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Abstract Most work on how estuarine dynamics impact dissolved oxygen (DO) distributions has focused on tides, but in shallow estuaries with large fetch or small tides, wind can be the primary mixing agent and also drives advection. To investigate how these processes affect DO distributions, an observational study was conducted in the shallow, microtidal Neuse Estuary. Salinity, DO, and velocity profiles were measured at multiple positions along and across the estuary over a 6‐month period. A one‐dimensional model (General Ocean Turbulence Model) provided additional insight into the response of salinity and DO to wind. Salinity and oxygen conservation equation terms were calculated from observations and simulations. Cross‐estuary wind drove lateral circulation and tilted the isohalines, reducing stratification; lateral advection and enhanced mixing reduced vertical gradients and increased the bottom DO. Down‐estuary wind tended to increase the exchange flow and stratification, but concurrently the surface wind‐mixed layer deepened over time. The balance of these processes determined if the water column became fully mixed or remained stratified, and the depth of the pycnocline and oxycline. An expression for steady state surface layer thickness was derived by considering the competition between the horizontal and vertical buoyancy flux, and the predictions agreed well with observations and simulations. Up‐estuary wind inhibited the exchange flow and the combination of advection and mixing homogenized the water column. While these patterns generally held for purely across‐ or along‐channel wind, the response was often more complex as the wind vector varied in orientation and with time. 

This paper presents a comprehensive approach to predicting short-term (for the upcoming 2 weeks) changes in estuarine dissolved oxygen concentrations via machine learning models that integrate historical water sampling, historical and upcoming 2-week meteorological data, and river discharge and discharge metrics. Dissolved oxygen is a critical indicator of ecosystem health, and this approach is implemented for the Neuse River Estuary, North Carolina, U.S.A., which has a long history of hypoxia-related habitat degradation. Through meticulous data preprocessing and feature selection, this research evaluates the predictions of dissolved oxygen concentrations by comparing a recurrent neural network with four other models, including a Multilayer Perceptron, Long Short-Term Memory, Gradient Boosting, and AutoKeras, through sensitivity experiments. The input predictors to our prediction models include water temperature, turbidity, chlorophyll-a, aggregated river discharge, and aggregated wind based on eight directions. By emphasizing the most impactful predictors, we streamlined the model-building processes and built a hindcast system from 2015 to 2019. We found that the recurrent neural network model was most effective in predicting the dissolved oxygen concentrations, with an R2 value of 0.99 at multiple stations. Different from our machine learning hindcast models that used observed upcoming meteorological and discharge data, an actual forecast system would use forecasted meteorological and discharge data. Therefore, an actual operational forecast may have lower accuracy than the hindcast, as determined by the accuracy of the predicted meteorological and discharge data. Nevertheless, our studies enhance our understanding of the factors influencing dissolved oxygen variability and set the basis for the implementation of a predictive tool for environmental monitoring and management. We also emphasized the importance of building station-specific models to improve the prediction results. 

The accurate forecast of algal blooms can provide helpful information for water resource management. However, the complex relationship between environmental variables and blooms makes the forecast challenging. In this study, we build a pipeline incorporating four commonly used machine learning models, Support Vector Regression (SVR), Random Forest Regression (RFR), Wavelet Analysis (WA)-Back Propagation Neural Network (BPNN) and WA-Long Short-Term Memory (LSTM), to predict chlorophyll-a in coastal waters. Two areas with distinct environmental features, the Neuse River Estuary, NC, USA—where machine learning models are applied for short-term algal bloom forecast at single stations for the first time—and the Scripps Pier, CA, USA, are selected. Applying the pipeline, we can easily switch from the NRE forecast to the Scripps Pier forecast with minimum model tuning. The pipeline successfully predicts the occurrence of algal blooms in both regions, with more robustness using WA-LSTM and WA-BPNN than SVR and RFR. The pipeline allows us to find the best results by trying different numbers of neuron hidden layers. The pipeline is easily adaptable to other coastal areas. Experience with the two study regions demonstrated that enrichment of the dataset by including dominant physical processes is necessary to improve chlorophyll prediction when applying it to other aquatic systems. 

ABSTRACT Chromatin is more than a simple genome packaging system, and instead locally distinguished by histone post-translational modifications (PTMs) that can directly change nucleosome structure and / or be “read” by chromatin-associated proteins to mediate downstream events. An accurate understanding of histone PTM binding preference is vital to explain normal function and pathogenesis, and has revealed multiple therapeutic opportunities. Such studies most often use histone peptides, even though these cannot represent the full regulatory potential of nucleosome context. Here we apply a range of complementary and easily adoptable biochemical and genomic approaches to interrogate fully defined peptide and nucleosome targets with a diversity of mono or multivalent chromatin readers. In the resulting data, nucleosome context consistently refined reader binding, and multivalent engagement was more often regulatory than simply additive. This included abrogating the binding of the Polycomb group L3MBTL1 MBT to histone tails with lower methyl states (me1 or me2 at H3K4, H3K9, H3K27, H3K36 or H4K20); and confirmation that the CBX7 chromodomain and AT-hook-like motif (CD-ATL) tandem act as a functional unit to confer specificity for H3K27me3. Further,in vitronucleosome preferences were confirmed byin vivoreader-CUT&RUN genomic mapping. Such data confirms that more representative chromatin substrates provide greater insight to biological mechanism and its disorder in human disease. 

Estuaries function as important transporters, transformers, and producers of organic matter (OM). Along the freshwater to saltwater gradient, the composition of OM is influenced by physical and biogeochemical processes that change spatially and temporally, making it difficult to constrain OM in these ecosystems. In addition, many of the environmental parameters (temperature, precipitation, riverine discharge) controlling OM are expected to change due to climate change. To better understand the environmental drivers of OM quantity (concentration) and quality (absorbance, fluorescence), we assessed both dissolved OM (DOM) and particulate OM (POM) spatially, along the freshwater to saltwater gradient and temporally, for a full year. We found seasonal differences in salinity throughout the estuary due to elevated riverine discharge during the late fall to early spring, with corresponding changes to OM quantity and quality. Using redundancy analysis, we found DOM covaried with salinity (adjusted r2 = 0.35, 0.41 for surface and bottom), indicating terrestrial sources of DOM in riverine discharge were the dominant DOM sources throughout the estuary, while POM covaried with environmental indictors of terrestrial sources (turbidity, adjusted r2 = 0.16, 0.23 for surface and bottom) as well as phytoplankton biomass (chlorophyll-a, adjusted r2 = 0.25, 0.14 for surface and bottom). Responses in OM quantity and quality observed during the period of elevated discharge were similar to studies assessing OM quality following extreme storm events suggesting that regional changes in precipitation, as predicted by climate change, will be as important in changing the estuarine OM pool as episodic storm events in the future. 

Coastal ecosystems display consistent patterns of trade-offs between resistance and resilience to tropical cyclones.