Development of Methods for Determining Dry Deposition of Mercury Using an Ion-exchange membrane: Relative Rates of Mercury Dry Deposition at Sardis, Enid, and Grenada Lakes

This research focuses on studies developing methods to measure dry deposition of mercury (Hg) using an ion-exchange (IX) membrane to capture gaseous mercury species in the air. Mercury is a toxic heavy metal that is spread globally through the atmosphere. Atmospheric Hg deposits to terrestrial and aquatic ecosystems through wet and dry deposition. While routine methods have been developed to study wet deposition of Hg, measuring dry deposition of Hg is more problematic and often overlooked. In this study, we developed an inexpensive means to deploy a polyethersulfone cation exchange membrane in the field by dangling it within a polycarbonate bottle containing holes in the bottom to allow gas exchange. We tested several different analytical methods to measure the Hg on the membranes including atomic absorption spectrometry, atomic fluorescence spectrometry, and mass spectrometry. After demonstrating that the field method is capable of capturing and retaining airborne Hg on the membrane, we deployed the bottles containing the membranes at Sardis, Enid, and Grenada Lakes, located in north Mississippi. The purpose was to estimate the relative rates of dry deposition of Hg in order to explore differences in the levels of Hg found in fish from these lakes. We hypothesized that point sources near Grenada Lake, including a coal-fired power plant, may result in higher Hg deposition rates, which may be the reason for the higher Hg levels observed in fish from Grenada Lake compared to the other lakes. However, results show that Sardis Lake had the highest dry deposition rates followed by Enid and Grenada Lakes. Thus, the higher levels of Hg in fish from Grenada Lake remain unexplained

Robust Graph Neural Networks via Unbiased Aggregation

The adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks despite the existence of numerous defenses. In this work, we delve into the robustness analysis of representative robust GNNs and provide a unified robust estimation point of view to understand their robustness and limitations. Our novel analysis of estimation bias motivates the design of a robust and unbiased graph signal estimator. We then develop an efficient Quasi-Newton iterative reweighted least squares algorithm to solve the estimation problem, which unfolds as robust unbiased aggregation layers in GNNs with a theoretical convergence guarantee. Our comprehensive experiments confirm the strong robustness of our proposed model, and the ablation study provides a deep understanding of its advantages

Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation

The use of accurate scanning transmission electron microscopy (STEM) image simulation methods require large computation times that can make their use infeasible for the simulation of many images. Other simulation methods based on linear imaging models, such as the convolution method, are much faster but are too inaccurate to be used in application. In this paper, we explore deep learning models that attempt to translate a STEM image produced by the convolution method to a prediction of the high accuracy multislice image. We then compare our results to those of regression methods. We find that using the deep learning model Generative Adversarial Network (GAN) provides us with the best results and performs at a similar accuracy level to previous regression models on the same dataset. Codes and data for this project can be found in this GitHub repository, https://github.com/uw-cmg/GAN-STEM-Conv2MultiSlice

Screening Key Indicators for Acute Kidney Injury Prediction Using Machine Learning

Acute kidney injury is a common critical disease with a high mortality. The large number of indicators in AKI patients makes it difficult for clinicians to quickly and accurately determine the patient’s condition. This study used machine learning methods to filter key indicators and use key indicator data to achieve advance prediction of AKI so that a small number of indicators could be measured to reliably predict AKI and provide auxiliary decision support for clinical staff. Sequential forward selection based on feature importance calculated by XGBoost was used to screen out 17 key indicators. Three machine learning algorithms were used to make predictions, namely, logistic regression (LR), decision tree, and XGBoost. To verify the validity of the method, data were extracted from the MIMIC III database and the eICU-CRD database for 1,009 and 1,327 AKI patients, respectively. The MIMIC III database was used for internal validation, and the eICU-CRD database was used for external validation. For all three machine learning algorithms, the prediction performance from using only the key indicator dataset was very close to that from using the full dataset. The XGBoost algorithm performed the best, and LR was the next best. The decision tree performed the worst. The key indicator screening method proposed in this study can achieve a good predictive performance while streamlining the number of indicators

Dietary inflammatory potential mediated gut microbiota and metabolite alterations in Crohn's disease:A fire-new perspective

Background & aims: Pro-inflammatory diet interacting with gut microbiome might trigger for Crohn's disease (CD). We aimed to investigate the relationship between dietary inflammatory potential and microflora/metabolites change and their link with CD. Methods: The dietary inflammatory potential was assessed using a dietary inflammatory index (DII) based on the Food Frequency Questionnaire from 150 new-onset CD patients and 285 healthy controls (HCs). We selected 41 CD patients and 89 HCs who had not received medication for metagenomic and targeted metabolomic sequencing to profile their gut microbial composition as well as fecal and serum metabolites. DII scores were classified into quartiles to investigate associations among different variables. Results: DII scores of CD patients were significantly higher than HCs (0.56 ± 1.20 vs 0.23 ± 1.02, p = 0.017). With adjustment for confounders, a higher DII score was significantly associated with higher risk of CD (OR: 1.420; 95% CI: 1.049, 1.923, p = 0.023). DII score also was positively correlated with disease activity (p = 0.001). Morganella morganii and Veillonella parvula were increased while Coprococcus eutactus was decreased in the pro-inflammatory diets group, as well as in CD. DII-related bacteria were associated with disease activity and inflammatory markers in CD patients. Among the metabolic change, pro-inflammatory diet induced metabolites change were largely involved in amino acid metabolic pathways that were also observed in CD. Conclusions: Pro-inflammatory diet might be associated with increased risk and disease activity of CD. Diet with high DII potentially involves in CD by mediating alterations in gut microbiota and metabolites