16 Feb 2024

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.

9 Feb 2024

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.

6 Feb 2024

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.

22 Jan 2024

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.

22 Jan 2024

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.

11 Jan 2024

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.

8 Jan 2024

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.

8 Jan 2024

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.

4 Jan 2024

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.

4 Jan 2024

Influence of Chemical Coatings on Solar Panel Performance Snow Accumulation

Abstract: Solar panel performance can be impacted when panel surfaces are coated with substances like dust, dirt, snow, or ice that scatter and/or absorb light and may reduce efficiency. As a consequence, time and resources are required to clean solar panels during and after extreme weather events or whenever surface coating occurs. Treating solar panels with chemical coatings that shed materials may decrease the operating costs associated with solar panel maintenance and cleaning. This study investigates three commercial coatings for use as self-cleaning glass technologies. Optical and thermal properties (reflectivity, absorption, and transmission) are investigated for each coating as well as their surface wettability and particle size. Incoming solar radiation was continuously monitored and snow events were logged to estimate power production capabilities and surface accumulation for each panel. In terms of power output, the commercial coatings made little impact on overall power production compared to the control (uncoated) panels. This was attributable to the overall high transmission, low absorption, and low reflection of each of the commercial coatings, making their presence on the surface of solar panels have minimal impact besides to potentially shed snow While the coatings made no observable difference to increase power production compared to the control panels, the shedding results from video monitoring suggest both the hydrophilic or hydrophobic test coatings decreased snow accumulation to a greater extent than the control panels (uncoated). Controlling the wettability properties of the solar panel surfaces has the potential to limit snow accumulation when compared to uncoated panel surfaces.