Framework for denoising Monte Carlo photon transport simulations using deep learning.

Significance: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resulting in high computational burdens.

Aim: We aim to develop an effective image denoising technique using deep learning (DL) to dramatically improve the low-photon MC simulation result quality, equivalently bringing further acceleration to the MC method.

Approach: We developed a cascade-network combining DnCNN with UNet, while extending a range of established image denoising neural-network architectures, including DnCNN, UNet, DRUNet, and deep residual-learning for denoising MC renderings (ResMCNet), in handling three-dimensional MC data and compared their performances against model-based denoising algorithms.

A Secure and Reusable Software Architecture for Supporting Online Data Harmonization.

Retrospective data harmonization across multiple research cohorts and studies is frequently done to increase statistical power, provide comparison analysis, and create a richer data source for data mining. However, when combining disparate data sources, harmonization projects face data management and analysis challenges. These include differences in the data dictionaries and variable definitions, privacy concerns surrounding health data representing sensitive populations, and lack of properly defined data models.

Machine learning-based biomarkers identification from toxicogenomics - Bridging to regulatory relevant phenotypic endpoints.

One of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo regulatory-relevant phenotypic toxicity endpoint. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (maximum relevance and minimum redundancy (MRMR)) and classification method (support vector machine (SVM)), an "optimal" number of biomarkers with minimum redundancy can be identified for prediction of phenotypic toxicity endpoints with good accuracy.

Effects of trust-based decision making in disrupted supply chains.

The United States has experienced prolonged severe shortages of vital medications over the past two decades. The causes underlying the severity and prolongation of these shortages are complex, in part due to the complexity of the underlying supply chain networks, which involve supplier-buyer interactions across multiple entities with competitive and cooperative goals. This leads to interesting challenges in maintaining consistent interactions and trust among the entities.

Effect of hydrogeological and anthropogenic factors on the spatial and temporal distribution of CVOCs in the karst system of northern Puerto Rico.

This study evaluates factors affecting the spatial and temporal distribution of chlorinated volatile organic contaminants (CVOCs) in the highly productive aquifers of the karst region in northern Puerto Rico (KR-NPR). Historical records from 1982 to 2016 are analyzed using spatial and statistical methods to evaluate hydrogeological and anthropogenic factors affecting the presence and concentrations of multiple CVOCs in the KR-NPR. Results show extensive spatial and temporal distributions of CVOCs, as single entities and as mixtures.

The influence of hydrogeological and anthropogenic variables on phthalate contamination in eogenetic karst groundwater systems.

This study investigates the occurrence of six phthalates and distribution of the three most-detected phthalates in the karst region of northern Puerto Rico (KRNPR) using data from historical records and current field measurements. Statistical data analyses, including ANOVA, Chi-Square, and logistic regression models are used to examine the major factors affecting the presence and concentrations of phthalates in the KRNPR. The most detected phthalates include DEHP, DBP, and DEP.

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms.

We present a highly scalable Monte Carlo (MC) three-dimensional photon transport simulation platform designed for heterogeneous computing systems. Through the development of a massively parallel MC algorithm using the Open Computing Language framework, this research extends our existing graphics processing unit (GPU)-accelerated MC technique to a highly scalable vendor-independent heterogeneous computing environment, achieving significantly improved performance and software portability. A number of parallel computing techniques are investigated to achieve portable performance over a wide range of computing hardware.

Patterns of temporal scaling of groundwater level fluctuation.

We studied the fractal scaling behavior of groundwater level fluctuation for various types of aquifers in Puerto Rico using the methods of (1) detrended fluctuation analysis (DFA) to examine the monofractality and (2) wavelet transform maximum modulus (WTMM) to analyze the multifractality. The DFA results show that fractals exist in groundwater fluctuations of all the aquifers with scaling patterns that are anti-persistent (1 < < 1.5; 1.

Big Data Analysis on Puerto Rico Testsite for Exploring Contamination Threats.

In this paper, we present the use of Principal Component Analysis and customized software, to accelerate the spectral analysis of biological samples. The work is part of the mission of the National Institute of Environmental Health Sciences sponsored Puerto Rico Testsite for Exploring Contamination Threats Center, establishing linkages between environmental pollutants and preterm birth. This paper provides an overview of the data repository developed for the Center, and presents a use case analysis of biological sample data maintained in the database system.

Spatiotemporal changes of CVOC concentrations in karst aquifers: analysis of three decades of data from Puerto Rico.

We studied the spatial and temporal distribution patterns of Chlorinated Volatile Organic Compounds (CVOCs) in the karst aquifers in northern Puerto Rico (1982-2013). Seventeen CVOCs were widely detected across the study area, with the most detected and persistent contaminated CVOCs including trichloroethylene (TCE), tetrachloroethylene (PCE), carbon tetrachloride (CT), chloroform (TCM), and methylene chloride (DCM). Historically, 471 (76%) and 319 (52%) of the 615 sampling sites have CVOC concentrations above the detection limit and maximum contamination level (MCL), respectively.

Accelerating mesh-based Monte Carlo method on modern CPU architectures.

In this report, we discuss the use of contemporary ray-tracing techniques to accelerate 3D mesh-based Monte Carlo photon transport simulations. Single Instruction Multiple Data (SIMD) based computation and branch-less design are exploited to accelerate ray-tetrahedron intersection tests and yield a 2-fold speed-up for ray-tracing calculations on a multi-core CPU. As part of this work, we have also studied SIMD-accelerated random number generators and math functions.

Towards the development of an error checker for radiotherapy treatment plans: a preliminary study.

Major accidents can happen during radiotherapy, with an extremely severe consequence to both patients and clinical professionals. We propose to use machine learning and data mining techniques to help detect large human errors in a radiotherapy treatment plan, as a complement to human inspection. One such technique is computer clustering.

A finite state model for respiratory motion analysis in image guided radiation therapy.

Effective image guided radiation treatment of a moving tumour requires adequate information on respiratory motion characteristics. For margin expansion, beam tracking and respiratory gating, the tumour motion must be quantified for pretreatment planning and monitored on-line. We propose a finite state model for respiratory motion analysis that captures our natural understanding of breathing stages.