Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chemistry Central Journal 10 (2016): 75, doi:10.1186/s13065-016-0211-y.Comprehensive two-dimensional gas chromatography (GC×GC) provides high-resolution separations across hundreds of compounds in a complex mixture, thus unlocking unprecedented information for intricate quantitative interpretation. We exploit this compound diversity across the (GC×GC) topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the (GC×GC) topography of biomarker hydrocarbons, hopanes and steranes, as they are generally recalcitrant to weathering. We introduce peak topography maps (PTM) and topography partitioning techniques that consider a notably broader and more diverse range of target and non-target biomarker compounds compared to traditional approaches that consider approximately 20 biomarker ratios. Specifically, we consider a range of 33–154 target and non-target biomarkers with highest-to-lowest peak ratio within an injection ranging from 4.86 to 19.6 (precise numbers depend on biomarker diversity of individual injections). We also provide a robust quantitative measure for directly determining “match” between samples, without necessitating training data sets. We validate our methods across 34 (GC×GC) injections from a diverse portfolio of petroleum sources, and provide quantitative comparison of performance against established statistical methods such as principal components analysis (PCA). Our data set includes a wide range of samples collected following the 2010 Deepwater Horizon disaster that released approximately 160 million gallons of crude oil from the Macondo well (MW). Samples that were clearly collected following this disaster exhibit statistically significant match (99.23±1.66)% using PTM-based interpretation against other closely related sources. PTM-based interpretation also provides higher differentiation between closely correlated but distinct sources than obtained using PCA-based statistical comparisons. In addition to results based on this experimental field data, we also provide extentive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the (GC×GC) biomarker ROI image of the MW pre-spill sample (sample #1 in Additional file 4: Table S1). We compare the robustness of the cross-PTM score against peak location variability in both dimensions and compare the results against PCA analysis over the same set of simulated images. Detailed description of the simulation experiment and discussion of results are provided in Additional file 1: Section S8. We provide a peak-cognizant informational framework for quantitative interpretation of (GC×GC) topography. Proposed topographic analysis enables (GC×GC) forensic interpretation across target petroleum biomarkers, while including the nuances of lesser-known non-target biomarkers clustered around the target peaks. This allows potential discovery of hitherto unknown connections between target and non-target biomarkers.This research was made possible in part by a grant from the Gulf of Mexico Research Initiative (GoMRI-015), and the DEEP-C consortium, and in part by NSF Grants OCE-0969841 and RAPID OCE-1043976 as well as a WHOI interdisciplinary study award

Fecal Fingerprints of Enteric Pathogen Contamination in Public Environments of Kisumu, Kenya, Associated with Human Sanitation Conditions and Domestic Animals.

Young children are infected by a diverse range of enteric pathogens in high disease burden settings, suggesting pathogen contamination of the environment is equally diverse. This study aimed to characterize across- and within-neighborhood diversity in enteric pathogen contamination of public domains in urban informal settlements of Kisumu, Kenya, and to assess the relationship between pathogen detection patterns and human and domestic animal sanitation conditions. Microbial contamination of soil and surface water from 166 public sites in three Kisumu neighborhoods was measured by enterococcal assays and quantitative reverse transcription polymerase chain reaction (qRT-PCR) for 19 enteric pathogens. Regression was used to assess the association between observed sanitary indicators of contamination with enterococci and pathogen presence and concentration, and pathogen diversity. Seventeen types of pathogens were detected in Kisumu public domains. Enteric pathogens were codetected in 33% of soil and 65% of surface water samples. Greater pathogen diversity was associated with the presence of domestic animal feces but not with human open defecation, deteriorating latrines, flies, or disposal of human feces. Sanitary conditions were not associated with enterococcal bacteria, specific pathogen concentrations, or "any pathogen". Young children played at 40% of observed sites. Managing domestic animal feces may be required to reduce enteric pathogen environmental contamination in high-burden settings

Emerging Priorities for Microbiome Research

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19–23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab’s findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration

A Structural Approach to Multi-User Detection

136 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.Multi-user detection focuses on the problem of demodulating the information symbols of multiple users sharing a common communication medium, e.g., a wireless communication system. An extensive literature currently exists on the various aspects of multi-user detection, including linear detection, successive and parallel interference cancellation, joint detection, and group detection, among others. The focus of this research is to address the problem of multi-user detection from the perspective of the physical structure of the signal constellation. Many popular methods approach the problem of multi-user detection in terms of algebraic relationships between the user signals. We have instead taken a more geometric approach to the problem of interference suppression and joint detection in the sense that the structural geometry of the signal constellation is used to derive more efficient detection algorithms. We have also considered the case where the combined multi-user signal is passed through a known memoryless non-linearity, e.g., the RF amplifier in a satellite communication system. The non-linearity breaks down any assumptions that currently existing detectors make about the multi-user channel model, e.g., the mutual interference of the user signals is additive, or that the user signals are linearly independent. Our approach has been to generalize the algebraic methods to include the possibility of non-linear and, hence, non-additive signal interference. We have come up with new detection algorithms that work effectively on non-linear as well as linear multi-user systems. We have shown that under sufficient conditions, some of our proposed detectors reduce to currently existing popular multi-user detectors, like the decorrelator. We have also posed the non-linear multi-user system model as a linear system with a higher number of users under certain assumptions on the non-linearity. Finally we have derived a joint detection algorithm that collectively demodulates all the user symbols by using the energy distribution of the signal constellation. The key motivation behind this research is to derive useful information based on the structural geometry of the signal constellation and design detectors that use this information to achieve higher performance gains in terms of the slope of the probability of error in the high SNR regime.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD