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At least 199 records · Page 11

Environmental Testing of SolarSCADA's SSI Data Acquisition System

In April of 2022, SolarSCADA, Inc., was awarded voucher funding to conduct research and analysis at the National Renewable Energy Laboratory (NREL) per the American-Made Solar Prize. SolarSCADA produces a fully integrated data acquisition system that is designed specifically for PV fields and includes a data monitoring platform. SolarSCADA is focused providing the SSI unit with all sensors/measurements per requirements in IEC-61724-1 [1] and with utility scale bankability. The objective of the SolarSCADA voucher funding was established as follows: Leverage NREL's existing calibration and environmental testing capabilities to evaluate the stability of the SSI channels and overall systeme in response to extreme environmental conditions associated with fielded PV.

14 SOLAR ENERGY↗

Structural adaptation of fungal cell wall in hypersaline environment

Abstract Halophilic fungi thrive in hypersaline habitats and face a range of extreme conditions. These fungal species have gained considerable attention due to their potential applications in harsh industrial processes, such as bioremediation and fermentation under unfavorable conditions of hypersalinity, low water activity, and extreme pH. However, the role of the cell wall in surviving these environmental conditions remains unclear. Here we employ solid-state NMR spectroscopy to compare the cell wall architecture of Aspergillus sydowii across salinity gradients. Analyses of intact cells reveal that A. sydowii cell walls contain a rigid core comprising chitin, β-glucan, and chitosan, shielded by a surface shell composed of galactomannan and galactosaminogalactan. When exposed to hypersaline conditions, A. sydowii enhances chitin biosynthesis and incorporates α-glucan to create thick, stiff, and hydrophobic cell walls. Such structural rearrangements enable the fungus to adapt to both hypersaline and salt-deprived conditions, providing a robust mechanism for withstanding external stress. These molecular principles can aid in the optimization of halophilic strains for biotechnology applications.

59 BASIC BIOLOGICAL SCIENCES↗

Utilizing coal-derived solid carbon materials towards next-generation smart and multifunction pavements

This project focused on developing an eco-friendly, multifunctional pavement system, called Coal-Derived Carbon Enabled Smart Pavement (CDC-SP), using coal-derived materials. The system utilizes coal-derived pyrolyzed char as a key component to create electrically conductive asphalt concrete for smart pavements that offer self-heating, self-sensing, and self-healing capabilities. These functions are made possible by the conductive properties of coal-char, which enable Ohmic heating for snow and ice deicing, piezoresistivity for self-sensing, and induction heating for self-healing of cracks caused by stress or aging. The team successfully created CDC-SP samples with over 50% coal char composition, demonstrating desirable mechanical properties such as rutting resistance, moisture susceptibility, and cracking resistance. These samples also exhibited strong electrical and thermal conductivity, making them ideal for heating applications. Extensive tests confirmed the pavement’s effectiveness in melting ice and snow and maintaining durability under environmental conditions. The CDC-SP presents a promising approach to integrating U.S. domestic coal resources into infrastructure projects, providing environmental and economic benefits by enhancing pavement performance while utilizing low-cost coal-derived materials. Further research and compliance with industry standards are recommended before commercial scaling.

01 COAL, LIGNITE, AND PEAT↗

Fe-rich X-ray amorphous material records past climate and persistence of water on Mars

X-ray amorphous material comprises 15-73 wt.% of sedimentary rocks and eolian sediments in Gale crater. This material is variably siliceous and iron rich but aluminum poor. The presence of volatiles is consistent with the existence of incipient weathering products. To better understand the implications of this material for past aqueous conditions on Mars, here we investigate X-ray amorphous material formation and longevity within terrestrial iron rich soils with varying ages and environmental conditions using bulk and selective dissolution methods, X-ray diffraction, and transmission electron microscopy. Results indicate that in situ aqueous alteration is required to concentrate iron into clay-size fraction material. Cooler climates promote the formation and persistence of X-ray amorphous material whereas warmer climates promote the formation of crystalline secondary phases. Iron rich X-ray amorphous material formation and persistence on Mars are therefore consistent with past cool and relatively wet environments followed by long-term cold and dry conditions.

54 ENVIRONMENTAL SCIENCES↗

Wavelet Analysis of Properties of Marine Boundary Layer Mesoscale Cells Observed From AMSR–E

Marine boundary layer clouds tend to organize into closed or open mesoscale cellular convection (MCC). Here, two-dimensional wavelet analysis is applied for the first time to passive microwave retrievals of cloud water path (CWP), water vapor path (WVP), and rain rate (RR) from Advanced Microwave Scanning Radiometer for Earth Observing System in 2008 over the Northeast and Southeast Pacific, and the Southeast Atlantic subtropical stratocumulus to cumulus transition regions. The (co-)variability between CWP, WVP, and RR in 160 × 160 km 2 analysis boxes is partitioned between four mesoscale wavelength octaves (20, 40, 80, and 160 km). The cell scale is identified as the wavelength of the peak CWP variance. Together with a machine-learning classification of cell type, this allows the statistical characteristics of open and closed MCC of various scales, and its relation to WVP, RR, and potential environmental controlling factors to be analyzed across a very large set of cases. Here, the results show that the cell wavelength is most commonly 40–80 km. Cell-scale CWP perturbations are good predictors of the WVP and RR perturbations. For cells larger than 20 km, there is no obvious dependence of cell scale on the environmental controlling factors tested, suggesting that the cell scale may depend more on its historical evolution than the current environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

Landscape analysis of environmental data sources for linkage with SEER cancer patients database

Abstract One of the challenges associated with understanding environmental impacts on cancer risk and outcomes is estimating potential exposures of individuals diagnosed with cancer to adverse environmental conditions over the life course. Historically, this has been partly due to the lack of reliable measures of cancer patients’ potential environmental exposures before a cancer diagnosis. The emerging sources of cancer-related spatiotemporal environmental data and residential history information, coupled with novel technologies for data extraction and linkage, present an opportunity to integrate these data into the existing cancer surveillance data infrastructure, thereby facilitating more comprehensive assessment of cancer risk and outcomes. In this paper, we performed a landscape analysis of the available environmental data sources that could be linked to historical residential address information of cancer patients’ records collected by the National Cancer Institute’s Surveillance, Epidemiology, and End Results Program. The objective is to enable researchers to use these data to assess potential exposures at the time of cancer initiation through the time of diagnosis and even after diagnosis. The paper addresses the challenges associated with data collection and completeness at various spatial and temporal scales, as well as opportunities and directions for future research.

60 APPLIED LIFE SCIENCES↗

Comprehensive framework for assessing and optimizing existing research networks

Conservation, monitoring, and research networks, or collections of ecological research sites unified under a common mission of data collection or a research mission, are essential infrastructure for understanding large landscapes. However, most networks developed opportunistically over decades rather than through systematic design, creating potential limitations in the ability to address conservation challenges across entire regions. We developed a framework to evaluate how well an existing research network represents the environmental conditions its members study and devised an approach to rank sites of priority for strategic expansion. Our approach measures performance through environmental representativeness, geographic coverage, and adequacy for scientific inference and thus optimizes limited monitoring resources to maximize scientific impact. We demonstrated this approach with the U.S. Department of Agriculture (USDA) Forest Service Experimental Forests and Ranges Network (EFRN), a 79‐site network across the United States that grew opportunistically over a century. At the national scale, the network effectively captured high‐biomass forests important for carbon cycle research; 82% of forest biomass was in well‐represented areas. Some areas in Texas, Florida, the Rocky Mountains, and the West Coast had no relevant EFRN sites, which limits the ability to make regional inferences. A fundamental challenge for the EFRN was that sites improving regional extent coverage sometimes provided minimal national benefits, which can create conflicts between local and global priorities. Adding the highest‐ranked candidate site provided a relevant site for 17% of currently poorly represented 1‐km pixel cells nationally, but regional and national site rankings varied considerably due to nested spatial inference. This framework provides quantitative tools for strategic infrastructure decision‐making, ensures that limited monitoring resources maximize conservation impact, and can be applied broadly to address the widespread challenge of optimizing conservation and monitoring networks worldwide.

additional site↗

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

59 BASIC BIOLOGICAL SCIENCES↗

Social Disparities of Pain and Pain Intensity Among Women Diagnosed With Early Stage Breast Cancer

Background: Breast cancer is one of the most commonly diagnosed cancers among women in the United States and pain is the most common side effect of breast cancer and its treatment. Yet, the relationships between social determinants of pain and pain experience/intensity remain under-investigated. We examined the associations between social determinants of pain both at the individual level and the neighborhood level to understand how social conditions are associated with pain perception among early stage breast cancer patients. Methods: We conducted integrated statistical analysis of 1,191 women with early stage breast cancer treated at a large cancer center in Memphis, Tennessee. Combining electronic health records, patient-reported data and census data regarding residential address at the time of first diagnosis, we evaluated the relationships between social determinants and pain perception. Pain responses were self-reported by a patient as a numerical rating scale score at the patient’s initial diagnosis and follow-up clinical visits. We implemented two sets of statistical analyses of the zero-inflated Poisson model and estimated the associations between neighborhood poverty prevalence and breast cancer pain intensity. After adjustment for demographic characteristics, cancer stage, and chemotherapy, pain perception was significantly associated with poverty and blight level of the neighborhood. Results: Among women living in the highest-poverty areas, the odds of reporting pain were 2.48 times higher than those in the lowest-poverty area. Women living in the highest-blight area had 5.43 times higher odds of reporting pain than those in the lowest-blight area. Neighborhood-level social determinants were significantly associated with pain intensity among women diagnosed with early-stage breast cancer. Conclusions: Distressed neighborhood conditions are significantly associated with higher pain perception. Breast cancer patients living in socio-economically disadvantaged neighborhoods and in poor environmental conditions reported higher pain severity compared to patients from less distressed neighborhoods. Therefore, post-diagnosis pain treatment design needs to be tailored to the social determinants of the breast cancer patients.

60 APPLIED LIFE SCIENCES↗

Meta Biome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Microbiology↗

Permanent Stress Adaptation and Unexpected High Light Tolerance in the Shade-Adapted Chlamydomonas priscui

The Antarctic photopsychrophile, Chlamydomonas priscui UWO241, is adapted to extreme environmental conditions, including permanent low temperatures, high salt, and shade. During long-term exposure to this extreme habitat, UWO241 appears to have lost several short-term mechanisms in favor of constitutive protection against environmental stress. This study investigated the physiological and growth responses of UWO241 to high-light conditions, evaluating the impacts of long-term acclimation to high light, low temperature, and high salinity on its ability to manage short-term photoinhibition. We found that UWO241 significantly increased its growth rate and photosynthetic activity at growth irradiances far exceeding native light conditions. Furthermore, UWO241 exhibited robust protection against short-term photoinhibition, particularly in photosystem I. Lastly, pre-acclimation to high light or low temperatures, but not high salinity, enhanced photoinhibition tolerance. These findings extend our understanding of stress tolerance in extremophilic algae. In the past 2 decades, climate change-related increasing glacial stream flow has perturbed long-term stable conditions, which has been associated with lake level rise, the thinning of ice covers, and the expansion of ice-free perimeters, leading to perturbations in light and salinity conditions. Our findings have implications for phytoplankton survival and the response to change scenarios in the light-limited environment of Antarctic ice-covered lakes.

Plant Sciences↗

Coupling of high-resolution mass spectrometer and photosynthesis system for comprehensive leaf volatile metabolite profiling

Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100–350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.

Biogenic volatile organic compounds↗

Measurement report: A comparative analysis of an intensive incursion of fluorescing African dust particles over Puerto Rico and another over Spain

Measurements during episodes of African dust, made with two wideband integrated bioaerosol spectrometers (WIBSs), one on the northeastern coast of Puerto Rico and the other in the city of León, Spain, show unmistakable, bioaerosol-like fluorescing aerosol particles (FAPs) that can be associated with these dust episodes. The Puerto Rico event occurred during a major incursion of African dust during June 2020. The León event occurred in the late winter and spring of 2022, when widespread, elevated layers of dust inundated the Iberian Peninsula. Satellite and back-trajectory analyses confirm that dust from northern Africa was the source of the particles during both events. The WIBSs measure the size of individual particles in the range from 0.5 to 30 µm, derive a shape factor, and classify seven types of fluorescence from the FAPs. In general, it is not possible to directly determine the specific biological identity from fluorescence signatures; however, measurements of these types of bioaerosols in laboratory studies allow us to compare ambient fluorescence patterns with whole microbial cells measured under controlled conditions. Here we introduce some new metrics that offer a more quantitative approach for comparing FAP characteristics derived from particles measured under different environmental conditions. The analysis highlights the similarities and differences at the two locations and reveals differences that can be attributed to the age and history of the dust plumes, e.g., the amount of time that the air masses were in the mixed layer and the frequency of precipitation along the air mass trajectory.

54 ENVIRONMENTAL SCIENCES↗

Partitioning Net Ecosystem Exchange (NEE) of CO 2 Using Solar-Induced Chlorophyll Fluorescence (SIF)

Accurate partitioning of net ecosystem exchange (NEE) of CO 2 to gross primary production (GPP) and ecosystem respiration (R eco ) is crucial for understanding carbon cycle dynamics under changing climate. However, it remains as a long-standing problem in global ecology due to lack of independent constraining information for the two offsetting component fluxes. solar-induced chlorophyll fluorescence (SIF), a mechanistic proxy for photosynthesis, holds great promise to improve NEE partitioning by constraining GPP. We developed a parsimonious SIF-based approach for NEE partitioning and examined its performance using synthetic simulations and field measurements. This approach outperforms conventional approaches in reproducing simulated GPP and R eco , especially under high vapor pressure deficit. For field measurements, it results in lower daytime GPP and R eco than conventional approaches. Furthermore, this study made the first attempt to demonstrate SIF's potential for improving NEE partitioning accuracy and sets the stage for future efforts to examine its robustness and scalability under real-world environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

AeDES: a next-generation monitoring and forecasting system for environmental suitability of Aedes-borne disease transmission

Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth, more frequent travels and ecological changes. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection upon their return home if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, the environmental suitability for the presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. We present a next-generation monitoring and forecasting system for Aedes-borne diseases’ environmental suitability (AeDES) of transmission in the conterminous United States and transboundary regions, using calibrated ento-epidemiological models, climate models and temperature observations. After analyzing the seasonal predictive skill of AeDES, we briefly consider the recent Zika epidemic, and the compound effects of the current Central American dengue outbreak happening during the SARS-CoV-2 pandemic, to illustrate how a combination of tailored deterministic and probabilistic forecasts can inform key prevention and control strategies .

54 ENVIRONMENTAL SCIENCES↗

2020 Nevada National Security Site Environmental Report

This environmental report summarizes actions taken in 2020 to protect the environment and the public while achieving the NNSA/NFO mission goals. It is prepared for the public and our stakeholders in hopes that it is readily understandable and usable. It is a key component in our efforts to keep the public informed of environmental conditions at the NNSS and its support facilities in Las Vegas, Nevada.

54 ENVIRONMENTAL SCIENCES↗

2020 Nevada National Security Site Environmental Report – Summary

This summary report provides an abbreviated and more readable version of the full report, which discusses actions taken in 2020 to protect the environment and the public while achieving the NNSA/NFO mission goals. It is prepared for the public and our stakeholders in hopes that it is readily understandable and usable. It is a key component in our efforts to keep the public informed of environmental conditions at the NNSS and its support facilities in Las Vegas, Nevada.

54 ENVIRONMENTAL SCIENCES↗

Nevada National Security Site Environmental Report 2021 - Summary

This summary report provides an abbreviated and more readable version of the full report, which discusses actions taken in 2021 to protect the environment and the public while achieving the NNSA/NFO mission goals. It is prepared for the public and our stakeholders in hopes that it is readily understandable and usable. It is a key component in our efforts to keep the public informed of environmental conditions at the NNSS and its support facilities in Las Vegas, Nevada.

54 ENVIRONMENTAL SCIENCES↗