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A multi-omics quantitative integrative analysis of lignin biosynthesis can advance the strategic engineering of wood for timber, pulp, and biofuels. Lignin is polymerized from three monomers (monolignols) produced by a grid-like pathway. The pathway in wood formation of Populus trichocarpa has at least 21 genes, encoding enzymes that mediate 37 reactions on 24 metabolites, leading to lignin and affecting wood properties. We perturb these 21 pathway genes and integrate transcriptomic, proteomic, fluxomic and phenomic data from 221 lines selected from ~2000 transgenics (6-month-old). The integrative analysis estimates how changing expression of pathway gene or gene combination affects protein abundance, metabolic-flux, metabolite concentrations, and 25 wood traits, including lignin, tree-growth, density, strength, and saccharification. The analysis then predicts improvements in any of these 25 traits individually or in combinations, through engineering expression of specific monolignol genes. The analysis may lead to greater understanding of other pathways for improved growth and adaptation.

The accumulation of fat, oil, and grease (FOG) deposits in sanitary sewer systems is a global issue responsible for billions of dollars in annual maintenance costs, environmental impact, and public health concerns. Laboratory and data-driven studies have explored the physical and chemical characteristics of FOG deposits, formation kinetics, and factors influencing their accumulation in sewer networks, such as pipe sags, pipe age, and pipe material. However, a scalable modeling platform capable of incorporating FOG kinetics and the key variables driving accumulation is not currently available. This study introduces a new platform for modeling the formation and attachment of FOG deposits in sewer systems, the FOG Sewer Waste Management System (FOG-SWMS). FOG-SWMS was utilized to quantify the impact of pipe sags, pipe age and roughness, and pipe material on the accumulation of FOG deposits in sewer lines. Results indicate that under typical sewer conditions, pipe sags and pipe age may increase the accumulation of FOG deposit mass by as much as approximately 23% and 150%, respectively. Surface pH≥8 , as a proxy for cementitious pipe materials, also resulted in more than a 172% increase in FOG deposit mass accumulation. These results confirm data from previous laboratory studies and machine learning algorithms, and also provide a mechanistic explanation of formation processes. Two full-scale sewer network case studies, Study Area 1 (SA1) and Study Area 2 (SA2), were analyzed using FOG-SWMS, to evaluate the predictive capability of the model to identify FOG deposit accumulation zones. FOG-SWMS successfully predicted approximately 85% and 73% of known accumulation zones in SA1 and SA2, respectively. Significant changes in the spatial and temporal distribution of FOG deposits in response to new commercial and residential development also were demonstrated with the model. Fat, oil, and grease deposits are solid masses that form in sanitary sewer lines and create a variety of issues for municipalities. They are primarily the result of discharged food waste residuals into sewers, which reacts with typical wastewater components to form a solid that can block sewer lines and cause overflow of wastewater. These impacts are common, and equate to an incredible amount of time and money spent on sewer repairs and maintenance. This study introduces a new computer-based model that allows researchers to visualize the formation of FOG deposits in sewer line segments. The model is not limited to specialized researchers; it also is intended to be used by engineers and pretreatment coordinators who may be concerned with the accumulation of FOG deposits in sewer line engineering structures. The main goals of this study were (1) to provide the sewer line workforce with a tool for predicting the accumulation of FOG deposits in sewer networks, especially when there are changes in commercial establishments; (2) to serve as a platform for integrating modern sewer inspection data and sensor deployment studies that are becoming more popular; and (3) to incorporate future advancements in machine learning and AI algorithms. [ABSTRACT FROM AUTHOR]

Time course transcriptome datasets are commonly used to predict key gene regulators associated with stress responses and to explore gene functionality. Techniques developed to extract causal relationships between genes from high throughput time course expression data are limited by low signal levels coupled with noise and sparseness in time points. We deal with these limitations by proposing the Cluster and Differential Alignment Algorithm (CDAA). This algorithm was designed to process transcriptome data by first grouping genes based on stages of activity and then using similarities in gene expression to predict influential connections between individual genes. Regulatory relationships are assigned based on pairwise alignment scores generated using the expression patterns of two genes and some inferred delay between the regulator and the observed activity of the target. We applied the CDAA to an iron deficiency time course microarray dataset to identify regulators that influence 7 target transcription factors known to participate in the Arabidopsis thaliana iron deficiency response. The algorithm predicted that 7 regulators previously unlinked to iron homeostasis influence the expression of these known transcription factors. We validated over half of predicted influential relationships using qRT-PCR expression analysis in mutant backgrounds. One predicted regulator-target relationship was shown to be a direct binding interaction according to yeast one-hybrid (Y1H) analysis. These results serve as a proof of concept emphasizing the utility of the CDAA for identifying unknown or missing nodes in regulatory cascades, providing the fundamental knowledge needed for constructing predictive gene regulatory networks. We propose that this tool can be used successfully for similar time course datasets to extract additional information and infer reliable regulatory connections for individual genes.

Biodosimetry is the currently accepted method for validation of fluence delivery in ultraviolet (UV) reactors for water disinfection. This method utilizes the inactivation of a surrogate microorganism to predict the reduction equivalent fluence and subsequent inactivation of a target pathogen. Two common surrogates--Bacillus subtilis spores and MS-2 coliphage--were used to examine the relationship between surrogate type and biodosimetry results. A pilot-scale LP UV reactor was investigated at two flow conditions (7.5 and 15 gpm) and four different UV 253.7 nm water transmittance (UVT, 1 cm) values between 82 and 91%. The calculated reduction equivalent fluence differed from a maximum of 30% at 7.5 gal./min and 15% at 15 gal./min between the surrogates tested, depending on the UVT. These differences were attributed to the sensitivity of organisms used, hydraulic inefficiences, and UV fluence distribution in the reactor, thus the choice of validation microbe may impact the determination of reduction equivalent fluence in UV reactors. CE Database subject headings: Water treatment: Water flow; Disinfection; Ultraviolet radiation.

The effect of light scattering on measurement of UV absorbance and penetration of germicidal UVC irradiance in a UV reactor were studied. Using a standard spectrophotometer, absorbance measurements exhibited significant error when particles that scatter light were present but could be corrected by integrating sphere spectroscopy. Particles from water treatment plants and wastewater effluents exhibited less scattering (20%-30%) compared with particles such as clay (50%) and alumina (95%-100%). The distribution of light intensity in a UV reactor for a scattering suspension was determined using a spherical chemical actinometry method. Highly scattering alumina particles increased the fluence rate in the reactor near the UV lamp, whereas clay particles and absorbing organic matter reduced the fluence rate. A radiative transfer fluence rate model reasonably predicted the fluence rate of absorbing media and highly scattering suspensions in the UV reactor. OCIS codes: 290.5820, 300.1030.

A study was performed to evaluate alternative methods for predicting the ultraviolet (UV) reactor performance using computational fluid dynamics. The study consists of modeling the UV fluence distribution and microbial inactivation using either Lagrangian or Eulerian methods for both low- and medium-pressure UV reactors. In the Eulerian method, fluence distributions were calculated using a flow-weighted and a mass-weighted fraction technique. The results show that the Eulerian flow-weighted fraction fluence distribution agreed well with the Lagrangian particle tracking fluence distribution when applied to the UV reactor outlet plane. However, when applied to planes downstream from effluent hydraulic structures, the Eulerian fluence distribution method was influenced by the additional convective mixing from these hydraulic structures and predicts a tighter fluence distribution range than the Lagrangian method. The Eulerian approach to modeling microbial inactivation seems comparable to the Lagrangian panicle tracking approach and can be viewed as a suitable alternative to the Lagrangian approach. The results also show that the Eulerian mass-weighted fraction distribution is comparable to the microbial kinetic weighted Lagrangian particle tracking approach, which can provide greater sensitivity to the low fluence regions in the UV reactor. DOI: 10.1061/(ASCE)0733-9372(2005)131:10(1393) CE Database subject headings: Computational fluid dynamics technique; Models; Disinfection; Ultraviolet radiation; Microbes.

A mathematical model was developed to describe nitrogen transport in duckweed-covered static ponds for nutrient recovery from swine lagoon water. A finite difference technique was used to solve the partial differential equations describing the ammonia transport and concentration in the pond. The key parameters in the model include the diffusion coefficient of ammonium in the medium (D) and kinetic constant of nitrogen uptake by duckweed (k). Using one order of magnitude parameter variations, the simulations showed that the model was clearly much more sensitive to D than to k, indicating the process of nitrogen removal in a static pond by duckweed is diffusion limited. Laboratory testing was conducted with Spirodela punctata 7776, a duckweed strain, to calibrate the model. The calibration of the model with experimental data yielded a new ammonium transport coefficient (T) that is 85 times of D value. Model results showed good agreement with depth-wise experimental ammonium concentration and the model also demonstrates that intermittent mixing every 3 h can enhance ammonium uptake. Additionally, an apparent drop in pH near the duckweed mat at the surface was observed that may explain low rates of ammonia emission from duckweed ponds. CE Database subject headings: Ponds; Wastewater treatment; North Carolina; Mathematical models.

A study has been done to evaluate the predictive capabilities of computational fluid dynamics (CFD) models of disinfection contactor hydraulics under model input uncertainty. The study consists of modeling the transport of a chemical tracer in a full-scale reactor and predicting the effluent residence time distribution (RTD) curve. An uncertainty analysis using Monte Carlo probabilistic techniques was used to determine the sensitivity of the effluent RTD to uncertainty in the influent turbulent kinetic energy constant, the turbulent Schmidt number, the wall roughness height, the influent turbulent length scale, and the turbulence model selection. Kruskal-Wallis, Friedman, and Spearman Rho statistical tests were used to evaluate changes in [T.sub.10]/HRT and Morril index due to input uncertainties. The results show that there are some variations in the effluent RTD due to changes in the model input parameters. The effluent RTD variations increased with decreasing contactor hydraulic efficiency or increased mixing. The effluent RTD was most sensitive to uncertainty in the turbulent Schmidt number and the selected turbulence model. DOI: 10.1061/(ASCE)0733-9372(2002)128:11(1056) CE Database keywords: Disinfection; Fluid dynamics; Uncertainty analysis; Cisterns; Hydraulic models.

In North America, temperatures nearing 100°C have been reported in several municipal solid waste landfills. However, the temporal and spatial-dependent processes that result in excessive heat accumulation are not well understood. The objective of this study was to develop a transient finite-element three-dimensional model that incorporates gas-liquid-heat reactive transfer in a landfill with biotic and abiotic reactions and spatially dependent heat transfer processes to better understand heat generation, accumulation, and propagation. The model incorporates gas-liquid-heat reactive transfer with aerobic and anaerobic biological reactions, anaerobic metal corrosion, and ash hydration and carbonation. Increasing boundary temperature, biological reaction rates, and landfill height increase the maximum temperature in the central region of a landfill, whereas the impact of thermal properties of municipal solid waste (MSW) is small. Simulation results predict that placement of ash near the corner of a landfill reduces the size of the elevated temperature region relative to placement in the landfill center. Mixing heat-generating wastes (ash or Al) with MSW decreases maximum temperatures but results in elevated temperatures over a larger fraction of the landfill volume relative to segregated ash disposal. [ABSTRACT FROM AUTHOR]