Tetra Tech (United States)

Underground coal fires (UCFs) pose a persistent environmental and economic threat to both the built and natural worlds. In Colorado, 38 known coal fires are currently monitored by the Colorado Division of Reclamation, Mining, and Safety, many of which are in the immediate vicinity of communities and transportation infrastructure. The Axial underground coal mine fire in northwestern Colorado has been active for over 70 years and has a documented history of surface impacts, including wildfire ignition and UCF-induced slope instability near a major highway corridor. Subsurface investigations indicate active combustion in multiple coal seams, contributing to complex and evolving surface deformation. Unmanned Aerial System (UAS)-based optical surveys acquired between 2018 and 2025 were used to assess terrain changes and slope instability at the Axial site. Structure-from-motion photogrammetry was used to generate three-dimensional point clouds and orthomosaics, and surface deformation was quantified using the Multiscale Model-to-Model Cloud Comparison (M3C2) algorithm. Orthomosaic products were additionally evaluated to characterize the development of geomorphic features and cross-validate the interpretation of M3C2-derived deformation patterns. Repeat UAS surveys effectively identified changes in unstable and hazardous terrain caused by UCFs. Results reveal progressive subsidence, fracture development, and localized slope instability associated with ongoing subsurface combustion. The findings provide critical information for risk mitigation and illustrate both the capabilities and challenges of using UAS photogrammetry for long-term monitoring of geohazards associated with legacy coal mine fires. The study further emphasizes the importance of georeferencing strategies, including ground control points and real-time kinematic positioning, to ensure consistent and reliable multi-temporal change detection.

Biochemical reactions involving phosphatidylinositol phosphate (PIP) lipids and small GTPases serve essential roles in signal transduction immediately downstream of cell surface receptor activation. Interconnected positive and negative feedback loops link these distinct classes of reactions and allow for the rapid establishment of the steep and precisely oriented intracellular activity gradients that are hallmarks of cell polarity. Although genetic approaches have identified numerous molecules that potentially regulate feedback between GTPases and PIP lipids in cells, it is unclear how different types of feedback modulate the spatiotemporal dynamics of membrane signaling reactions. Here, we reconstitute communication and feedback between Ras GTPase and phosphatidylinositol 3-kinase gamma (PI3Kγ)-mediated generation of PIP3 on supported membranes. We employ light-induced membrane recruitment of a guanine nucleotide exchange factor (GEF) to rapidly and reversibly shift steady-state conditions and monitor corresponding Ras activation and PIP3 production in time and space. Alone, the Ras-PI3Kγ module exhibits weak and transient activation due to global inhibitors. However, the introduction of GEF-mediated positive feedback drives local amplification of Ras activation and PIP3 generation, resulting in a wave of activity that propagates across the membrane surface. Spatial coupling between activated Ras and PIP3 molecules depends on their respective diffusion coefficients and the species involved in the feedback circuit. This work identifies activation thresholds, membrane diffusion, and feedback architecture as key factors that determine how small GTPases and PIP lipid modifying enzymes amplify membrane signaling reactions in the presence of global inhibitors.

AimLow carbohydrate, ketogenic foods have shown convincing evidence for their metabolic role in mitigating severe adversities due to obesity and other chronic diseases. They induce systemic ketosis: a process where ketone bodies, namely β-hydroxybutyrate, acetoacetate and acetone are produced in vivo. Beyond serving as an alternative source of energy besides glucose, various analogs of ketones present unprecedented opportunities for therapeutic interventions in the management of numerous chronic diseases and neurological disorders. The profound benefits of ketone bodies to human health, unquestionably, demand exogenous administration of ketone molecules in doses that promote and enhance energy levels in the human body. Hence, it is of paramount importance to develop sophisticated delivery vehicles wherein ketones are made bioavailable in a sustainable fashion in vivo. Engineering nano-formulations of ketone molecules allow efficient cellular penetration of ketones, thus presenting prospects for enhanced bioavailability of energy molecules in vivo. In this article, we report nanoencapsulation of (R)-3-hydroxybutyrate monoglyceride, a Ketone Molecule (KM) within biocompatible pea protein nano-framework utilizing natural phytochemical crosslinking.PurposeThe goal was to develop a sophisticated delivery vehicle wherein ketones are made bioavailable in a sustainable and biocompatible fashion.MethodsWe present full details on the production of well-defined Ketone Molecule (KM) encapsulated nanoparticles of pea protein using naturally available crosslinking agents such as mangiferin, epigallocatechin 3-O-gallate (EGCG) and quercetin from their respective plant extracts. The Ketone Molecule (KM) encapsulated Pea Protein Nanoparticles by phytochemical crosslinking was fully characterized by transmission electron microscopy (TEM), dynamic light scattering (DLS) size and zeta potential (ZP) measurements. The KM concentration was estimated using gas chromatography–mass spectrometry (GC-MS). Phytochemical and water-soluble pea protein interaction was comprehensively studied using nuclear magnetic resonance (NMR) spectroscopy.ResultsGreen nanotechnology offers the most effective means to encapsulate and transform small molecules into pea protein nanoparticles with optimum size for effective cell-specific delivery, thus offering an attractive delivery vehicle to enhance bioavailability. The Ketone Molecule (KM) encapsulated Pea Protein Nanoparticles, by phytochemical crosslinking importantly, demonstrated the most favorable in vivo pharmacokinetics with sustained (R)-3- hydroxybutyrate (BHB) levels and higher area under the curve (AUC) relative to free KM.ConclusionNovel pathways toward the design and development of protein nanoparticle-encapsulated ketone molecules were explored utilizing plant-based proteins from a biocompatibility, biodegradability, and biosafety perspective.

International audience

ABSTRACT The alteration of the hydrological regime caused by damming remains a critical challenge for river systems. Rio Chama, a highly regulated river, has undergone severe alterations in its hydrological regime due to a series of dams that impact sediment transport mechanisms and riparian vegetation dynamics. This study employs remote sensing to assess reach‐scale changes in riparian vegetation and geomorphology and field‐informed 2D hydrodynamic modelling to determine sediment transport processes. We used a random forest classifier within Google Earth engine and high‐resolution National Agriculture Imagery Program imagery (2011–2022) to assess changes in riparian vegetation and channel planform. We evaluated the impact of different flows on sediment transport mechanisms, along with field‐data–informed sediment distribution at the sub‐reach scale. Following a 2009 high‐flow release, channel width remained relatively stable, although planform changes were observed, including shifts in the channel centre line and localised bank erosion, especially in the sinuous sections. Vegetation expanded over time and encroached along bars, linked to reduced overbank flooding and sustained base flow year‐round. Furthermore, results indicated that even smaller flows can lead to fine sediment displacement, while higher flows are necessary to mobilise coarse sediments. This study offers valuable insights for ecological flow recommendations, particularly for Rio Chama, and supports improved restoration strategies and long‐term river management in other dam‐regulated systems across arid and semi‐arid regions.

Traditional, classical, and vernacular architectural forms often exhibit fractal qualities that enhance aesthetic appeal and engage the human eye. This study explores whether the complexity and ornamentation found in traditional designs have measurable impacts on how people unconsciously perceive and visually engage with buildings. Through analyzing 28 facades generated with artificial intelligence, we calculated the fractal dimension of each one by the box-counting method. Unconscious visual attention was then predicted using eye-tracking emulation software to determine which fractal qualities capture most attention. Our results show a significant correlation: buildings with more fractality and organized visual complexity tend to capture more pre-attentive visual attention before viewer cultural associations come into play. These findings highlight the enduring appeal of the proportional complexity, fractal scaling, ornamentation, and intricate geometries found in traditional architecture. The fractal patterns inherent in traditional buildings may contribute to human visual experience, aesthetic appreciation, and psychophysiological health.

Microbial-induced carbonate precipitation is a ground improvement method and has the potential to be a sustainable and environmentally friendly alternative to conventional techniques. Several studies have investigated different behavioural aspects of treated soils, and some have used advanced testing such as triaxial shearing. Very limited studies can be found in the literature that have examined the treated soil behaviour from a critical state theory perspective. A series of drained and undrained triaxial tests are conducted on treated and untreated sand. Due to difficulties in obtaining a critical state line (CSL) for treated soil, its behaviour is analysed using the CSL of the untreated soil as a reference. Behavioural characteristics such as brittleness index, characteristic state, dilatancy and instability state are examined. Correlation of the state parameter (derived from the CSL of untreated soil) with peak stress ratio, brittleness index and characteristic state stress ratio is evaluated. Sudden partial or total loss of strength is observed for soil specimens sheared under relatively high confining stress and is attributed to localised failure and subsequent re-adjustment in soil behaviour.

FemTech is an emerging industry offering products, software, and services to support women’s health and well-being. Within FemTech, mobile health applications (mHealth apps) are popular for managing menstruation, fertility, pregnancy, and menopause. Yet, these apps expose users to deceptive and misleading practices, which can be characterized as Dark Patterns in user experience (or dark UX). Dark UX in commercial FemTech mHealth apps is underexplored, leaving a critical gap in understanding how deceptive patterns manifest in intimate health contexts, the harms they cause, and how to address them. We crowd-source and thematically analyze user accounts of dark UX through user reviews from sixteen systematically selected FemTech mHealth apps. User-reported accounts of dark UX in FemTech mHealth apps reveal several problematic design patterns, which emphasize risks for minors and the need for more transparent design of FemTech mHealth apps. Based on our results, we outline recommendations for enhancing ethical UX design and furthering regulatory action in FemTech.

The digital transformation of manufacturing, driven by Industry 4.0, demands unprecedented levels of autonomy, quality, and operational ‎efficiency. At the heart of these smart production systems lies Computer Numerical Control (CNC) turning, a critical subtractive manufacturing process known for its versatility, repeatability, and ability to achieve sub-micron precision. As the backbone of modern machining, ‎CNC turning plays a vital role in sectors such as aerospace, automotive, and medical device manufacturing. Realizing its full potential ‎requires robust, real-time in-process monitoring to ensure zero-defect output and stable performance. Vibration Signature Analysis (VSA) is a ‎critical, non-invasive diagnostic tool for characterizing the dynamic behavior of machining operations. However, conventional VSA methods rely heavily on manual feature extraction and linear modeling, which are limited in handling the nonlinear, high-dimensional nature of ‎machining dynamics. Deep learning (DL) has introduced a transformative shift, enabling automated, end-to-end learning from raw sensor ‎data to deliver precise diagnostics and prognostics. This review provides a comprehensive overview of DL-based approaches applied to ‎VSA in CNC turning. It critically evaluates dominant architectures, highlights the persistent challenge of model generalizability, and proposes-‎es concrete solutions. Furthermore, it highlights promising future directions such as domain adaptation, transfer learning, and physics-informed neural networks, aiming to guide researchers and practitioners toward the development of intelligent, adaptive, and self-optimizing ‎manufacturing systems‎.