A review of long-term change in surface water natural organic matter concentration in the northern hemisphere and the implications for drinking water treatment.

Reduced atmospheric acid deposition has given rise to recovery from acidification - defined as increasing pH, acid neutralization capacity (ANC), or alkalinity in surface waters. Strong evidence of recovery has been reported across North America and Europe, driving chemical responses. The primary chemical responses identified in this review were increasing concentration and changing character of natural organic matter (NOM) towards predominantly hydrophobic nature. The concentration of NOM also influenced trace metal cycling as many browning surface waters also reported increases in Fe and Al. Further, climate change and other factors (e.g., changing land use) act in concert with reductions in atmospheric deposition to contribute to widespread browning and will have a more pronounced effect as deposition stabilizes. The observed water quality trends have presented challenges for drinking water treatment (e.g., increased chemical dosing, poor filter operations, formation of disinfection by-products) and many facilities may be under designed as a result. This comprehensive review has identified key research areas to be addressed, including 1) a need for comprehensive monitoring programs (e.g., larger timescales; consistency in measurements) to assess climate change impacts on recovery responses and NOM dynamics, and 2) a better understanding of drinking water treatment vulnerabilities and the transition towards robust treatment technologies and solutions that can adapt to climate change and other drivers of changing water quality.

Successful Dietary Therapy in Paediatric Crohn's Disease is Associated with Shifts in Bacterial Dysbiosis and Inflammatory Metabotype Towards Healthy Controls.

BACKGROUND AND AIMS: Nutritional therapy with the Crohn's Disease Exclusion Diet + Partial Enteral Nutrition [CDED+PEN] or Exclusive Enteral Nutrition [EEN] induces remission and reduces inflammation in mild-to-moderate paediatric Crohn's disease [CD]. We aimed to assess if reaching remission with nutritional therapy is mediated by correcting compositional or functional dysbiosis. METHODS: We assessed metagenome sequences, short chain fatty acids [SCFA] and bile acids [BA] in 54 paediatric CD patients reaching remission after nutritional therapy [with CDED + PEN or EEN] [NCT01728870], compared to 26 paediatric healthy controls. RESULTS: Successful dietary therapy decreased the relative abundance of Proteobacteria and increased Firmicutes towards healthy controls. CD patients possessed a mixture of two metabotypes [M1 and M2], whereas all healthy controls had metabotype M1. M1 was characterised by high Bacteroidetes and Firmicutes, low Proteobacteria, and higher SCFA synthesis pathways, and M2 was associated with high Proteobacteria and genes involved in SCFA degradation. M1 contribution increased during diet: 48%, 63%, up to 74% [Weeks 0, 6, 12, respectively.]. By Week 12, genera from Proteobacteria reached relative abundance levels of healthy controls with the exception of E. coli. Despite an increase in SCFA synthesis pathways, remission was not associated with increased SCFAs. Primary BA decreased with EEN but not with CDED+PEN, and secondary BA did not change during diet. CONCLUSION: Successful dietary therapy induced correction of both compositional and functional dysbiosis. However, 12 weeks of diet was not enough to achieve complete correction of dysbiosis. Our data suggests that composition and metabotype are important and change quickly during the early clinical response to dietary intervention. Correction of dysbiosis may therefore be an important future treatment goal for CD.

Novel Application of Survival Models for Predicting Microbial Community Transitions with Variable Selection for Environmental DNA.

Survival analysis is a prolific statistical tool in medicine for inferring risk and time to disease-related events. However, it is underutilized in microbiome research to predict microbial community-mediated events, partly due to the sparsity and high-dimensional nature of the data. We advance the application of Cox proportional hazards (Cox PH) survival models to environmental DNA (eDNA) data with feature selection suitable for filtering irrelevant and redundant taxonomic variables. Selection methods are compared in terms of false positives, sensitivity, and survival estimation accuracy in simulation and in a real data setting to forecast harmful cyanobacterial blooms. A novel extension of a method for selecting microbial biomarkers with survival data (SuRFCox) reliably outperforms other methods. We determine that Cox PH models with SuRFCox-selected predictors are more robust to varied signal, noise, and data correlation structure. SuRFCox also yields the most accurate and consistent prediction of blooms according to cross-validated testing by year over eight different bloom seasons. Identification of common biomarkers among validated survival forecasts over changing conditions has clear biological significance. Survival models with such biomarkers inform risk assessment and provide insight into the causes of critical community transitions. IMPORTANCE In this paper, we report on a novel approach of selecting microorganisms for model-based prediction of the time to critical microbially modulated events (e.g., harmful algal blooms, clinical outcomes, community shifts, etc.). Our novel method for identifying biomarkers from large, dynamic communities of microbes has broad utility to environmental and ecological impact risk assessment and public health. Results will also promote theoretical and practical advancements relevant to the biology of specific organisms. To address the unique challenge posed by diverse environmental conditions and sparse microbes, we developed a novel method of selecting predictors for modeling time-to-event data. Competing methods for selecting predictors are rigorously compared to determine which is the most accurate and generalizable. Model forecasts are applied to show suitable predictors can precisely quantify the risk over time of biological events like harmful cyanobacterial blooms.