Search for: All records

Abstract Multiscale geographically weighted regression (MGWR) extends geographically weighted regression (GWR) by allowing process heterogeneity to be modeled at different spatial scales. While MGWR improves parameter estimates compared to GWR, the relationship between spatial scale and correlations within and among covariates—specifically spatial autocorrelation and collinearity—has not been systematically explored. This study investigates these relationships through controlled simulation experiments. Results indicate that spatial autocorrelation and collinearity affect specific model components rather than the entire model. Their impacts are cumulative but remain minimal unless they become very strong. MGWR effectively mitigates local multicollinearity issues by applying varying bandwidths across parameter surfaces. However, high levels of spatial autocorrelation and collinearity can lead to bandwidth underestimation for global processes, potentially producing false local effects. Additionally, strong collinearity may cause bandwidths to be overestimated for some processes, which helps mitigate collinearity but may obscure local effects. These findings suggest that while MGWR offers greater robustness against multicollinearity compared to GWR, bandwidth estimates should be interpreted with caution, as they can be influenced by strong spatial autocorrelation and collinearity. These results have important implications for empirical applications of MGWR. 

In order to promote justice-centered making in STEAM classrooms, the NASA Landsat-based Science and Technology Education for Land/Life Assessment (STELLA) instrument is proposed as an affordable educational tool for students to collect and analyze data pertaining to vegetation health, surface temperature, and air quality. This instrument can be used to investigate justice-centered, community-based problems and promote civic engagement toward policy change for a healthier world. Our study applies the MakerTPICK theoretical framework to a qualitative study to explore changes in teacher beliefs about the STELLA instrument following three justice-centered STEAM-making activities pertaining to urban heat islands, air quality, and vegetation health. The implications of this research can be used to inform professional development and promote justice-centered learning in the STEAM classroom. 

The etching of ZnO thin films using acetylacetone (Hacac) doses with long exposure times, followed by purging and subsequent exposure to O2 plasma, is studied in a hot-wall reactor using simultaneous in situ spectroscopic ellipsometry and quadrupole mass spectrometry. The static exposure step results in the efficient consumption of Hacac. For each etch cycle, the O2 plasma plays a crucial role in removing unreacted Hacac from the ZnO surface, priming the surface for subsequent Hacac etching. This is confirmed by the production of CO2 during the O2 plasma pulse. The temperature window for etching is established as 220–280 °C with a maximum etch per cycle (EPC) of 0.15 nm/cy. Under these conditions, the Hacac pulse is 2 s long with a 30 s static hold step followed by 5 s O2 plasma step at 300 W power. Statistical analyses of etch data at the granularity level of each cycle reveal the importance of the static hold step in determining EPC. Arrhenius behavior of etching during the hold step reveals a piecewise linear trend with a low temperature (120–200 °C) activation energy (Ea) of 202 meV and a high temperature (200–320 °C) Ea of 32 meV. It is shown that saturation behavior in EPC is pulse time and static hold time dependent. Shorter Hacac pulses (≤1 s) demonstrate saturation behavior for static hold times ∼30 s, longer pulses of Hacac (≥2 s) show no saturation in EPC with static hold times up to 75 s. 

In this session, we will share our model for K-5 STEM teacher leadership. 

ABSTRACT Spatial organization of pathway enzymes has emerged as a promising tool to address several challenges in metabolic engineering, such as flux imbalances and off-target product formation. Bacterial microcompartments (MCPs) are a spatial organization strategy used natively by many bacteria to encapsulate metabolic pathways that produce toxic, volatile intermediates. Several recent studies have focused on engineering MCPs to encapsulate heterologous pathways of interest, but how this engineering affects MCP assembly and function is poorly understood. In this study, we investigated the role of signal sequences, short domains that target proteins to the MCP core, in the assembly of 1,2-propanediol utilization (Pdu) MCPs. We characterized two novel Pdu signal sequences on the structural proteins PduM and PduB, which constitute the first report of metabolosome signal sequences on structural proteins rather than enzymes. We then explored the role of enzymatic and structural Pdu signal sequences on MCP assembly by deleting their encoding sequences from the genome alone and in combination. Deleting enzymatic signal sequences decreased the MCP formation, but this defect could be recovered in some cases by overexpressing genes encoding the knocked-out signal sequence fused to a heterologous protein. By contrast, deleting structural signal sequences caused similar defects to knocking out the genes encoding the full-length PduM and PduB proteins. Our results contribute to a growing understanding of how MCPs form and function in bacteria and provide strategies to mitigate assembly disruption when encapsulating heterologous pathways in MCPs.IMPORTANCESpatially organizing biosynthetic pathway enzymes is a promising strategy to increase pathway throughput and yield. Bacterial microcompartments (MCPs) are proteinaceous organelles that many bacteria natively use as a spatial organization strategy to encapsulate niche metabolic pathways, providing significant metabolic benefits. Encapsulating heterologous pathways of interest in MCPs could confer these benefits to industrially relevant pathways. Here, we investigate the role of signal sequences, short domains that target proteins for encapsulation in MCPs, in the assembly of 1,2-propanediol utilization (Pdu) MCPs. We characterize two novel signal sequences on structural proteins, constituting the first Pdu signal sequences found on structural proteins rather than enzymes, and perform knockout studies to compare the impacts of enzymatic and structural signal sequences on MCP assembly. Our results demonstrate that enzymatic and structural signal sequences play critical but distinct roles in Pdu MCP assembly and provide design rules for engineering MCPs while minimizing disruption to MCP assembly. 

Abstract Hyporheic zones regulate biogeochemical processes in streams and rivers, but high spatiotemporal heterogeneity makes it difficult to predict how these processes scale from individual reaches to river basins. Recent work applying allometric scaling (i.e., power‐law relationships between size and function) to river networks provides a new paradigm for understanding cumulative hyporheic biogeochemical processes. We used previously published model predictions of reach‐scale hyporheic aerobic respiration to explore patterns in allometric scaling across two climatically divergent basins with differing characteristics in the Pacific Northwest, United States. In the model, hydrologic exchange fluxes (HEFs) regulate hyporheic respiration, so we examined how HEFs might influence allometric scaling of respiration. We found consistent scaling behaviors where HEFs were either very low or very high, but differences between basins when HEFs were moderate. Our findings provide initial model‐generated hypotheses for factors influencing allometric scaling of hyporheic respiration. These hypotheses can be used to optimize new data generation efforts aimed at developing predictive understanding of allometries that can, in turn, be used to scale biogeochemical dynamics across watersheds. 

Early to intermediate ontogenetic stages of trees are important in forest regeneration. However, these critical life stages are often overlooked due to survey intensity and impracticality and/or disinterest in characterizing early life stage cohorts. This problem is particularly pervasive in mangrove forests where visibility of smaller stature trees may be limited by tidal flooding and younger cohorts are particularly vulnerable to changing hydrologic and biogeochemical conditions driven by climate change. Lacking data on early life stages in mangrove forests makes it difficult to predict ecosystem degradation and inform habitat resilience and restoration in one of the earth's most valuable blue carbon ecosystems. We identify challenges to collecting empirical data on early to intermediate age classes in mangroves and provide solutions to characterizing these cohorts. We emphasize the importance of gathering these data for improved understanding of forest regeneration dynamics and provide multi-scalar solutions to quantify vegetation structure of mangrove forest.