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Category: Publications: Environmental Laboratory (EL)
  • Assessing the Genetic Diversity of Nymphoides peltata in the Native and Adventive Range Using Microsatellite Markers

    Abstract: Nymphoides peltata (yellow floatingheart), native to Eurasia, is an invasive plant in the USA, where it grows in relatively isolated but widespread populations. The species is capable of sexual reproduction by seed and asexual reproduction through fragmentation. Although N. peltata is recognized as a noxious weed, little is known about its geographic region of origin or its dispersal mechanisms and relative amount of genetic variation in its adventive range. We conducted a genetic analysis of N. peltata by studying 68 localities across the native range and 47 localities in the adventive range, using microsatellite markers to determine genetic variability within and among populations, and to infer regions in the native range from which invasive plants originated. A large number of sites in the USA were genetically identical to one another, and there were two predominant multilocus allele phenotypes that were distributed in the northern and southern latitudes, respectively. Additional USA sites were similar to one of the predominant genetic profiles, with greater genetic diversity in southern populations. The genetically identical sites are consistent with asexual spread, potentially via anthropogenic mechanisms. Plants across the USA range were observed to produce viable seeds, and some genetic variation could be explained by sexual reproduction. All USA plants were more similar to plants in Europe than they were to plants in Asia, indicating that the plants likely were introduced originally from Europe. The existence of two genetic clusters and their similarity to plants in different parts of Europe constitute evidence for at least two N. peltata introductions into the USA.
  • Deep Learning Methods for Omics Data Imputation

    Abstract: One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.
  • Overview of Microscale Analytical Methods for the Quantitative Detection of Bioaccumulative Contaminants in Small Tissue Masses

    Abstract: For many bioaccumulation studies, generation of large sample masses of exposed organisms is challenging or even prohibitive. Therefore, the use of smaller sample masses for analysis without compromising data quality or quantitative level achieved is desirable. To this end, a variety of microanalytical procedures have been developed that used 1 g or less of tissue to address specific experimental challenges. However, these methods have not been systematically evaluated or published. The present work evaluates the current state of the microanalytical methods reported and identifies additional needs that would benefit US Army Corps of Engineers (USACE) research and navigation dredging programs. Discussions with commercial laboratories revealed that they typically do not accept small sample masses and require individual sample masses ranging from 10 to 20 g wet weight of tissue per analysis. If they do analyze a small mass sample, they routinely do not modify their standard process, resulting in detection and reporting limits orders of magnitude higher; therefore, essentially useless nondetect data are generated for regulatory decisions. To address the lack of commercial availability of microanalytical methods, we recommend pursuing method development and subsequent validation of microscale extraction and analysis of a variety of common contaminant compounds in tissue matrices.
  • USACE Freshwater Harmful Algal Bloom Research and Development Initiative

    Abstract: Harmful Algal Blooms (HABs) represent a significant and costly threat to our nation’s economy and natural resources. This report outlines the US Army Corps of Engineers, Engineer Research and Development Center’s (USACE-ERDC’s) approach to deliver scalable technologies for prevention, early detection, and management of HABs to reduce HAB event frequency, severity, and duration.
  • Framework Development for Rapid Assessment and Economic Valuation of Feral Swine Damage to Wetland Terrain: A Pilot Study at US Army Corps of Engineers–Somerville Lake, Texas

    Abstract: The increased spread and presence of feral swine on sensitive natural resources landscapes like wetlands has become a considerable concern on lands managed by the US Army Corps of Engineers. In August 2021 a pilot study was carried out at Somerville Lake, Texas, as the first step in a three-year research plan to develop an ecological-economic framework for feral swine damage assessments (FSDA) and valuation. The study sought to quantify and value soil disturbance caused by feral swine trampling, rooting, and wallowing on wetland soils. The primary objective—to develop and test a rapid FSDA prototype—was achieved and represents an important first step to creating a quick and user-friendly damage-assessment framework that also estimates the economic value of the damage observed. With continued testing and development, this rapid FSDA protocol will be of use to all who manage feral swine impacts on landscapes with wetland ecosystems, and findings from this information will be of use for scientifically informed cost-benefit analysis and management decision-making.
  • Considerations for Integrating Ecological and Hydrogeomorphic Models: Developing a Comprehensive Marsh Vegetation Model

    PURPOSE: Predictive models for salt marsh management require a systems perspective that recognizes the dynamic interactions between physical and ecological processes. It is critical to link physical process and landscape evolution models to quantify hydro-eco-geomorphic feedbacks in marsh environments. A framework that explicitly defines how to integrate these disparate models is a necessary step towards developing a comprehensive marsh model. This technical note (TN) proposes an approach to integrate existing hydrodynamic and geomorphic models with a mechanistic vegetation model into a coupled framework to better simulate salt marsh evolution.
  • Classifying and Benchmarking High-Entropy Alloys and Associated Materials for Electrocatalysis: A Brief Review of Best Practices

    Abstract: In light of the immense compositional diversity of high-entropy materials (HEMs) recently reported (e.g., high-entropy chalcogenides, perovskites, ceramics, etc.) and the relatively amorphous definition of High-Entropy, it is imperative that consistent material classification and benchmarking practices be employed to facilitate comparison between reported figures of merit. In this opinion, an updated form of the numerical high entropy definition is reviewed, which renders a universal entropy metric applicable to high-entropy alloys and emerging HEMs alike. Analytical methods to verify the existence of a solid-solution microstructure, elucidate atomic valence states, and probe atomic disorder are discussed with literature examples to facilitate the physical classification of HEMs. Electrocatalytic benchmarking is discussed in the context of water splitting reactions and best practices are reviewed for determining the electrocatalytically active surface area, reaction overpotential, and electrocatalyst stability.
  • Scaled-Up Synthesis of Water-Retaining Alginate-Based Hydrogel

    Purpose: Synthesis of a scaled-up version of a lithium-ion-based alginate/poly(acrylamide-co-stearyl methacrylate) [Li-alginate/P(AAm-co-SMA)] hydrogel with several optimizations for thermal signature investigations on various environmental substrates.
  • Assessing Differences in the Wetland Functional Capacity of Wet Pine Flatwood Compensatory Mitigation Sites Managed with Prescribed Fire and Mechanical Mowing

    Abstract: This report assesses the functional capacity of wet pine flatwood wetland habitats in the Gulf Coastal region of the United States, with a specific focus on compensatory mitigation sites maintained using mowing or prescribed fire, or both, as understory management strategies. The use of mowing in lieu of prescribed fire treatments has been proposed for a variety of reasons, including when mitigation sites are located near residential areas or where fires pose a risk to private property and public safety. This study evaluates the effects of mechanized mowing on ecosystem functions by using the hydrogeomorphic (HGM) wetland functional-assessment method to compare mitigation sites managed by mowing to sites managed by prescribed-fire regimes. Assessing mowing as a vegetation-control strategy in lieu of prescribed-fire regimes provides valuable information that can improve the design and management of wet pine flatwoods mitigation sites throughout portions of the southeastern United States, where this wetland class occurs.
  • Radio Frequency Heating of Washable Conductive Textiles for Bacteria and Virus Inactivation

    Abstract: The ongoing COVID-19 pandemic has increased the use of single-use medical fabrics such as surgical masks, respirators, and other personal protective equipment (PPE), which have faced worldwide supply chain shortages. Reusable PPE is desirable in light of such shortages; however, the use of reusable PPE is largely restricted by the difficulty of rapid sterilization. In this work, we demonstrate successful bacterial and viral inactivation through remote and rapid radio frequency (RF) heating of conductive textiles. The RF heating behavior of conductive polymer-coated fabrics was measured for several different fabrics and coating compositions. Next, to determine the robustness and repeatability of this heating response, we investigated the textile’s RF heating response after multiple detergent washes. Finally, we show a rapid reduction of bacteria and virus by RF heating our conductive fabric. 99.9% of methicillin¬resistant Staphylococcus aureus (MRSA) was removed from our conductive fabrics after only 10 min of RF heating; human cytomegalovirus (HCMV) was completely sterilized after 5 min of RF heating. These results demonstrate that RF heating conductive polymer-coated fabrics offer new opportunities for applications of conductive textiles in the medical and/or electronic fields.