thermal tolerance ( e.g. , AMPK, PPAR, mTOR, FoxO, and MAPK) but also contained several unexpected pathways ( e.g. , apoptosis, cellular senescence), suggesting that physiological trait variation is affected by many diverse genes.