Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data.

The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations. The application of these approaches, however, is challenged by limited sample availability and unbalanced water quality datasets. In this study, we estimated the E. coli concentration in an irrigation pond in Maryland, USA, during the summer season using demosaiced natural color (red, green, and blue: RGB) imagery in the visible and infrared spectral ranges, and a set of 14 water quality parameters. We did this by deploying four machine learning models - Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), and K-nearest Neighbor (KNN) - under three data utilization scenarios: water quality parameters only, combined water quality and small unmanned aircraft system (sUAS)-based RGB data, and RGB data only. To select the training and test datasets, we applied two data-splitting methods: ordinary and quantile data splitting. These methods provided a constant splitting ratio in each decile of the E. coli concentration distribution. Quantile data splitting resulted in better model performance metrics and smaller differences between the metrics for both the training and testing datasets. When trained with quantile data splitting after hyperparameter optimization, models RF, GBM, and XGB had R2 values above 0.847 for the training dataset and above 0.689 for the test dataset. The combination of water quality and RGB imagery data resulted in a higher R2 value (>0.896) for the test dataset. Shapley additive explanations (SHAP) of the relative importance of variables revealed that the visible blue spectrum intensity and water temperature were the most influential parameters in the RF model. Demosaiced RGB imagery served as a useful predictor of E. coli concentration in the studied irrigation pond.

Multiscale spatiotemporal variability of fecal indicator bacteria and associated particle size distributions in the sandy bottom sediments of a Pennsylvania creek.

Concentrations of the fecal indicator bacteria (FIB) Escherichia coli and enterococci are used to assess microbial impairment in irrigation and recreation water sources. Although the FIB concentrations' variability at large temporal scales, such as seasons, and large spatial scales encompassing different land use has been studied, the knowledge about smaller scale variability remains sparse. This work aimed to research the small-scale variability of E. coli and enterococci in a montane creek with sandy bottom sediments. Sediment samples were collected weekly for a year in triplicate at sampling sites in a forested headwater, an agricultural area, and a mixed urban-agricultural area. The average weekly change in concentrations was from two times at the forested site to five times at the urban-agricultural site. Mean relative deviations from averages across sampling locations increased from -25% at the forested site to 45% at the urban-agricultural site. This trend was also observed separately over the cold and warm seasons. Over a week without precipitation, E. coli concentrations decreased on average by 20% in warm period and by 45% in cold period; the enterococci concentration declined by 12% in both cold and warm periods. The sediment particle size distributions were significantly different among the three sites and between the cold and warm seasons. Rankings of sediment fine mass fractions and FIB concentrations were positively correlated at two of three sampling sites in more than 70% of observation dates. The results of this work indicate the need to evaluate the uncertainty of sediment FIB concentrations before designing sediment FIB monitoring quality.

The Role of Fibroblast Growth Factor Signaling in Somitogenesis.

Fibroblast growth factor (FGF) signaling is conserved from cnidaria to mammals (Ornitz and Itoh, 2022) and it regulates several critical processes such as differentiation, proliferation, apoptosis, cell migration, and embryonic development. One pivotal process FGF signaling controls is the division of vertebrate paraxial mesoderm into repeated segmented units called somites (i.e., somitogenesis). Somite segmentation occurs periodically and sequentially in a head-to-tail manner, and lays down the plan for compartmentalized development of the vertebrate body axis (Gomez et al., 2008). These somites later give rise to vertebrae, tendons, and skeletal muscle. Somite segments form sequentially from the anterior end of the presomitic mesoderm (PSM). The periodicity of somite segmentation is conferred by the segmentation clock, comprising oscillatory expression of Hairy and enhancer-of-split (Her/Hes) genes in the PSM. The positional information for somite boundaries is instructed by the double phosphorylated extracellular signal-regulated kinase (ppERK) gradient, which is the relevant readout of FGF signaling during somitogenesis (Sawada et al., 2001; Delfini et al., 2005; Simsek and Ozbudak, 2018; Simsek et al., 2023). In this review, we summarize the crosstalk between the segmentation clock and FGF/ppERK gradient and discuss how that leads to periodic somite boundary formation. We also draw attention to outstanding questions regarding the interconnected roles of the segmentation clock and ppERK gradient, and close with suggested future directions of study.

ETV2 primes hematoendothelial gene enhancers prior to hematoendothelial fate commitment.

Mechanisms underlying distinct specification, commitment, and differentiation phases of cell fate determination remain undefined due to difficulties capturing these processes. Here, we interrogate the activity of ETV2, a transcription factor necessary and sufficient for hematoendothelial differentiation, within isolated fate intermediates. We observe transcriptional upregulation of Etv2 and opening of ETV2-binding sites, indicating new ETV2 binding, in a common cardiac-hematoendothelial progenitor population. Accessible ETV2-binding sites are active at the Etv2 locus but not at other hematoendothelial regulator genes. Hematoendothelial commitment coincides with the activation of a small repertoire of previously accessible ETV2-binding sites at hematoendothelial regulators. Hematoendothelial differentiation accompanies activation of a large repertoire of new ETV2-binding sites and upregulation of hematopoietic and endothelial gene regulatory networks. This work distinguishes specification, commitment, and sublineage differentiation phases of ETV2-dependent transcription and suggests that the shift from ETV2 binding to ETV2-bound enhancer activation, not ETV2 binding to target enhancers, drives hematoendothelial fate commitment.

Hedgehog signaling activates a mammalian heterochronic gene regulatory network controlling differentiation timing across lineages.

Many developmental signaling pathways have been implicated in lineage-specific differentiation; however, mechanisms that explicitly control differentiation timing remain poorly defined in mammals. We report that murine Hedgehog signaling is a heterochronic pathway that determines the timing of progenitor differentiation. Hedgehog activity was necessary to prevent premature differentiation of second heart field (SHF) cardiac progenitors in mouse embryos, and the Hedgehog transcription factor GLI1 was sufficient to delay differentiation of cardiac progenitors in vitro. GLI1 directly activated a de novo progenitor-specific network in vitro, akin to that of SHF progenitors in vivo, which prevented the onset of the cardiac differentiation program. A Hedgehog signaling-dependent active-to-repressive GLI transition functioned as a differentiation timer, restricting the progenitor network to the SHF. GLI1 expression was associated with progenitor status across germ layers, and it delayed the differentiation of neural progenitors in vitro, suggesting a broad role for Hedgehog signaling as a heterochronic pathway.

Intra-daily variation of Escherichia coli concentrations in agricultural irrigation ponds.

Microbial water quality is determined by comparing observed Escherichia coli concentrations with regulatory thresholds. Measured concentrations can be expected to change throughout the course of a day in response to diurnal variation in environmental conditions, such as solar radiation and temperature. Therefore, the time of day at which samples are taken is an important factor within microbial water quality measurements. However, little is known about the diurnal variations of E. coli concentrations in surface sources of irrigation water. The objectives of this work were to evaluate the intra-daily dynamics of E. coli in three irrigation ponds in Maryland over several years and to determine the water quality parameters to which E. coli populations are most sensitive. Water sampling was conducted across the ponds at 0900, 1200, and 1500 h on a total of 17 dates in the summers of 2019-2021. One-way ANOVA revealed significant diurnal variability in E. coli concentrations in Pond (P)1 and P2, whereas no significant effects were observed in P3. Escherichia coli die-off rates calculated between sampling time points in the same day were significantly higher in P2 than in P1 and P3, and these rates ranged from 0.005 to 0.799 h-1 across ponds. Concentrations of dissolved oxygen, pH, conductivity, and turbidity exerted the most control over E. coli populations. Results of this work demonstrate that sampling in the early-morning hours provides the most conservative assessment of the microbial quality of irrigation waters.

Prediction of E. coli Concentrations in Agricultural Pond Waters: Application and Comparison of Machine Learning Algorithms.

The microbial quality of irrigation water is an important issue as the use of contaminated waters has been linked to several foodborne outbreaks. To expedite microbial water quality determinations, many researchers estimate concentrations of the microbial contamination indicator Escherichia coli (E. coli) from the concentrations of physiochemical water quality parameters. However, these relationships are often non-linear and exhibit changes above or below certain threshold values. Machine learning (ML) algorithms have been shown to make accurate predictions in datasets with complex relationships. The purpose of this work was to evaluate several ML models for the prediction of E. coli in agricultural pond waters. Two ponds in Maryland were monitored from 2016 to 2018 during the irrigation season. E. coli concentrations along with 12 other water quality parameters were measured in water samples. The resulting datasets were used to predict E. coli using stochastic gradient boosting (SGB) machines, random forest (RF), support vector machines (SVM), and k-nearest neighbor (kNN) algorithms. The RF model provided the lowest RMSE value for predicted E. coli concentrations in both ponds in individual years and over consecutive years in almost all cases. For individual years, the RMSE of the predicted E. coli concentrations (log10 CFU 100 ml-1) ranged from 0.244 to 0.346 and 0.304 to 0.418 for Pond 1 and 2, respectively. For the 3-year datasets, these values were 0.334 and 0.381 for Pond 1 and 2, respectively. In most cases there was no significant difference (P > 0.05) between the RMSE of RF and other ML models when these RMSE were treated as statistics derived from 10-fold cross-validation performed with five repeats. Important E. coli predictors were turbidity, dissolved organic matter content, specific conductance, chlorophyll concentration, and temperature. Model predictive performance did not significantly differ when 5 predictors were used vs. 8 or 12, indicating that more tedious and costly measurements provide no substantial improvement in the predictive accuracy of the evaluated algorithms.

Depth-Dependent Response of Fecal Indicator Bacteria in Sediments to Changes in Water Column Nutrient Levels.

Concentrations of in bottom sediments can influence the assessment of microbial stream water quality. Runoff events bring nutrients to streams that can support the growth of in sediments. The objective of this work was to evaluate depth-dependent changes in populations after nutrients are introduced to the water column. Bovine feces were collected fresh and mixed into sediment. Studies were performed in a microcosm system with continuous flow of synthetic stream water over inoculated sediment. Dilutions of autoclaved bovine manure were added to water on Day 16 at two concentrations, and KBr tracer was introduced into the water column to evaluate ion diffusion. Concentrations of , total coliforms, and total aerobic heterotrophic bacteria, along with orthophosphate-P and ammonium N, were monitored in water and sediment for 32 d. Sediment samples were analyzed in 0- to 1-cm and 1- to 3-cm sectioned depths. Concentrations of and total coliforms in top sediments were approximately one order of magnitude greater than in bottom sediments throughout the experiment. Introduction of nutrients to the water column triggered an increase of nutrient levels in both top and bottom sediments and increased concentrations of bacteria in the water. However, the added nutrients had a limited effect on in sediment where bacterial inactivation continued. Vertical gradients of concentrations in sediments persisted during the inactivation periods both before and after nutrient addition to the water column.

Spatial Patterns of Escherichia coli Concentrations in Sediment before and after High-Flow Events in a First-Order Creek.

Understanding spatial patterns of in freshwater sediments is necessary to characterize sediments as microbial reservoirs and to evaluate the impact of sediment resuspension on microbial water quality in watersheds. Sediment particle size distributions and streambed concentrations were measured along a 500-m-long reach of a first-order creek 1 d before and on Days 1, 3, 6, and 10 after each of two artificial high-flow events, with natural high-flow events also occurring within the sampling periods. Spatial variability of was greater in sediments than in water within any given sampling; however, variation between sampling days was greater for water than for sediment. The mean relative difference analysis revealed temporally stable patterns of concentrations in sediments. rich locations along the reach corresponded to areas with higher organic matter and fine particle contents. Although low ( < 0.5 d) or negative survival rates were observed at most locations along the reach during times where no precipitation was recorded, a small number of locations showed such large concentration increase that on average the survival rate remained positive at the reach scale. The studied creek appears to have hot spots of concentration increase, where conditions for populations to increase are much more favorable than in most other locations across the reach. The effect of this increase can be seen at the reach scale but is difficult to discern without individual sampling that is dense in space and time.

Escherichia coli Export from Manured Fields Depends on the Time between the Start of Rainfall and Runoff Initiation.

After rainfall or irrigation begins, surface-applied chemicals and manure-borne microorganisms typically enter the soil with infiltration until the soil saturates, after which time the chemicals and microbes are exported from the field in the overland flow. This process is viewed as a reason for the dependence of chemical export on the time between rainfall start and runoff initiation that has been documented for agricultural chemicals. The objective of this work was to observe and quantify such dependence for released from solid farmyard dairy manure in field conditions. Experiments were performed for 6 yr and consisted of manure application followed by an immediate simulated rainfall event and a second event 1 wk later. The nonlinearity of the release seen in laboratory and plot studies did not manifest itself in the field. The number of exported cells in runoff was proportional to rainfall depth after runoff initiation in each trial. The proportionality coefficient, termed export rate, demonstrated a strong dependence on the runoff delay time that could be approximated with the exponential decrease. The export rate decreased by one order of magnitude when the rainfall depth at runoff initiation increased from 18 to 42 mm. The same dependence could approximate data from the simulated rainfall event 1 wk after the manure application, assuming that the initial content in manure after 1 wk of weathering was 10% of the initial content. Overall, accounting for the dependence of manure-borne export on the runoff delay time should improve the accuracy of export predictions related to the assessment of agricultural practices on microbial water quality.

Temporal Stability of Escherichia coli Concentrations in Waters of Two Irrigation Ponds in Maryland.

Fecal contamination of water sources is an important water quality issue for agricultural irrigation ponds. Escherichia coli concentrations are commonly used to evaluate recreational and irrigation water quality. We hypothesized that there may exist temporally stable spatial patterns of E. coli concentrations across ponds, meaning that some areas mostly have higher and other areas mostly lower than average concentrations of E. coli To test this hypothesis, we sampled two irrigation ponds in Maryland at nodes of spatial grids biweekly during the summer of 2016. Environmental covariates-temperature, turbidity, conductivity, pH, dissolved oxygen, chlorophyll a, and nutrients-were measured in conjunction with E. coli concentrations. Temporal stability was assessed using mean relative differences between measurements in each location and averaged measurements across ponds. Temporally stable spatial patterns of E. coli concentrations and the majority of environmental covariates were expressed for both ponds. In the pond interior, larger relative mean differences in chlorophyll a corresponded to smaller mean relative differences in E. coli concentrations, with a Spearman's rank correlation coefficient of 0.819. Turbidity and ammonium concentrations were the two other environmental covariates with the largest positive correlations between their location ranks and the E. coli concentration location ranks. Tenfold differences were found between geometric mean E. coli concentrations in locations that were consistently high or consistently low. The existence of temporally stable patterns of E. coli concentrations can affect the results of microbial water quality assessment in ponds and should be accounted for in microbial water quality monitoring design.IMPORTANCE The microbial quality of water in irrigation water sources must be assessed to prevent the spread of microbes that can cause disease in humans because of produce consumption. The microbial quality of irrigation water is evaluated based on concentrations of Escherichia coli as the indicator organism. Given the high spatial and temporal variability of E. coli concentrations in irrigation water sources, recommendations are needed on where and when samples of water have to be taken for microbial analysis. This work demonstrates the presence of a temporally stable spatial pattern in the distributions of E. coli concentrations across irrigation ponds. The ponds studied had zones where E. coli concentrations were mostly higher than average and zones where the concentrations were mostly lower than average over the entire observation period, covering the season when water was used for irrigation. Accounting for the existence of such zones will improve the design and implementation of microbial water quality monitoring.

Enrichment of stream water with fecal indicator organisms during baseflow periods.

Fecal indicator organisms (FIOs) are generally believed to be present in surface waters due solely to direct deposition of feces or through transport in runoff. However, emerging evidence points toward hyporheic exchange between sediment pore water and the overlying water column during baseflow periods as a source of FIOs is surface waters. The objective of this work was to (a) propose a mass balance-based technique for estimating changes of FIO concentrations in the same volume of water (or "slug") from the inlet to outlet of stream reaches in baseflow conditions and (b) to use such enumeration to estimate rate of the FIO release to stream water column. Concentrations of Escherichia coli (E. coli) and enterococci were measured in the slug while simultaneously monitoring the movement of a conservative tracer, Br that labeled the slug. Concentrations of E. coli in the slug were significantly larger (P = 0.035, P = 0.001, and P = 0.001, respectively) at the outlet reach in all three replications, while enterococci concentrations were significantly larger in two of three replications (P = 0.001, P < 0.001, and P = 0.602). When estimated without accounting for die-off in water column, FIO net release rates across replications ranged from 36 to 57 cells m-2 s-1 and 43 to 87 cells m-2 s-1 for E. coli and enterococci, respectively. These release rates were 5 to 20% higher when the die-off in water column was taken into account. No diurnal trends were observed in indicator concentrations. No FIO sources other than bottom sediment have been observed during the baseflow period. FIOs are released into stream water column through hyporheic exchange during baseflow periods.

Irrigation waters and pipe-based biofilms as sources for antibiotic-resistant bacteria.

The presence of antibiotic-resistant bacteria in environmental surface waters has gained recent attention. Wastewater and drinking water distribution systems are known to disseminate antibiotic-resistant bacteria, with the biofilms that form on the inner-surfaces of the pipeline as a hot spot for proliferation and gene exchange. Pipe-based irrigation systems that utilize surface waters may contribute to the dissemination of antibiotic-resistant bacteria in a similar manner. We conducted irrigation events at a perennial stream on a weekly basis for 1 month, and the concentrations of total heterotrophic bacteria, total coliforms, and fecal coliforms, as well as the concentrations of these bacterial groups that were resistant to ampicillin and tetracycline, were monitored at the intake water. Prior to each of the latter three events, residual pipe water was sampled and 6-in. sections of pipeline (coupons) were detached from the system, and biofilm from the inner-wall was removed and analyzed for total protein content and the above bacteria. Isolates of biofilm-associated bacteria were screened for resistance to a panel of seven antibiotics, representing five antibiotic classes. All of the monitored bacteria grew substantially in the residual water between irrigation events, and the biomass of the biofilm steadily increased from week to week. The percentages of biofilm-associated isolates that were resistant to antibiotics on the panel sometimes increased between events. Multiple-drug resistance was observed for all bacterial groups, most often for fecal coliforms, and the distributions of the numbers of antibiotics that the total coliforms and fecal coliforms were resistant to were subject to change from week to week. Results from this study highlight irrigation waters as a potential source for antibiotic-resistant bacteria, which can subsequently become incorporated into and proliferate within irrigation pipe-based biofilms.