Engaging Stakeholders to Develop a Roadmap for Dry Eye and MGD PCORI-Funded Research.

Introduction: Although affecting an estimated 35% of the population, Dry Eye is not well understood by patients and the medical community. As a result, both in research and clinical settings, diagnostic and treatment protocols tend to be non-specific, ad hoc, and inadequate, with a narrow industry-driven focus. The purpose of this convening was to propose a research roadmap that orients Dry Eye researchers toward a comprehensive patient-centered approach to diagnosing and treating Dry Eye, Meibomian gland dysfunction (MGD), and related comorbidities with a goal of improving clinical outcomes for Dry Eye/MGD patients. Methods: Sixteen participants, including Dry Eye/MGD patients, caregivers, and patient advocates together with a group of experts in Dry Eye, MGD and other fields identified gaps in research on Dry Eye and MGD diagnostic and treatment approaches (age range 20-80; male to female ratio of 7:11; patients: 7). During a 2-day virtual convening, participants were assigned to topic-specific focus-group sessions to discuss and develop research questions pertaining to Dry Eye and MGD. The research questions were compiled into a proposed patient-centered roadmap for Dry Eye and MGD research. Two additional participants contributed to the proposed roadmap following the convening. Results: The focus groups identified over 80 patient-centered research questions important to patients and other stakeholders and compiled these into a proposed research roadmap. Conclusion: The convened stakeholders aim to establish a cohesive and comprehensive patient-centered approach to treating Dry Eye, Meibomian Gland Dysfunction, and comorbidities. The research roadmap will serve as a reference for researchers, educational institutions, clinicians, and others evaluating diagnostic and treatment protocols in Dry Eye and MGD.

Microelectrode evaluation of in situ oxidant reactivity and pH variability at new ductile iron and copper coupon surfaces.

Thirty-two short term (∼7.5 h) abiotic experiments were conducted with new ductile iron and copper coupons exposed to various water qualities, including pH (7 or 9), dissolved inorganic carbon (DIC, 10 or 50 mg C L-1) and phosphate (0 or 3 mg P L-1) concentrations and 4 mg Cl2 L-1 free chlorine or monochloramine. To quantify oxidant reactivity with the new metal coupons, microelectrodes were used to obtain oxidant (free chlorine or monochloramine and dissolved oxygen (DO)) concentration and pH microprofiles from the bulk water to near the metal coupon surface. From the microprofiles, apparent surface reaction rate constants (k) were determined for each oxidant. An ANOVA analysis evaluated if the five variables (Material, Oxidant, Phosphate, DIC, and pH) significantly affected estimates of k, finding that the Material and Oxidant variables and their interaction were statistically significant (p<0.05), but the effect of variables of Phosphate, DIC, and pH on k values were not significant in this study. In general, both ductile iron and copper coupons showed significant surface reactivity towards free chlorine and monochloramine. For ductile iron, DO consumption was greater than for copper, which showed minimal DO reactivity, and DO was less reactive towards the copper surface than either free chlorine or monochloramine. Furthermore, pH microprofiles provided insight into the complexity that might exist near corroding metal surfaces where the bulk water pH may be substantially different from that measured near metal surfaces which is significant as pH is a controlling variable in terms of scale formation and metal solubility. This study represents an important first step towards using microelectrodes to (1) understand and provide direct measurement of oxidant microprofiles from the bulk water to the metal surface; (2) determine pipe wall reactivity using the directly measured concentrations profiles versus estimated pipe wall reactivity from bulk water measurements, and (3) understand how variables measured by bulk water samples (e.g., pH) may be drastically different from what is occurring at and near the metal surface. Together, these insights will assist in understanding disinfectant residual maintenance, corrosion, and metal release.

Predicting bilgewater emulsion stability by oil separation using image processing and machine learning.

Bilgewater is a shipboard multi-component oily wastewater, combining numerous wastewater sources. A better understanding of bilgewater emulsions is required for proper wastewater management to meet discharge regulations. In this study, we developed 360 emulsion samples based on commonly used Navy cleaner data and previous bilgewater composition studies. Oil value (OV) was obtained from image analysis of oil/creaming layer and validated by oil separation (OS) which was experimentally determined using a gravimetric method. OV (%) showed good agreement with OS (%), indicating that a simple image-based parameter can be used for emulsion stability prediction model development. An ANOVA analysis was conducted of the five variables (Cleaner, Salinity, Suspended Solids [SS], pH, and Temperature) that significantly impacted estimates of OV, finding that the Cleaner, Salinity, and SS variables were statistically significant (p < 0.05), while pH and Temperature were not. In general, most cleaners showed improved oil separation with salt additions. Novel machine learning (ML)-based predictive models of both classification and regression for bilgewater emulsion stability were then developed using OV. For classification, the random forest (RF) classifiers achieved the most accurate prediction with F1-score of 0.8224, while in regression-based models the decision tree (DT) regressor showed the highest prediction of emulsion stability with the average mean absolute error (MAE) of 0.1611. Turbidity also showed a good emulsion prediction with RF regressor (MAE of 0.0559) and RF classifier (F1-score of 0.9338). One predictor variable removal test showed that Salinity, SS, and Temperature are the most impactful variables in the developed models. This is the first study to use image processing and machine learning for the prediction of oil separation for the application of bilgewater assessment within the marine sector.

Joint estimation and regularized aggregation of brain network in FMRI data.

BACKGROUND: In the Gaussian graphical model framework, precision matrices reveal conditional dependence structure among random variables. In functional magnetic resonance imaging (fMRI) data, estimating such precision matrices of multi-subjects and aggregating them to a group-level is an essential step for constructing a group brain network. NEW METHOD: In this article, we considered joint estimation of multiple precision matrices with regularized aggregation. Also, in the construction of a group precision matrix, we integrated robust aggregation to the estimation. In the estimation of individual precision matrices, we took a regularization approach to induce sparsity, which made brain network estimation more realistic. RESULTS: We demonstrated the effectiveness of the proposed method through simulated examples, and analyses on real fMRI data acquired during eye movement tasks assessing cognitive control. For the fMRI data, the joint estimation of multiple precision matrices (JEMP) with regularized aggregation (RA) captured more robust associations between task-relevant neural regions of interest (ROIs), compared to the analyses using JEMP alone. The JEMP with RA also was sensitive to increased neural efficiency after task practice. COMPARISON WITH EXISTING METHOD(S): The simple average of individual precision matrices may be affected by outliers and provide inconsistent outcomes between subject-level and group-level networks. In contrast, the proposed method yielded a robust group graph that could identify and ease the effect of outliers. CONCLUSIONS: The proposed method identified regions of practice-induced attenuation associated with reduced cognitive demand after repeat task exposure. Through simulated and real data, we demonstrated that this method does not require any distribution assumption, can identify outliers, and provides robust, representative group brain networks. This method can be applied to datasets that have extensive variability and/or multiple outliers, including applications to specific, and general, cognitive processes, as well as for studies that may require longitudinal data, such as pharmaceutical trials.

Regularized aggregation of statistical parametric maps.

Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them susceptible to subjects with outlying values. Furthermore, t tests are prone to false positives and false negatives when outlying values are observed. We propose a regularized unsupervised aggregation method for SPMs to find an optimal weight for aggregation, which aids in detecting and mitigating the effect of outlying subjects. We also present a bootstrap-based weighted t test using the optimal weights to construct an activation map robust to outlying subjects. We validate the performance of the proposed aggregation method and test using simulated and real data examples. Results show that the regularized aggregation approach can effectively detect outlying subjects, lower their weights, and produce robust SPMs.