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The misfolding, aggregation, and spread of tau protein fibrils underlie tauopathies, a diverse class of neurodegenerative diseases for which effective treatments remain elusive. Among these are corticobasal dementia (CBD) and progressive supranuclear palsy (PSP), canonical examples of 4-repeat (4R) tauopathies characterized by tau isoforms exclusively with four microtubule-binding repeat domains. We target this 4R tau isoform-specific mechanism by focusing on misfolded tau’s distinctive stem-loop-stem structural motif formed by the junction of the 4R-defining alternatively spliced exon and the adjacent constitutive exon. A synthetic peptide based on this stem-loop-stem sequence can induce aggregation and spread in an isoform-specific manner. Here, we develop a protein-like polymer (PLP) in which multiple copies of this synthetic peptide form a brush-like structure capable of preventing tau aggregation by binding and capping fibril endsin vitro, in human brain organoids, and in cellular models with an EC50 of 105 ± 14 nM. PLPs demonstrate robust activity against fibrils derived from CBD and PSP patient brains and a PS19 mouse tauopathy model. Previous tau-targeted treatments have primarily focused on broad tau clearance, aggregation inhibition, or microtubule stabilization, often lacking isoform specificity and precision. In contrast, this approach targets the 4R tau isoform’s unique structural motif, offering a tailored therapeutic intervention for diseases like CBD and PSP. Supported by prior studies showing blood-brain barrier penetrance and safety profiles, this tau-binding PLP offers a promising translational path toward clinical applications in tauopathy treatment. 

Peptide-brush polymers generated by graft-through living polymerization of peptide-modified monomers exhibit high proteolytic stability, therapeutic efficacy, and potential as functional tandem repeat protein mimetics. Prior work has focused on polymers generated from structurally disordered peptides that lack defined conformations. To obtain insight into how the structure of these polymers is influenced by the folding of their peptide sidechains, a set of polymers with varying degrees of polymerization was prepared from peptide monomers that adopt α-helical secondary structure for comparison to those having random coil structures. Circular dichroism and nuclear magnetic resonance spectroscopy confirm the maintenance of the secondary structure of the constituent peptide when polymerized. Small-angle X-ray scattering (SAXS) studies reveal the solution-phase conformation of PLPs in different solvent environments. In particular, X-ray scattering shows that modulation of solvent hydrophobicity, as well as hydrogen bonding patterns of the peptide sidechain, plays an important role in the degree of globularity and conformation of the overall polymer, with polymers of helical peptide brushes showing less spherical compaction in conditions where greater helicity is observed. These structural insights into peptide brush folding and polymer conformation inform the design of these proteomimetic materials with promise for controlling and predicting their artificial fold and morphology 

Background The decision making process undertaken during wildfire responses is complex and prone to uncertainty. In the US, decisions federal land managers make are influenced by numerous and often competing factors. Aims To assess and validate the presence of decision factors relevant to the wildfire decision making context that were previously known and to identify those that have emerged since the US federal wildfire policy was updated in 2009. Methods Interviews were conducted across the US while wildfires were actively burning to elucidate time-of-fire decision factors. Data were coded and thematically analysed. Key results Most previously known decision factors as well as numerous emergent factors were identified. Conclusions To contextualise decision factors within the decision making process, we offer a Wildfire Decision Framework that has value for policy makers seeking to improve decision making, managers improving their process and wildfire social science researchers. Implications Managers may gain a better understanding of their decision environment and use our framework as a tool to validate their deliberations. Researchers may use these data to help explain the various pressures and influences modern land and wildfire managers experience. Policy makers and agencies may take institutional steps to align the actions of their staff with desired wildfire outcomes. 

Wildland firefighters must be able to maintain situational awareness to ensure their safety. Crew members, including lookouts and crew building handlines, rely on visibility to assess risk and communicate changing conditions. Geographic information systems and remote sensing offer potential solutions for characterizing visibility using models incorporating terrain and vegetation height. Visibility can be assessed using viewshed algorithms, and while previous research has demonstrated the utility of these algorithms across multiple fields, their use in wildland firefighter safety has yet to be explored. The goals of this study were to develop an approach for assessing visibility at the handline level, quantify the effects of spatial resolution on a lidar-driven visibility analysis, and demonstrate a set of spatial metrics that can be used to inform handline safety. Comparisons were made between elevation models derived from airborne lidar at varying spatial resolutions and those derived from LANDFIRE, a US-wide 30 m product. Coarser resolution inputs overestimated visibility by as much as 223%, while the finest-scale resolution input was not practical due to extreme processing times. Canopy cover and slope had strong linear relationships with visibility, with R2 values of 0.806 and 0.718, respectively. Visibility analyses, when conducted at an appropriate spatial resolution, can provide useful information to inform situational awareness in a wildland fire context. Evaluating situational awareness at the handline level prior to engaging a fire may help firefighters evaluate potential safety risks and more effectively plan handlines. 

Safety zones (SZs) are critical tools that can be used by wildland firefighters to avoid injury or fatality when engaging a fire. Effective SZs provide safe separation distance (SSD) from surrounding flames, ensuring that a fire’s heat cannot cause burn injury to firefighters within the SZ. Evaluating SSD on the ground can be challenging, and underestimating SSD can be fatal. We introduce a new online tool for mapping SSD based on vegetation height, terrain slope, wind speed, and burning condition: the Safe Separation Distance Evaluator (SSDE). It allows users to draw a potential SZ polygon and estimate SSD and the extent to which that SZ polygon may be suitable, given the local landscape, weather, and fire conditions. We begin by describing the algorithm that underlies SSDE. Given the importance of vegetation height for assessing SSD, we then describe an analysis that compares LANDFIRE Existing Vegetation Height and a recent Global Ecosystem Dynamics Investigation (GEDI) and Landsat 8 Operational Land Imager (OLI) satellite image-driven forest height dataset to vegetation heights derived from airborne lidar data in three areas of the Western US. This analysis revealed that both LANDFIRE and GEDI/Landsat tended to underestimate vegetation heights, which translates into an underestimation of SSD. To rectify this underestimation, we performed a bias-correction procedure that adjusted vegetation heights to more closely resemble those of the lidar data. SSDE is a tool that can provide valuable safety information to wildland fire personnel who are charged with the critical responsibility of protecting the public and landscapes from increasingly intense and frequent fires in a changing climate. However, as it is based on data that possess inherent uncertainty, it is essential that all SZ polygons evaluated using SSDE are validated on the ground prior to use. 

Wildfires are an essential part of a healthy ecosystem, yet the expansion of the wildland-urban interface, combined with climatic changes and other anthropogenic activities, have led to the rise of wildfire hazards in the past few decades. Managing future wildfires and their multi-dimensional impacts requires moving from traditional reactive response to deploying proactive policies, strategies, and interventional programs to reduce wildfire risk to wildland-urban interface communities. Existing risk assessment frameworks lack a unified analytical method that properly captures uncertainties and the impact of decisions across social, ecological, and technical systems, hindering effective decision-making related to risk reduction investments. In this paper, a conceptual probabilistic wildfire risk assessment framework that propagates modeling uncertainties is presented. The framework characterizes the dynamic risk through spatial probability density functions of loss, where loss can include different decision variables, such as physical, social, economic, environmental, and health impacts, depending on the stakeholder needs and jurisdiction. The proposed approach consists of a computational framework to propagate and integrate uncertainties in the fire scenarios, propagation of fire in the wildland and urban areas, damage, and loss analyses. Elements of this framework that require further research are identified, and the complexity in characterizing wildfire losses and the need for an analytical-deliberative process to include the perspectives of the spectrum of stakeholders are discussed.