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High-solids incubations were performed to enrich for microbial communities and enzymes that decompose rice straw under mesophilic (35°C) and thermophilic (55°C) conditions. Thermophilic enrichments yielded a community that was 7.5 times more metabolically active on rice straw than mesophilic enrichments. Extracted xylanase and endoglucanse activities were also 2.6 and 13.4 times greater, respectively, for thermophilic enrichments. Metagenome sequencing was performed on enriched communities to determine community composition and mine for genes encoding lignocellulolytic enzymes. Proteobacteria were found to dominate the mesophilic community while Actinobacteria were most abundant in the thermophilic community. Analysis of protein family representation in each metagenome indicated that cellobiohydrolases containing carbohydrate binding module 2 (CBM2) were significantly overrepresented in the thermophilic community. Micromonospora, a member of Actinobacteria, primarily housed these genes in the thermophilic community. In light of these findings, Micromonospora and other closely related Actinobacteria genera appear to be promising sources of thermophilic lignocellulolytic enzymes for rice straw deconstruction under high-solids conditions. Furthermore, these discoveries warrant future research to determine if exoglucanases with CBM2 represent thermostable enzymes tolerant to the process conditions expected to be encountered during industrial biofuel production.

Regulatory pressure along with environmental and human health concerns drive the development of soil fumigation alternatives such as soil biosolarization (SBS). SBS involves tarping soil that is at field capacity with a transparent film following amendment with certain organic materials. Heating via the greenhouse effect results in an increase of the soil temperature. The organic amendments can promote microbial activity that can enhance pest inactivation by depleting oxygen, producing biopesticidal fermentation products, and competing with pests. The properties of the organic amendments can heavily influence the type and magnitude of these effects. This study evaluated the viability of chitin as a novel SBS soil amendment to influence soil fungal and bacterial microbial communities, including control of the plant pathogen Fusarium oxysporum f.sp. lactucae (FOL). Changes to FOL and the broader soil microbiota were monitored in response to biosolarization using 0.1% (by dry weight) amendment with chitin (Rootguard). FOL suppression was only observed in chitin amended soils that were incubated at room temperature, not under solarized conditions. Conversely, it decreased solarization efficacy in the upper (0-10 cm) soil layer. The presence of chitin also showed increase in FOL under anaerobic and fluctuating temperature regime conditions. Biosolarization with chitin amendment did exhibit an impact on the overall soil microbial community. The fungal genus Mortierella and the bacterial family Chitinophagaceae were consistently enriched in biosolarized soils with chitin amendment. This study showed low potential FOL suppression due chitin amendment at the studied levels. However, chitin amendment showed a higher impact on the fungal community than the bacterial community. The impact of these microbial changes on crop protection and yields need to be studied in the long-term.

Abstract The properties of digestates generated through anaerobic digestion are influenced by interactions between the digester microbial communities, feedstock properties and digester operating conditions. This study investigated the effect of varying initial feedstock carbon to nitrogen (C/N) ratios on digestate microbiota and predicted abundance of genes encoding lignocellulolytic activity. The C/N ratio had a significant impact on the digestate microbiome. Feedstocks with intermediate C/N ratio (20–27) (where higher biomethane potential was observed) showed higher relative abundance of archaea compared to feedstocks with C/N ratios at 17 and 34. Within microbial networks, four microbial clusters and eight connector microorganisms changed significantly with the C/N ratio (P < 0.05). Feedstocks with C/N < 23 were richer in organisms from the family Thermotogaceae and genus Caldicoprobacter and enhanced potential for degradation of maltose, galactomannans, melobiose and lactose. This study provides new insights into how anaerobic digestion conditions relate to the structure and functional potential of digester microbial communities, which may be relevant to both digester performance and subsequent utilization of digestates for composting or amending soil.

In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μg/m3, and a root mean square error (RMSE) of 5.8 μg/m3. This study also found that the model performance as well as the site measured PM2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R2) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R2 scores correlate positively with the mean measured PM2.5 concentration at monitoring sites. Mean measured PM2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM2.5 concentrations. In addition, the reconstructed PM2.5 surfaces serve as an important data source for pollution event tracking and PM2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM2.5 estimates were made for 10 km by 10 km and the PM2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM2.5 concentrations.

IntroductionThe SARS-CoV-2 mediated COVID-19 pandemic has impacted millions worldwide. Hyper-inflammatory processes, including cytokine storm, contribute to long-standing tissue injury and damage in COVID-19. The metabolism of sphingolipids as regulators of cell survival, differentiation, and proliferation has been implicated in inflammatory signaling and cytokine responses. Sphingosine-kinase-1 (SK1) and ceramide-synthase-2 (CERS2) generate metabolites that regulate the anti- and pro-apoptotic processes, respectively. Alterations in SK1 and CERS2 expression may contribute to the inflammation and tissue damage during COVID-19. The central objective of this study is to evaluate structural changes in the lung post-SARS-CoV-2 infection and to investigate whether the sphingolipid rheostat is altered in response to SARS-CoV-2 infection.MethodsCentral and peripheral lung tissues from COVID-19+ or control autopsies and resected lung tissue from COVID-19 convalescents were subjected to histologic evaluation of airspace and collagen deposisiton, and immunohistochemical evaluation of SK1 and CERS2.ResultsHere, we report significant reduction in air space and increase in collagen deposition in lung autopsy tissues from patients who died from COVID-19 (COVID-19+) and COVID-19 convalescent individuals. SK1 expression increased in the lungs of COVID-19+ autopsies and COVID-19 convalescent lung tissue compared to controls and was mostly associated with Type II pneumocytes and alveolar macrophages. No significant difference in CERS2 expression was noted. SARS-CoV-2 infection upregulates SK1 and increases the ratio of SK1 to CERS2 expression in lung tissues of COVID-19 autopsies and COVID-19 convalescents.DiscussionThese data suggest an alteration in the sphingolipid rheostat in lung tissue during COVID-19, suggesting a potential contribution to the inflammation and tissue damage associated with viral infection.

Phelipanche ramosa , or branched broomrape, is a parasitic weed that attaches to the roots of host plants and can cause great agricultural losses, from reduced yield to crop termination without harvest. Conventional approaches to manage broomrape infestations include fumigation with toxic compounds, such as methyl bromide. Solarization has been used as well, but can leave a field out of service for entire seasons. Biosolarization is an alternative pest management strategy with a shorter timeframe that can reduce the need for fumigants and preplant herbicides by amending soil with organic matter, covering with a clear tarp, and irrigating. Weed seeds subjected to biosolarization experience a variety of stresses –including biocidal organic acids, high temperatures, and low oxygen– that can prevent future germination. This study evaluated the application of biosolarization with two different amendments, three amendment rates, and three temperatures for reduction of P. ramosa germinability. Simulated biosolarization was carried out in anaerobic bioreactors of wetted and amended soil with seeds incorporated. Tomato pomace and spent mushroom substrate were used as the organic matter amendments. Soil pH, electrical conductivity, and production of biocidal organic acids were evaluated before and after biosolarization, alongside germination capacity of P. ramosa seeds. Soil metrics of biosolarization treatments were compared to unamended, solarized soil and seed germination capacity was compared to that of untreated, control seeds. Broomrape germination is triggered by strigolactone, a plant hormone released into the rhizosphere. Therefore, a strigolactone analog was used to test the germinability of the treated seeds in absence of a host. To isolate the effects of biocidal organic acid exposure on broomrape germinability, seeds were also exposed to the acids produced during biosolarization, but without the added thermal and low oxygen stresses. The results suggest that biosolarization is an effective method to reduce P. ramosa germinability by >99% with amendment rates as low as 1.0% by dry weight and temperatures of 35 °C, which can inform future validation in field studies. Use of biosolarization may help protect the production of host plants on an infested field, while reducing the need for toxic compounds or lengthy treatments.

PM2.5, a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM2.5 concentrations modeling remains a hot topic. In this study, the Next Generation Weather Radar (NEXRAD) is used as a supplementary weather data source, along with European Centre for Medium-Range Weather Forecasts (ECMWF), solar angles, and Geostationary Operational Environmental Satellite (GOES16) Aerosol Optical Depth (AOD) to model high spatial-temporal PM2.5 concentrations. PM2.5 concentrations as well as in situ weather condition variables are collected from the 31 sensors that are deployed in the Dallas Metropolitan area. Four machine learning models with different predictor variables are developed based on an ensemble approach. Since in situ weather observations are not widely available, ECMWF is used as an alternative data source for weather conditions in studies. Hence, the four established models are compared in three groups. Both models in this first group use weather variables collected from deployed sensors, but one uses NEXRAD and the other does not. In the second group, the two models use weather variables retrieved from ECMWF, one using NEXRAD and one without. In the third group, one model uses weather variables from ECMWF, and the other uses in situ weather variables, both without NEXRAD. The first two environmental groups investigate how NEXRAD can enhance model performances with weather variables collected from in situ observations and ECMWF, respectively. The third group explores how effective using ECMWF as an alternative source of weather conditions. Based on the results, the incorporation of NEXRAD achieves an R2 score of 0.86 and 0.83 for groups 1 and 2, respectively, for an improvement of 2.8% and 9.6% over those models without NEXRAD. For group three, the use of ECMWF as an alternative source of in situ weather observations results in a 0.13 R2 drop. For PM2.5 estimation, weather variables including precipitation, temperature, pressure, and surface pressure from ECMWF and deployed sensors, as well as NEXRAD velocity, are shown to be significant factors.

Lignocellulosic plant biomass is the target feedstock for production of second‐generation biofuels. Ionic liquid (IL) pretreatment can enhance deconstruction of lignocellulosic biomass into sugars that can be fermented to ethanol. Although biomass is typically washed following IL pretreatment, small quantities of residual IL can inhibit fermentative microorganisms downstream, such as the widely used ethanologenic yeast, Saccharomyces cerevisiae. The aim of this study was to identify yeasts tolerant to the IL 1‐ethyl‐3‐methylimidazolium acetate, one of the top performing ILs known for biomass pretreatment. One hundred and sixty eight strains spanning the Ascomycota and Basidiomycota phyla were selected for screening, with emphasis on yeasts within or closely related to the Saccharomyces genus and those tolerant to saline environments. Based on growth in media containing 1‐ethyl‐3‐methylimidazolium acetate, tolerance to IL levels ranging 1–5% was observed for 80 strains. The effect of 1‐ethyl‐3‐methylimidazolium acetate concentration on maximum cell density and growth rate was quantified to rank tolerance. The most tolerant yeasts included strains from the genera Clavispora, Debaryomyces, Galactomyces, Hyphopichia, Kazachstania, Meyerozyma, Naumovozyma, Wickerhamomyces, Yarrowia, and Zygoascus. These yeasts included species known to degrade plant cell wall polysaccharides and those capable of ethanol fermentation. These yeasts warrant further investigation for use in saccharification and fermentation of IL‐pretreated lignocellulosic biomass to ethanol or other products.

This study calibrates an affordable, solar-powered LoRaWAN air quality monitoring prototype using the research-grade Palas Fidas Frog sensor. Motivated by the need for sustainable air quality monitoring in smart city initiatives, this work integrates low-cost, self-sustaining sensors with research-grade instruments, creating a cost-effective hybrid network that enhances both spatial coverage and measurement accuracy. To improve calibration precision, the study leverages the Super Learner machine learning technique, which optimally combines multiple models to achieve robust PM (Particulate Matter) monitoring in low-resource settings. Data was collected by co-locating the Palas sensor and LoRaWAN devices under various climatic conditions to ensure reliability. The LoRaWAN monitor measures PM concentrations alongside meteorological parameters such as temperature, pressure, and humidity. The collected data were calibrated against precise PM concentrations and particle count densities from the Palas sensor. Various regression models were evaluated, with the stacking-based Super Learner model outperforming traditional approaches, achieving an average test R2 value of 0.96 across all target variables, including 0.99 for PM2.5 and 0.91 for PM10.0. This study presents a novel approach by integrating Super Learner-based calibration with LoRaWAN technology, offering a scalable solution for low-cost, high-accuracy air quality monitoring. The findings demonstrate the feasibility of deploying these sensors in urban areas such as the Dallas-Fort Worth metroplex, providing a valuable tool for researchers and policymakers to address air pollution challenges effectively.

Pretreatment with ionic liquids (IL) such as 1-ethyl-3-methylimidazolium chloride or acetate is an effective method for aiding deconstruction of lignocellulosic biomass; however, the residual IL remaining in hydrolysates can be inhibitory to growth of ethanologenic or oleaginous yeasts that have been examined in the literature. The aim of this study was to identify oleaginous yeasts that are tolerant of the IL [C 2 C 1 Im][OAc] and [C 2 C 1 Im]Cl using 45 strains belonging to 38 taxonomically diverse species within phyla Ascomycota and Basidiomycota. Yeasts were cultivated in laboratory medium supplemented with 0, 2, or 4% IL in 96-well plates. The eight most tolerant strains were then cultivated in 10-mL media with no IL, 242mM [C 2 C 1 Im][OAc], or 242mM [C 2 C 1 Im]Cl. The effects of [C 2 C 1 Im] + exposure on cell mass production and lipid accumulation varied at the species and strain level. The acetate salt decreased cell biomass and lipid production more severely than did the chloride ion for six strains. Lipid output was not markedly different (2.1 vs. 2.3 g/L) in Yarrowia lipolytica UCDFST 51-30, but decreased from 5 to 65% in other yeasts. An equimolar concentration of the chloride salt resulted in much milder effects, from 25% decrease to 66% increase in lipid output. The highest lipid outputs in this media were 8.3 and 7.9 g/L produced by Vanrija humicola UCDFST 10-1004 and UCDFST 12-717, respectively. These results demonstrated substantial lipid production in the presence of [C 2 C 1 Im]Cl at concentrations found in lignocellulosic hydrolysates, and thus, these two strains are ideal candidates for further investigation.