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.

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.

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.

PKCε Inhibits Neuronal Dendritic Spine Development through Dual Phosphorylation of Ephexin5.

Protein kinase C (PKC)-dependent mechanisms promote synaptic function in the mature brain. However, the roles of PKC signaling during synapse development remain largely unknown. Investigating each brain-enriched PKC isoform in early neuronal development, we show that PKCε acutely and specifically reduces the number of dendritic spines, sites of eventual synapse formation on developing dendrites. This PKCε-mediated spine suppression is temporally restricted to immature neurons and mediated through the phosphorylation and activation of Ephexin5, a RhoA guanine nucleotide exchange factor (GEF) and inhibitor of hippocampal synapse formation. Our data suggest that PKCε acts as an early developmental inhibitor of dendritic spine formation, in contrast to its emerging pro-synaptic roles in mature brain function. Moreover, we identify a substrate of PKCε, Ephexin5, whose early-elevated expression in developing neurons may in part explain the mechanism by which PKCε plays seemingly opposing roles that depend on neuronal maturity.

Nasal neuron PET imaging quantifies neuron generation and degeneration.

Olfactory dysfunction is broadly associated with neurodevelopmental and neurodegenerative diseases and predicts increased mortality rates in healthy individuals. Conventional measurements of olfactory health assess odor processing pathways within the brain and provide a limited understanding of primary odor detection. Quantification of the olfactory sensory neurons (OSNs), which detect odors within the nasal cavity, would provide insight into the etiology of olfactory dysfunction associated with disease and mortality. Notably, OSNs are continually replenished by adult neurogenesis in mammals, including humans, so OSN measurements are primed to provide specialized insights into neurological disease. Here, we have evaluated a PET radiotracer, [11C]GV1-57, that specifically binds mature OSNs and quantifies the mature OSN population in vivo. [11C]GV1-57 monitored native OSN population dynamics in rodents, detecting OSN generation during postnatal development and aging-associated neurodegeneration. [11C]GV1-57 additionally measured rates of neuron regeneration after acute injury and early-stage OSN deficits in a rodent tauopathy model of neurodegenerative disease. Preliminary assessment in nonhuman primates suggested maintained uptake and saturable binding of [18F]GV1-57 in primate nasal epithelium, supporting its translational potential. Future applications for GV1-57 include monitoring additional diseases or conditions associated with olfactory dysregulation, including cognitive decline, as well as monitoring effects of neuroregenerative or neuroprotective therapeutics.

Identification of FHL1 as a therapeutic target for Duchenne muscular dystrophy.

Utrophin is a potential therapeutic target for the fatal muscle disease, Duchenne muscular dystrophy (DMD). In adult skeletal muscle, utrophin is restricted to the neuromuscular and myotendinous junctions and can compensate for dystrophin loss in mdx mice, a mouse model of DMD, but requires sarcolemmal localization. NFATc1-mediated transcription regulates utrophin expression and the LIM protein, FHL1 which promotes muscle hypertrophy, is a transcriptional activator of NFATc1. By generating mdx/FHL1-transgenic mice, we demonstrate that FHL1 potentiates NFATc1 activation of utrophin to ameliorate the dystrophic pathology. Transgenic FHL1 expression increased sarcolemmal membrane stability, reduced muscle degeneration, decreased inflammation and conferred protection from contraction-induced injury in mdx mice. Significantly, FHL1 expression also reduced progressive muscle degeneration and fibrosis in the diaphragm of aged mdx mice. FHL1 enhanced NFATc1 activation of the utrophin promoter and increased sarcolemmal expression of utrophin in muscles of mdx mice, directing the assembly of a substitute utrophin-glycoprotein complex, and revealing a novel FHL1-NFATc1-utrophin signaling axis that can functionally compensate for dystrophin.