Analysis of leading edge and trailing edge cover glass samples before and after treatment with advanced satellite contamination removal techniques

Two samples from Long Duration Exposure Facility (LDEF) experiment M0003-4 were analyzed for molecular and particulate contamination prior to and following treatment with advanced satellite contamination removal techniques (CO2 gas/solid jet spray and oxygen ion beam). The pre- and post-cleaning measurements and analyses are presented. The jet spray removed particulates in seconds. The low energy reactive oxygen ion beam removed 5,000 A of photo polymerized organic hydrocarbon contamination in less than 1 hour. Spectroscopic analytical techniques were applied to the analysis of cleaning efficiency including: Fourier transform infrared, Auger, x ray photoemissions, energy dispersive x ray, and ultraviolet/visible. The results of this work suggest that the contamination studied here was due to spacecraft self-contamination enhanced by atomic oxygen plasma dynamics and solar UV radiation. These results also suggest the efficacy for the jet spray and ion beam contamination control technologies for spacecraft optical surfaces

Comprehensive Seagrass Restoration Planning in Southwest Florida: Science, Law and Management

In coastal Florida, the development and maintenance of docks, marinas, and channels frequently cause destruction of seagrass beds. Seagrass loss is accompanied by a loss of the ecosystem services the beds provide, such as sediment stabilization, water filtration, protection from storms, and habitat and nursery grounds for fish species. The current legal framework for seagrass protection and the implementation of mitigation for seagrass loss could be improved. In this Article, the authors argue that policymakers could revise the Uniform Mitigation Assessment Method to include more assessments related specifically to the ecology of seagrass beds and their ecosystem services. Seagrass mitigation is currently carried out by the permittee that applied to create or maintain the seagrass-impacting development. In comparison, wetland mitigation is typically carried out by publicly or privately operated mitigation banks. The creation of mitigation banks for seagrass restoration would streamline the process of seagrass mitigation and promote the public\u27s interest in seagrass restoration

Comprehensive Seagrass Restoration Planning in Southwest Florida: Science, Law and Management

In coastal Florida, the development and maintenance of docks, marinas, and channels frequently cause destruction of seagrass beds. Seagrass loss is accompanied by a loss of the ecosystem services the beds provide, such as sediment stabilization, water filtration, protection from storms, and habitat and nursery grounds for fish species. The current legal framework for seagrass protection and the implementation of mitigation for seagrass loss could be improved. In this Article, the authors argue that policymakers could revise the Uniform Mitigation Assessment Method to include more assessments related specifically to the ecology of seagrass beds and their ecosystem services. Seagrass mitigation is currently carried out by the permittee that applied to create or maintain the seagrass-impacting development. In comparison, wetland mitigation is typically carried out by publicly or privately operated mitigation banks. The creation of mitigation banks for seagrass restoration would streamline the process of seagrass mitigation and promote the public\u27s interest in seagrass restoration

A call for standardized outcomes in microTESE

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136713/1/andr12356.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136713/2/andr12356_am.pd

The principal as evaluator : an application of the curriculum evaluation model of Elliot W. Eisner to a kindergarten setting

The purpose of this study was to apply the curriculum evaluation model of Elliot W. Eisner to a kindergarten setting. The writer, as principal and evaluator of the setting, based her investigation on Eisner's belief that evaluation needs to be grounded in a view of how persons create meaning from their experiences. Dale L. Brubaker's definition of curriculum, what each person experiences as learning settings are cooperatively created, was utilized in the study. The study included description, interpretation and assessment of the pervasive qualities of the curriculum as currently experienced by the setting's participants. The themes of control, understanding and liberation, identified by James B. Macdonald as basic value positions, recurred in the participants' expressions of the meaning of their shared experiences. The use of participant observation, interviews, review of documentary sources and ethnography, methodology consistent with field research, enabled the writer to define the parts that communicated a holistic meaning

Comparison of Artificial Intelligence based approaches to cell function prediction

Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels

Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and non-invasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to non-invasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate non-invasive cell therapy characterization can be achieved with QBAM and machine learning

Glycaemic control and risk of incident urinary incontinence in women with Type 1 diabetes: results from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study

AimsTo study the impact of glycaemic control on urinary incontinence in women who participated in the Diabetes Control and Complications Trial (DCCT; 1983–1993) and its observational follow‐up study, the Epidemiology of Diabetes Interventions and Complications (EDIC; 1994–present).MethodsStudy participants were women who completed, at both years 10 (2003) and 17 (2010) of the EDIC follow‐up, the urological assessment questionnaire (UroEDIC). Urinary incontinence was defined as self‐reported involuntary leakage of urine that occurred at least weekly. Incident urinary incontinence was defined as weekly urinary incontinence present at EDIC year 17 but not at EDIC year 10. Multivariable regression models were used to examine the association of incident urinary incontinence with comorbid prevalent conditions and glycaemic control (mean HbA1c over the first 10 years of EDIC).ResultsA total of 64 (15.3%) women with Type 1 diabetes (mean age 43.6 ± 6.3 years at EDIC year 10) reported incident urinary incontinence at EDIC year 17. When adjusted for clinical covariates (including age, DCCT cohort assignment, DCCT treatment arm, BMI, insulin dosage, parity, hysterectomy, autonomic neuropathy and urinary tract infection in the last year), the mean EDIC HbA1c was associated with increased odds of incident urinary incontinence (odds ratio 1.03, 95% CI 1.01–1.06 per mmol/mol increase; odds ratio 1.41, 95% CI 1.07–1.89 per % HbA1c increase).ConclusionsIncident urinary incontinence was associated with higher HbA1c levels in women with Type 1 diabetes, independent of other recognized risk factors. These results suggest the potential for women to modify their risk of urinary incontinence with improved glycaemic control. (Clinical Trials Registry no: NCT00360815 and NCT00360893).What’s new?Research to date has failed to show an association between glycaemic control and urinary incontinence (UI) in women with diabetes.We examined the relationship between HbA1c and UI using longitudinal data from the Diabetes Control and Complications Trial (DCCT) and its observational follow‐up, the Epidemiology of Diabetes Interventions and Complications (EDIC) study.Our findings show that the odds of UI increase with poor glycaemic control in women with Type 1 diabetes, independently of other well‐described predictors of UI.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134490/1/dme13126.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134490/2/dme13126_am.pd

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C)

Background: Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness. Methods: Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest. Results: We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies. Conclusions: The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data

Pre-existing autoimmunity is associated with increased severity of COVID-19: A retrospective cohort study using data from the National COVID Cohort Collaborative (N3C)

Identifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes.A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease, and hospitalization were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities.Of the 2,453,799 adults diagnosed with COVID-19, 191,520 (7.81%) had a pre-existing AID diagnosis and 278,095 (11.33%) had a pre-existing IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a pre-existing AID (OR = 1.13, 95% CI 1.09 - 1.17; P< 0.001), IS (OR= 1.27, 95% CI 1.24 - 1.30; P< 0.001), or both (OR = 1.35, 95% CI 1.29 - 1.40; P< 0.001) were more likely to have a life-threatening COVID-19 disease. These results were consistent when evaluating hospitalization. A sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66- 0.96; P=0.017) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; P< 0.001).Patients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19