A focused simulation-based optimization of print time and material usage with respect to orientation, layer height and support settings for multi-pathological anatomical models in inverted vat photopolymerization 3D printing.

BACKGROUND: 3D printing of anatomical models requires multi-factorial decision making for optimal model manufacturing. Due to the complex nature of the printing process, there are frequently multiple potentialities based on the desired end goal. The task of identifying the most optimal combination of print control variables is inherently subjective and rests on sound operator intuition. This study investigates the effect of orientation, layer and support settings on print time and material usage. This study also presents a quantitative optimization framework to jointly optimize print time and material usage as a function of those settings for multi-pathological anatomical models. METHODS: Seven anatomical models representing different anatomical regions (cardiovascular, abdominal, neurological and maxillofacial) were selected for this study. A reference cube was also included in the simulations. Using PreForm print preparation software the print time and material usage was simulated for each model across 4 orientations, 2 layer heights, 2 support densities and 2 support tip sizes. A 90-10 weighted optimization was performed to identify the 5 most optimal treatment combinations that resulted in the lowest print time (90% weight) and material usage (10% weight) for each model. RESULTS: The 0.1 mm layer height was uniformly the most optimal setting across all models. Layer height had the largest effect on print time. Orientation had a complex effect on both print time and material usage in certain models. The support density and the support tip size settings were found to have a relatively minor effect on both print time and material usage. Hollow models had a larger support volume fraction compared to solid models. CONCLUSIONS: The quantitative optimization framework identified the 5 most optimal treatment combinations for each model using a 90-10 weighting for print time and material usage. The presented optimization framework could be adapted based on the individual circumstance of each 3D printing lab and/or to potentially incorporate additional response variables of interest.

A systematic evaluation of medical 3D printing accuracy of multi-pathological anatomical models for surgical planning manufactured in elastic and rigid material using desktop inverted vat photopolymerization.

PURPOSE: The dimensional accuracy of three-dimensional (3D) printed anatomical models is essential to correctly understand spatial relationships and enable safe presurgical planning. Most recent accuracy studies focused on 3D printing of a single pathology for surgical planning. This study evaluated the accuracy of medical models across multiple pathologies, using desktop inverted vat photopolymerization (VP) to 3D print anatomic models using both rigid and elastic materials. METHODS: In the primary study, we 3D printed seven models (six anatomic models and one reference cube) with volumes ranging from ~2 to ~209 cc. The anatomic models spanned multiple pathologies (neurological, cardiovascular, abdominal, musculoskeletal). Two solid measurement landing blocks were strategically created around the pathology to allow high-resolution measurement using a digital micrometer and/or caliper. The physical measurements were compared to the designed dimensions, and further analysis was conducted regarding the observed patterns in accuracy. All of the models were printed in three resins: Elastic, Clear, and Grey Pro in the primary experiments. A full factorial block experimental design was employed and a total of 42 models were 3D printed in 21 print runs. In the secondary study, we 3D printed two of the anatomic models in triplicates selected from the previous six to evaluate the effect of 0.1 mm vs 0.05 mm layer height on the accuracy. RESULTS: In the primary experiment, all dimensional errors were less than 1 mm. The average dimensional error across the 42 models was 0.238  ±  0.219 mm and the relative error was 1.10  ±  1.13%. Results from the secondary experiments were similar with an average dimensional error of 0.252  ±  0.213 mm and relative error of 1.52%  ±  1.28% across 18 models. There was a statistically significant difference in the relative errors between the Elastic resin and Clear resin groups. We explained this difference by evaluating inverted VP 3D printing peel forces. There was a significant difference between the Solid and Hollow group of models. There was a significant difference between measurement landing blocks oriented Horizontally and Vertically. In the secondary experiments, there was no difference in accuracy between the 0.10 and 0.05 mm layer heights. CONCLUSIONS: The maximum measured error was less than 1 mm across all models, and the mean error was less than 0.26mm. Therefore, inverted VP 3D printing technology is suitable for medical 3D printing if 1 mm is considered the cutoff for clinical use cases. The 0.1 mm layer height is suitable for 3D printing accurate anatomical models for presurgical planning in a majority of cases. Elastic models, models oriented horizontally, and models that are hollow tend to have relatively higher deviation as seen from experimental results and mathematical model predictions. While clinically insignificant using a 1 mm cutoff, further research is needed to better understand the complex physical interactions in VP 3D printing which influence model accuracy.

Enhancing anaerobic oxidation of methane in municipal solid waste landfill cover soil.

Landfills are the third largest anthropogenic source of the greenhouse gas methane worldwide. In the upper portions of landfill covers, methane is oxidized aerobically by microorganisms to form the less-potent greenhouse gas carbon dioxide; however, because of the low permeability of oxygen, no aerobic oxidation occurs in deeper portions of the cover. Therefore, the goal of this study was to enhance anaerobic oxidation of methane (AOM) in the deeper parts of landfill covers, to increase overall methane removal, via addition of electron acceptors besides oxygen. In batch tests, landfill cover soil was amended using five alternate electron acceptors: iron(III), nitrate, nitrite, sulfate, and manganese. AOM was then measured via column tests, which included realistic conditions of gas flow, cover thickness, and compaction. In the batch tests, soils amended with nitrate, sulfate, and the combination of sulfate + hematite removed more methane compared to control soil. Methane generation inhibitor had no impact on net methane removal. Adding nutrients to the soil significantly enhanced methane removal only for the case of soil without electron acceptors. Greater methane removal was observed for reactors with higher initial methane concentration. Results of the column tests showed that soil amended with sulfate + iron had the highest (around 10%) removal of methane in the anoxic zone, followed by soil amended with sulfate. Hydrogen sulfide (H2S) gas was measured in the headspace of these two columns, which indicated that sulfate-reducing bacteria were likely responsible for methane removal.

A decision-making framework for adaptive pain management.

Pain management is a critical international health issue. The Eugene McDermott Center for Pain Management at The University of Texas Southwestern Medical Center conducted a two-stage interdisciplinary pain management program that considers a wide variety of treatments. Prior to treatment (beginning of Stage 1), an evaluation records the patient's pain characteristics, medical history and related health parameters. A treatment regime is then determined. At the midpoint of the program (beginning of Stage 2), an evaluation is conducted to determine if an adjustment in the treatment should be made. A final evaluation is conducted at the end of the program to assess final outcomes. We structure this decision-making process using dynamic programming (DP) to generate adaptive treatment strategies for this two-stage program. An approximate DP solution method is employed in which state transition models are constructed empirically based on data from the pain management program, and the future value function is approximated using state space discretization based on a Latin hypercube design and artificial neural networks. The optimization seeks for treatment plans that minimize treatment dosage and pain levels simultaneously.

A data-integrated simulation-based optimization for assigning nurses to patient admissions.

The health care system in the United States has a shortage of nurses. A careful planning of nurse resources is needed to ease the health care system from the burden of the nurse shortage and standardize nurse workload. An earlier research study developed a data-integrated simulation to evaluate nurse-patient assignments (SIMNA) at the beginning of a shift based on a real data set provided by a northeast Texas hospital. In this research, with the aid of the same SIMNA model, two policies are developed to make nurse-to-patient assignments when new patients are admitted during a shift. A heuristic (HEU) policy assigns a newly-admitted patient to the nurse who has performed the least assigned direct care among all the nurses. A partially-optimized (OPT) policy seeks to minimize the difference in workload among nurses for the entire shift by estimating the assigned direct care from SIMNA. Results comparing HEU and OPT policies are presented.

A data-integrated simulation model to evaluate nurse-patient assignments.

This research develops a novel data-integrated simulation to evaluate nurse-patient assignments (SIMNA) based on a real data set provided by a northeast Texas hospital. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree models, data mining tools for prediction and classification, were used to develop five tree structures: (a) four classification trees from which transition probabilities for nurse movements are determined, and (b) a regression tree from which the amount of time a nurse spends in a location is predicted based on factors such as the primary diagnosis of a patient and the type of nurse. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Results obtained from SIMNA to evaluate nurse-patient assignments in Medical/Surgical unit I of the northeast Texas hospital are discussed.

Characterization of spatially homogeneous regions based on temporal patterns of fine particulate matter in the continental United States.

Statistical analyses of time-series or spatial data have been widely used to investigate the behavior of ambient air pollutants. Because air pollution data are generally collected in a wide area of interest over a relatively long period, such analyses should take into account both spatial and temporal characteristics. The objective of this study is 2-fold: (1) to identify an efficient way to characterize the spatial variations of fine particulate matter (PM2.5) concentrations based solely upon their temporal patterns, and (2) to analyze the temporal and seasonal patterns of PM2.5 concentrations in spatially homogenous regions. This study used 24-hr average PM2.5 concentrations measured every third day during a period between 2001 and 2005 at 522 monitoring sites in the continental United States. A k-means clustering algorithm using the correlation distance was used to investigate the similarity in patterns between temporal profiles observed at the monitoring sites. A k-means clustering analysis produced six clusters of sites with distinct temporal patterns that were able to identify and characterize spatially homogeneous regions of the United States. The study also presents a rotated principal component analysis (RPCA) that has been used for characterizing spatial patterns of air pollution and discusses the difference between the clustering algorithm and RPCA.

Controlling the False Discovery Rate for Feature Selection in High-resolution NMR Spectra.

Successful implementation of feature selection in nuclear magnetic resonance (NMR) spectra not only improves classification ability, but also simplifies the entire modeling process and, thus, reduces computational and analytical efforts. Principal component analysis (PCA) and partial least squares (PLS) have been widely used for feature selection in NMR spectra. However, extracting meaningful metabolite features from the reduced dimensions obtained through PCA or PLS is complicated because these reduced dimensions are linear combinations of a large number of the original features. In this paper, we propose a multiple testing procedure controlling false discovery rate (FDR) as an efficient method for feature selection in NMR spectra. The procedure clearly compensates for the limitation of PCA and PLS and identifies individual metabolite features necessary for classification. In addition, we present orthogonal signal correction to improve classification and visualization by removing unnecessary variations in NMR spectra. Our experimental results with real NMR spectra showed that classification models constructed with the features selected by our proposed procedure yielded smaller misclassification rates than those with all features.