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  • Ecological Model to Evaluate Borrow Areas in the Lower Mississippi River

    Abstract: An aquatic analysis of constructing borrow areas adjacent to the main line levees in the Lower Mississippi River was conducted as part of an Environmental Impact Statement for upgrading the levee system. A Habitat Suitability Index (HSI) regression model based on field collections was developed to predict fish species richness as a function of the morphometry and water quality of borrow areas. The HSI score was multiplied by acres of borrow areas created during construction to obtain habitat units (HUs) for each alternative indicating a substantial gain of fishery habitat in the floodplain. Environmental features identified by the model to increase fish species richness and overall habitat heterogeneity include the shape of the pit (e.g., bowl-shaped with deep water rather than long rectangular with shallower water), the availability of littoral areas for fish spawning and rearing, using best management practices such as tree screens and bank stabilization to lower turbidity, adding islands, and creating sinuous shorelines. The project results in an overall gain in aquatic habitat by creating permanent or semi-permanent water bodies on the floodplain that our research indicates may be occupied by at least 75 species of fish contributing to the overall biodiversity of the lower Mississippi River.
  • Potential Lock Operations Management Application (LOMA) Hardware Installation Sites along the Ohio River to Improve Automatic Identification System (AIS) Reception and Transmit Range

    Abstract: The purpose of this Coastal and Hydraulics Engineering technical note (CHETN) is to propose a list of candidate sites along the Ohio River for the installation of Automatic Identification System (AIS) shoreside towers within the US Army Corps of Engineers (USACE) Lock Operations Management Application (LOMA) program. The LOMA program manages a network of terrestrial (shoreside) AIS sites (Figure 1) and vessel-mounted AIS sites with receive and transmit capability. However, there are known limits to the reception and transmission areas served by existing shoreside towers (referred to as “coverage gaps”) along the Ohio River (DiJoseph et al. 2021). Parties interested in improving AIS coverage to enhance maritime domain awareness and navigational safety along the Ohio River may wish to pursue the installation of LOMA program hardware for this purpose.
  • Characterising Earth Scent

    Abstract: Rationale. Earth scent is the odour emitted from soils. This scent, primarily comprising the alcohols geosmin and 2-methylisoborneol (MIB), has not been fully characterised, but offers high potential for use as an environmental interrogation tool. Methodology. We utilised our field- based, solid-phase microextraction fibre method to test the hypothesis that soil activity and soil property variation can be detected in situ by comparing biogenic volatile emissions. Results. We eliminated sources of error utilising field-based sampling with these fibres, concluding that room temperature storage for up to 7 days is acceptable with minimal loss. Variation in individual fibre affinity for both compounds was higher than expected but no measured concentrations were observed to constitute outliers. Disturbance of minor soil volumes led to significantly higher emission of both compounds over background levels. Soil texture and soil cover had a significant effect on the emission of both compounds. Simulated rainfall, producing the characteristic odour known as petrichor, initiates elevated emission of geosmin. Background (undisturbed soil) concentrations of MIB were occasionally detectable during some sampling events, but geosmin concentrations in the air were always below detection limits without soil disturbance. Virtually all background and disturbed soil samples contained much higher concentrations of MIB compared to geosmin, but geosmin variation between replicates and experimental units was much lower. Discussion. Soil disturbance and soil property variation can be remotely detected using emission of volatile compounds. Correlating emission from the soil with respect to disturbance events and environmental properties could yield a powerful new tool for acquiring soil information.
  • 2D Fluorinated Graphene Oxide (FGO)-Polyethyleneimine (PEI) Based 3D Porous Nanoplatform for Effective Removal of Forever Toxic Chemicals, Pharmaceutical Toxins, and Waterborne Pathogens from Environmental Water Samples

    Abstract: Although water is essential for life, as per the United Nations, around 2 billion people in this world lack access to safely managed drinking water services at home. Herein we report the development of a two-dimensional (2D) fluorinated graphene oxide (FGO) and polyethylenimine (PEI) based three-dimensional (3D) porous nanoplatform for the effective removal of polyfluoroalkyl substances (PFAS), pharmaceutical toxins, and waterborne pathogens from contaminated water. Experimental data show that the FGO-PEI based nanoplatform has an estimated adsorption capacity (qm) of ∼219 mg g−1 for perfluorononanoic acid (PFNA) and can be used for 99% removal of several short- and long-chain PFAS. A comparative PFNA capturing study using different types of nanoplatforms indicates that the qm value is in the order FGO-PEI > FGO > GO-PEI, which indicates that fluorophilic, electrostatic, and hydrophobic interactions play important roles for the removal of PFAS. Reported data show that the FGO-PEI based nanoplatform has a capability for 100% removal of moxifloxacin antibiotics with an estimated qm of ∼299 mg g−1. Furthermore, because the pore size of the nanoplatform is much smaller than the size of pathogens, it has a capability for 100% removal of Salmonella and Escherichia coli from water. Moreover, reported data show around 96% removal of PFAS, pharmaceutical toxins, and pathogens simultaneously from spiked river, lake, and tap water samples using the nanoplatform.
  • 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.
  • A Comprehensive Review on Wood Chip Moisture Content Assessment and Prediction

    Abstract: Wood chips are the primary sources of raw materials for numerous industries, including pelleting mills, biorefineries, pulp-and-paper industries, and biomass-based power generation facilities. Unfortunately, when wood chips are utilized as a renewable and environmentally friendly resource, industries are constantly challenged by the consistency of the wood chip qualities (e.g., moisture/ash contents, size distributions) - a historically recognized problem on a global scale. Among other wood chip quality attributes, the moisture content is considered the most pressing one as it directly impacts the energy content, storage stability, and handling properties of the raw and finished products. Therefore, accurate wood chip moisture content prediction can help optimize the drying process and reduce energy consumption. In this review, a survey was conducted on various techniques and models employed for predicting wood chip moisture content. The advantages and limitations of these approaches, as well as their potential applications and future directions were also discussed. This review aims to provide a comprehensive overview of the current state-of-the-art in wood chip moisture content prediction and to highlight the challenges and opportunities for further research and development in this field.
  • Hydraulic Sorting of Dredged Sediment in a Pipeline: An Evaluation of the Sediment Distribution Pipe

    Abstract: The US Army Corps of Engineers (USACE) recently established a goal to beneficially use 70% of material dredged from the nation’s navigable waterways by the year 2030. Most of the sediments dredged by the USACE are heterogeneous mixtures of mud and sand, which can limit beneficial use of dredged material (BUDM) applications. Innovative technologies that can sort material during the dredging process are needed to help increase BUDM practices. This investigation sought to evaluate the ability of a sediment distribution pipe (SDP) to sort particles during transport in a pipeline. Field demonstrations were conducted during dredged material placements at Sturgeon Island, New Jersey. Velocity within the pipeline was found to be inadequate for efficient hydraulic sorting of fines (<75 μm) and produced inconclusive results. Small scale laboratory SDP experiments found that effluent from the SDP holes had an altered sediment texture compared to the initial slurry and that hydraulic sorting was occurring within the pipeline. However, outflow from the SDP holes was inconsistent, and typically >90% of the sediment mass was discharged out the end of the pipeline. Sorting efficiency of the SDP could not be accurately assessed in the current experimental configuration.
  • 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.
  • Ecological Modeling of Microbial Community Composition under Variable Temperatures

    Abstract: Soil microorganisms interact with one another within soil pores and respond to external conditions such as temperature. Data on microbial community composition and potential function are commonly generated in studies of soils. However, these data do not provide direct insight into the drivers of community composition and can be difficult to interpret outside the context of ecological theory. In this study, we explore the effect of abiotic environmental variation on microbial species diversity. Using a modified version of the Lotka-Volterra Competition Model with temperature-dependent growth rates, we show that environmentally relevant temperature variability may expand the set of temperature-tolerance phenotype pairs that can coexist as two-species communities compared to constant temperatures. These results highlight a potential role of temperature variation in influencing microbial diversity. This in turn suggests a need to incorporate temperature into predictive models of microbial communities in soil and other environments. We recommend future work to parameterize the model applied in this study with empirical data from environments of interest, and to validate the model predictions using field observations and experimental manipulations.