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High-flow events that significantly impact Water Resource Recovery Facility (WRRF) operations are rare, but accurately predicting these flows could improve treatment operations. Data-driven modeling approaches could be used; however, high flow events that impact operation are an infrequent occurrence, providing limited data from which to learn meaningful patterns. The performance of a statistical model (logistic regression) and two machine learning (ML) models (support vector machine and random forest) were evaluated to predict high flow events one-day-ahead to two plants located in different parts of the United States, Northern Virginia and the Gulf Coast of Texas, with combined and separate sewers, respectively. We compared baseline models (no synthetic data added) to models trained with synthetic data added from two different sampling techniques (SMOTE and ADASYN) that increased the representation of rare events in the training data. Both techniques enhanced the sample size of the very high-flow class, but ADASYN, which focused on generating synthetic samples near decision boundaries, led to greater improvements in model performance (reduced misclassification rates). Random forest combined with ADASYN achieved the best overall performance for both plants, demonstrating its robustness in identifying one-day-ahead extreme flow events to treatment plants. These results suggest that combining sampling techniques with ML has the potential to significantly improve the modeling of high-flow events at treatment plants. Our work will prove useful in building reliable predictive models that can inform management decisions needed for the better control of treatment operations. 

ABSTRACT Microbes can be programmed to record participation in gene transfer by coding biological-recording devices into mobile DNA. Upon DNA uptake, these devices transcribe a catalytic RNA (cat-RNA) that binds to conserved sequences within ribosomal RNA (rRNA) and perform a trans-splicing reaction that adds a barcode to the rRNA. Existing cat-RNA designs were generated to be broad-host range, providing no control over the organisms that were barcoded. To achieve control over the organisms barcoded by cat-RNA, we created a program called Ribodesigner that uses input sets of rRNA sequences to create designs with varying specificities. We show how this algorithm can be used to identify designs that enable kingdom-wide barcoding, or selective barcoding of specific taxonomic groups within a kingdom. We use Ribodesigner to create cat-RNA designs that target Pseudomonadales while avoiding Enterobacterales, and we compare the performance of one design to a cat-RNA that was previously found to be broad host range. When conjugated into a mixture ofEscherichia coliandPseudomonas putida, the new design presents increased selectivity compared to a broad host range cat-RNA. Ribodesigner is expected to aid in developing cat-RNA that store information within user-defined sets of microbes in environmental communities for gene transfer studies. GRAPHICAL ABSTRACT 

SUMMARY Engineered microbes are being programmed using synthetic DNA for applications in soil to overcome global challenges related to climate change, energy, food security, and pollution. However, we cannot yet predict gene transfer processes in soil to assess the frequency of unintentional transfer of engineered DNA to environmental microbes when applying synthetic biology technologies at scale. This challenge exists because of the complex and heterogeneous characteristics of soils, which contribute to the fitness and transport of cells and the exchange of genetic material within communities. Here, we describe knowledge gaps about gene transfer across soil microbiomes. We propose strategies to improve our understanding of gene transfer across soil communities, highlight the need to benchmark the performance of biocontainment measuresin situ, and discuss responsibly engaging community stakeholders. We highlight opportunities to address knowledge gaps, such as creating a set of soil standards for studying gene transfer across diverse soil types and measuring gene transfer host range across microbiomes using emerging technologies. By comparing gene transfer rates, host range, and persistence of engineered microbes across different soils, we posit that community-scale, environment-specific models can be built that anticipate biotechnology risks. Such studies will enable the design of safer biotechnologies that allow us to realize the benefits of synthetic biology and mitigate risks associated with the release of such technologies. 

Membrane‐aerated biofilm reactors (MABRs) are being increasingly being implemented at full‐scale for domestic wastewater treatment and effective biofilm control is critical to their performance. This study investigated the impact of three biofilm scouring strategies on nitrogen removal performance of a pilot‐scale MABR operated in Houston, TX: (1) regular air scouring, (2) high intensity air scouring, and (3) high liquid flow scouring. Normal and high intensity air scouring regimes and a high liquid flow scour (10× baseline flow) were each tested sequentially. High NH₄⁺‐N removal efficiency of 52% in flow‐through mode was observed post‐high liquid flow scouring, which was comparable to the performance during the intense scouring regime. The absolute abundance ofamoAgene for ammonia oxidizing bacteria (AOB) increased significantly by over 200%, between pre‐ and post‐high liquid flow scouring. The energy consumption was 43% lower for the combination of high liquid flow scouring with regular air scouring as compared to the intense air scouring. This study showed that high liquid flows may be utilized as an energy‐efficient biofilm control strategy in nitrifying MABR systems. Practitioner PointsPilot‐scale MABR reactors were operated with different scouring settings: regular aeration, intense aeration, and high liquid flow.High liquid flow scouring improved nitrification efficiency, comparable to intense scouring.High liquid flow scouring selected for nitrifiers as seen by an increase in AOB quantified asamoAgene abundance.Using high liquid flow with regular aeration scouring reduces electrical energy consumption by 43% as compared to intense aeration scouring.High liquid flows may be used as an energy‐efficient biofilm control strategy to improve nitrification performance in MABR systems. 

ABSTRACT Microbial biosensors that convert environmental information into real-time visual outputs are limited in their sensing abilities in complex environments, such as soil and wastewater, due to optical inaccessibility. Biosensors that could record transient exposure to analytes within a large time window for later retrieval represent a promising approach to solve the accessibility problem. Here, we test the performance of recombinase-memory biosensors that sense a sugar (arabinose) and a microbial communication molecule (3-oxo-C12-L-homoserine lactone) over 8 days (~70 generations) following analyte exposure. These biosensors sense the analyte and trigger the expression of a recombinase enzyme which flips a segment of DNA, creating a genetic memory, and initiates fluorescent protein expression. The initial designs failed over time due to unintended DNA flipping in the absence of the analyte and loss of the flipped state after exposure to the analyte. Biosensor performance was improved by decreasing recombinase expression, removing the fluorescent protein output, and using quantitative PCR to read out stored information. Application of memory biosensors in wastewater isolates achieved memory of analyte exposure in an uncharacterizedPseudomonasisolate. By returning these engineered isolates to their native environments, recombinase-memory systems are expected to enable longer duration andin situinvestigation of microbial signaling, cross-feeding, community shifts, and gene transfer beyond the reach of traditional environmental biosensors.IMPORTANCEMicrobes mediate ecological processes over timescales that can far exceed the half-lives of transient metabolites and signals that drive their collective behaviors. We investigated strategies for engineering microbes to stably record their transient exposure to a chemical over many generations through DNA rearrangements. We identify genetic architectures that improve memory biosensor performance and characterize these in wastewater isolates. Memory biosensors are expected to be useful for monitoring cell-cell signals in biofilms, detecting transient exposure to chemical pollutants, and observing microbial cross-feeding through short-lived metabolites within cryptic methane, nitrogen, and sulfur cycling processes. They will also enablein situstudies of microbial responses to ephemeral environmental changes, or other ecological processes that are currently challenging to monitor non-destructively using real-time biosensors and analytical instruments.