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1. Contribution of urea-N to nitrification in the Southern Ocean west of the Antarctic Peninsula

   Hollibaugh, J.T.; Okotie-Oyekan, A.; Damashek, J.; H. Ducklow, H.; Popp, B.N.; Wallsgrove, N.; Allen, T. (October 2023, Applied and environmental microbiology)

   NOTE: THIS MS IS TO BE SUBMITTED FOLLOWING OUTCOME OF NATURE COMMUNICATIONS REVIEW OF A COMPANION MS. ABSTRACT (246 words, 1,457 characters including spaces) We measured the oxidation rates of N supplied as urea (UO) and ammonium (AO) in continental shelf and slope waters of the Southern Ocean west of the Antarctic Peninsula during the austral summer of 2018. The response of rates to substrate concentration varied by water mass. Rates increased moderately (up to 200%) with 440 vs 6 nM substrate amendments to samples from the Winter Water (WW, sampled at 35-100 m), but decreased (down to 7%) in samples from the Circumpolar Deep Water (CDW, 175-1000 m). AO rates decreased more than UO rates. This response suggests that CDW Thaumarchaeota are not well adapted to short-term variation in substrate concentrations and that even low amendments (we used 44 or 47 nM) may inhibit oxidation. Rates of AO and UO were not correlated; nor were they correlated with the abundance, or ratios of abundance, of marker genes; or with [NH4+]; or [urea]. UO and AO were distributed uniformly across the study area within a water mass; however, they displayed strong vertical gradients. Rates in most samples from Antarctic Surface Water (ASW, 10-15 m) were below the limit of detection. Highest rates of both processes were in samples from the WW (21.2 and 1.6 nmol L-1 d-1 for AO vs UO, respectively) and CDW (7.9 and 2.5 nmol L-1 d-1), comparable to rates from the study area reported previously. The contribution of UO to nitrite production was ~24% of that from AO alone, comparable to ratios measured at lower latitudes. 

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2. Prediction of lactate concentrations after cardiac surgery using machine learning and deep learning approaches

   https://doi.org/10.3389/fmed.2023.1165912  

   Kobayashi, Yuta; Peng, Yu-Chung; Yu, Evan; Bush, Brian; Jung, Youn-Hoa; Murphy, Zachary; Goeddel, Lee; Whitman, Glenn; Venkataraman, Archana; Brown, Charles H. (September 2023, Frontiers in Medicine)

   BackgroundAlthough conventional prediction models for surgical patients often ignore intraoperative time-series data, deep learning approaches are well-suited to incorporate time-varying and non-linear data with complex interactions. Blood lactate concentration is one important clinical marker that can reflect the adequacy of systemic perfusion during cardiac surgery. During cardiac surgery and cardiopulmonary bypass, minute-level data is available on key parameters that affect perfusion. The goal of this study was to use machine learning and deep learning approaches to predict maximum blood lactate concentrations after cardiac surgery. We hypothesized that models using minute-level intraoperative data as inputs would have the best predictive performance. MethodsAdults who underwent cardiac surgery with cardiopulmonary bypass were eligible. The primary outcome was maximum lactate concentration within 24 h postoperatively. We considered three classes of predictive models, using the performance metric of mean absolute error across testing folds: (1) static models using baseline preoperative variables, (2) augmentation of the static models with intraoperative statistics, and (3) a dynamic approach that integrates preoperative variables with intraoperative time series data. Results2,187 patients were included. For three models that only used baseline characteristics (linear regression, random forest, artificial neural network) to predict maximum postoperative lactate concentration, the prediction error ranged from a median of 2.52 mmol/L (IQR 2.46, 2.56) to 2.58 mmol/L (IQR 2.54, 2.60). The inclusion of intraoperative summary statistics (including intraoperative lactate concentration) improved model performance, with the prediction error ranging from a median of 2.09 mmol/L (IQR 2.04, 2.14) to 2.12 mmol/L (IQR 2.06, 2.16). For two modelling approaches (recurrent neural network, transformer) that can utilize intraoperative time-series data, the lowest prediction error was obtained with a range of median 1.96 mmol/L (IQR 1.87, 2.05) to 1.97 mmol/L (IQR 1.92, 2.05). Intraoperative lactate concentration was the most important predictive feature based on Shapley additive values. Anemia and weight were also important predictors, but there was heterogeneity in the importance of other features. ConclusionPostoperative lactate concentrations can be predicted using baseline and intraoperative data with moderate accuracy. These results reflect the value of intraoperative data in the prediction of clinically relevant outcomes to guide perioperative management. 

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3. Nanosheets of CuCo2O4 As a High-Performance Electrocatalyst in Urea Oxidation

   https://doi.org/10.3390/app9040793  

   Zequine, Camila; Wang, Fangzhou; Li, Xianglin; Guragain, Deepa; Mishra, S.R.; Siam, K.; Kahol, P.; Gupta, Ram (February 2019, Applied Sciences)

   The urea oxidation reaction (UOR) is a possible solution to solve the world’s energy crisis. Fuel cells have been used in the UOR to generate hydrogen with a lower potential compared to water splitting, decreasing the costs of energy production. Urea is abundantly present in agricultural waste and in industrial and human wastewater. Besides generating hydrogen, this reaction provides a pathway to eliminate urea, which is a hazard in the environment and to people’s health. In this study, nanosheets of CuCo2O4 grown on nickel foam were synthesized as an electrocatalyst for urea oxidation to generate hydrogen as a green fuel. The synthesized electrocatalyst was characterized using X-ray diffraction, scanning electron microscopy, and X-ray photoelectron spectroscopy. The electroactivity of CuCo2O4 towards the oxidation of urea in alkaline solution was evaluated using electrochemical measurements. Nanosheets of CuCo2O4 grown on nickel foam required the potential of 1.36 V in 1 M KOH with 0.33 M urea to deliver a current density of 10 mA/cm2. The CuCo2O4 electrode was electrochemically stable for over 15 h of continuous measurements. The high catalytic activities for the hydrogen evolution reaction make the CuCo2O4 electrode a bifunctional catalyst and a promising electroactive material for hydrogen production. The two-electrode electrolyzer demanded a potential of 1.45 V, which was 260 mV less than that for the urea-free counterpart. Our study suggests that the CuCo2O4 electrode can be a promising material as an efficient UOR catalyst for fuel cells to generate hydrogen at a low cost. 

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4. Lignin phenol quantification from machine learning‐assisted decomposition of liquid chromatography‐absorbance spectroscopy data

   https://doi.org/10.1002/lom3.10561  

   Bruhn, Anders Dalhoff; Wünsch, Urban; Osburn, Christopher L.; Rudolph, Jacob C.; Stedmon, Colin A. (June 2023, Limnology and Oceanography: Methods)

   Abstract Analysis of lignin in seawater is essential to understanding the fate of terrestrial dissolved organic matter (DOM) in the ocean and its role in the carbon cycle. Lignin is typically quantified by gas or liquid chromatography, coupled with mass spectrometry (GC‐MS or LC‐MS). MS instrumentation can be relatively expensive to purchase and maintain. Here we present an improved approach for quantification of lignin phenols using LC and absorbance detection. The approach applies a modified version of parallel factor analysis (PARAFAC2) to 2^ndderivative absorbance chromatograms. It is capable of isolating individual elution profiles of analytes despite co‐elution and overall improves sensitivity and specificity, compared to manual integration methods. For most lignin phenols, detection limits below 5 nmol L⁻¹were achieved, which is comparable to MS detection. The reproducibility across all laboratory stages for our reference material showed a relative standard deviation between 1.47% and 16.84% for all 11 lignin phenols. Changing the amount of DOM in the reaction vessel for the oxidation (dissolved organic carbon between 22 and 367 mmol L⁻¹), did not significantly affect the final lignin phenol composition. The new method was applied to seawater samples from the Kattegat and Davis Strait. The total concentration of dissolved lignin phenols measured in the two areas was between 4.3–10.1 and 2.1–3.2 nmol L⁻¹, respectively, which is within the range found by other studies. Comparison with a different oxidation approach and detection method (GC‐MS) gave similar results and underline the potential of LC and absorbance detection for analysis of dissolved lignin with our proposed method. 

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5. Rapid, high-sensitivity analysis of oxyhalides by non-suppressed ion chromatography-electrospray ionization-mass spectrometry: application to ClO ₄ ⁻ , ClO ₃ ⁻ , ClO ₂ ⁻ , and BrO ₃ ⁻ quantification during sunlight/chlorine advanced oxidation

   https://doi.org/10.1039/d0ew00429d  

   Young, Tessora R.; Cheng, Shi; Li, Wentao; Dodd, Michael C. (August 2020, Environmental Science: Water Research & Technology)

   null (Ed.)

   A rapid and sensitive method is described for measuring perchlorate (ClO 4 − ), chlorate (ClO 3 − ), chlorite (ClO 2 − ), bromate (BrO 3 − ), and iodate (IO 3 − ) ions in natural and treated waters using non-suppressed ion chromatography with electrospray ionization and tandem mass spectrometry (NS-IC-MS/MS). Major benefits of the NS-IC-MS/MS method include a short analysis time (12 minutes), low limits of quantification for BrO 3 − (0.10 μg L −1 ), ClO 4 − (0.06 μg L −1 ), ClO 3 − (0.80 μg L −1 ), and ClO 2 − (0.40 μg L −1 ), and compatibility with conventional LC-MS/MS instrumentation. Chromatographic separations were generally performed under isocratic conditions with a Thermo Scientific Dionex AS16 column, using a mobile phase of 20% 1 M aqueous methylamine and 80% acetonitrile. The isocratic method can also be optimized for IO 3 − analysis by including a gradient from the isocratic mobile phase to 100% 1 M aqueous methylamine. Four common anions (Cl − , Br − , SO 4 2− , and HCO 3 − /CO 3 2− ), a natural organic matter isolate (Suwannee River NOM), and several real water samples were tested to examine influences of natural water constituents on oxyhalide detection. Only ClO 2 − quantification was significantly affected – by elevated chloride concentrations (>2 mM) and NOM. The method was successfully applied to quantify oxyhalides in natural waters, chlorinated tap water, and waters subjected to advanced oxidation by sunlight-driven photolysis of free available chlorine (sunlight/FAC). Sunlight/FAC treatment of NOM-free waters containing 200 μg L −1 Br − resulted in formation of up to 263 ± 35 μg L −1 and 764 ± 54 μg L −1 ClO 3 − , and up to 20.1 ± 1.0 μg L −1 and 33.8 ± 1.0 μg L −1 BrO 3 − (at pH 6 and 8, respectively). NOM strongly inhibited ClO 3 − and BrO 3 − formation, likely by scavenging reactive oxygen or halogen species. As prior work shows that the greatest benefits in applying the sunlight/FAC process for purposes of improving disinfection of chlorine-resistant microorganisms are realized in waters with lower DOC levels and higher pH, it may therefore be desirable to limit potential applications to waters containing moderate DOC concentrations ( e.g. , ∼1–2 mg C L −1 ), low Br − concentrations ( e.g. , <50 μg L −1 ), and circumneutral to moderately alkaline pH ( e.g. , pH 7–8) to strike a balance between maximizing microbial inactivation while minimizing formation of oxyhalides and other disinfection byproducts. 

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