Mass grave localization prediction with geographical information systems in Guatemala and future impacts.

Conducting physical searches for mass grave locations based on anecdotal evidence is a time-consuming and resource-intensive endeavor in circumstances that often pose a threat to personal safety. The development of tools and procedures to speed such searches can greatly reduce the risk involved, increase the number of individuals whose remains are recovered and identified, and more importantly, reunite these remains with their loved ones to provide them with a proper burial. Geographic information systems (GIS) software, which can analyze and manipulate the spatial characteristics of known mass grave data, represents a powerful tool that can be used to predict new mass grave locations and increase the speed and efficiency with which they are investigated. Using the open source QGIS project, existing mass grave locations in Guatemala were analyzed based on their distance from and change in elevation relative to roads, streets, waterways, points of interest, and possible villages/towns. Statistical and geostatistical analyses performed to detect relationships among the variables resulted in patterns that warrant further study and can be used to further narrow areas of investigation.

Prediction of minimum postmortem submersion interval (PMSImin) based on eukaryotic community succession on skeletal remains recovered from a lentic environment.

Although recent studies explored using microbial succession during decomposition to estimate the postmortem interval (PMI) and postmortem submersion interval (PMSI), there is currently no published research using aquatic eukaryotic community succession to estimate the minimum postmortem submersion interval (PMSImin). The goals of this study were to determine whether eukaryotic community succession occurs on porcine skeletal remains in a lentic environment, and, if so, to develop a statistical model for PMSImin prediction. Fresh porcine bones (rib N = 100, scapula N = 100) were placed in cages (10'' x 10'') attached to floatation devices and submerged in a fresh water lake (Crozet, VA), using waterproof loggers and a YSI Sonde to record temperature and water quality variables, respectively. In addition to baseline samples, one cage, containing five ribs and five scapulae, and water samples (500 mL) were collected approximately every 250 accumulated degree days (ADD). Nineteen sample cohorts were collected over a period of 5200 ADD (579 Days). Variable region nine (V9) of the 18S ribosomal DNA (rDNA) was amplified and sequenced using a dual-index strategy on the MiSeq FGx sequencing platform. Resulting sequences underwent quality control parameters and analysis in mothur v 1.42.3, R v 3.5.3, and R v 3.6.0. Permutational multivariate analysis of variance (PERMANOVA) revealed a significant difference in phylogenetic ß-diversity among ribs, scapulae and water (p = 0.001) and among ADD (p ≤ 0.011), which was supported by distinct clustering of samples associated with each ADD in UniFrac distance based non-metric multidimensional scaling (NMDS) ordinations. Using similarity percentage (SIMPER) analysis of class and family level taxa, differences observed between bone types were attributed to Peronosporomycetes_cl, Eukaryota_unclassified, and Intramacronucleata (e.g., Armophorida), however these differences were not statistically significant. Alpha diversity revealed a non-linear increase in phylogenetic diversity with an increase in ADD. Random forest models for ribs and scapulae predicted PMSImin with an error rate within±104 days (937 ADD) and±63 days (564 ADD), respectively. In conclusion, this study suggests that eukaryotic succession is capable of predicting long term PMSImin in lentic systems.

Postmortem submersion interval (PMSI) estimation from the microbiome of sus scrofa bone in a freshwater lake.

While many studies have developed microbial succession-based models for the prediction of postmortem interval (PMI) in terrestrial systems, similar well-replicated long-term decomposition studies are lacking for aquatic systems. Therefore, this study sought to identify temporal changes in bacterial community structure associated with porcine skeletal remains (n = 198) for an extended period in a fresh water lake. Every ca. 250 ADD, one cage, containing 5 ribs and 5 scapulae, was removed from the lake for a total of nineteen collections. Water was also sampled at each interval. Variable region 4 (V4) of 16S rDNA was amplified and sequenced for all collected samples using Illumina MiSeq FGx Sequencing platform; resulting data were analyzed with the mothur (v1.39.5) and R (v3.6.0). Bacterial communities associated with ribs differed significantly from those associated with scapulae. This difference was mainly attributed to Clostridia, Holophagae, and Spirochaete relative abundances. For each bone type, α-diversity increased with ADD; similarly, ß-diversity bacterial community structure changed significantly with ADD and were explained using environmental parameters and inferred functional pathways. Models developed using 24 rib and 34 scapula family-level taxa allowed the prediction of PMSI with root mean square error of 522.97 ADD (~57 days) and 333.8 ADD (~37 days), respectively.

Postmortem submersion interval (PMSI) estimation from the microbiome of Sus scrofa bone in a freshwater river.

Due to inherent differences between terrestrial and aquatic systems, methods for estimating the postmortem interval (PMI) are not directly applicable to remains recovered from water. Recent studies have explored the use of microbial succession for estimating the postmortem submersion interval (PMSI); however, a non-disturbed, highly replicated and long-term aquatic decomposition study in a freshwater river has not been performed. In this study, porcine skeletal remains (N = 200) were submerged in a freshwater river from November 2017-2018 (6322 accumulated degree days (ADD)/353 days) to identify changes and successional patterns in bacterial communities. One cage (e.g., 5 ribs and 5 scapulae) was collected approximately every 250 ADD for twenty-four collections; baseline samples never exposed to water acted as controls. Variable region 4 (V4) of 16S rDNA, was amplified and sequenced via the Illumina MiSeq FGx sequencing platform. Resulting sequences were analyzed using mothur (v1.39.5) and R (v3.6.0). The abundances of bacterial communities differed significantly between sample types. These differences in relative abundance were attributed to Clostridia, Holophagae and Gammaproteobacteria. Phylogenetic diversity increased with ADD for each bone type; comparably, ß-diversity bacterial community structure ordinated chronologically, which was explained with environmental parameters and inferred functional pathways. Models fit using rib samples provided a tighter prediction interval than scapulae, with a prediction of PMSI with root mean square error of within 472.31 (∼27 days) and 498.47 (∼29 days), respectively.

A eukaryotic community succession based method for postmortem interval (PMI) estimation of decomposing porcine remains.

Recent, short-term studies on porcine and human models (albeit with few replicates) demonstrated that the succession of the microbial community of remains may be used to estimate time since death. Using a porcine model (N=6) over an extended period of time (1703 ADD, or two months), this study characterized the eukaryote community of decomposing remains. Skin microbial samples were collected from the torso of each set of remains every day during the first week, on alternate days during the second week, and once a week for the remainder of the 60-day period; all collection intervals were recorded in accumulated degree days (ADD). The eukaryote community of each sample was determined using 18S ribosomal DNA (rDNA) MiSeq high throughput sequencing; data were analyzed in the Mothur pipeline (v1.39.5) and in IBM SPSS and R statistical packages. The relative abundance of eukaryote taxa across ADD/Days and an Analysis of Molecular Variance (AMOVA) indicated similarities between sequential ADD/Days, but significant differences in the eukaryote communities as broad stage 'milestones' of decomposition were reached. Fresh remains (0-57 ADD/0-2 Days; exhibiting a total body score (TBS) of 0-10) were characterized by the combined presence of Saccharomycetaceae, Debaryomycetaceae, Trichosporonaceae, Rhabditida, and Trichostomatia. During bloat and active decay (87-209 ADD/3-7 Days; exhibiting TBS of 11-20), Diptera was the most abundant eukaryotic taxa. During advanced decay stage (267-448 ADD/9-15 Days; exhibiting TBS of 21-25), Rhabditida was the most dominant eukaryote. Dry/skeletal remains (734-1703 ADD/26-61 Days; TBS≥26) were dominated by fungal families Dipodascaceae, Debaryomycetaceae, Trichosporonaceae, and Sporidiobolaceae. Using the family-level eukaryote taxonomic data for the entire study, random forest modelling explained 89.58% of the variation in ADD/Days, with a root mean square error (RMSE) of 177.55 ADD (≈6 days). Overall, these results highlight the importance of the microbial eukaryote community during the process of decomposition and in estimation of PMI.

Addressing uncertainty in fecal indicator bacteria dark inactivation rates.

Assessing the potential threat of fecal contamination in surface water often depends on model forecasts which assume that fecal indicator bacteria (FIB, a proxy for the concentration of pathogens found in fecal contamination from warm-blooded animals) are lost or removed from the water column at a certain rate (often referred to as an "inactivation" rate). In efforts to reduce human health risks in these water bodies, regulators enforce limits on easily-measured FIB concentrations, commonly reported as most probable number (MPN) and colony forming unit (CFU) values. Accurate assessment of the potential threat of fecal contamination, therefore, depends on propagating uncertainty surrounding "true" FIB concentrations into MPN and CFU values, inactivation rates, model forecasts, and management decisions. Here, we explore how empirical relationships between FIB inactivation rates and extrinsic factors might vary depending on how uncertainty in MPN values is expressed. Using water samples collected from the Neuse River Estuary (NRE) in eastern North Carolina, we compare Escherichia coli (EC) and Enterococcus (ENT) dark inactivation rates derived from two statistical models of first-order loss; a conventional model employing ordinary least-squares (OLS) regression with MPN values, and a novel Bayesian model utilizing the pattern of positive wells in an IDEXX Quanti-Tray®/2000 test. While our results suggest that EC dark inactivation rates tend to decrease as initial EC concentrations decrease and that ENT dark inactivation rates are relatively consistent across different ENT concentrations, we find these relationships depend upon model selection and model calibration procedures. We also find that our proposed Bayesian model provides a more defensible approach to quantifying uncertainty in microbiological assessments of water quality than the conventional MPN-based model, and that our proposed model represents a new strategy for developing robust relationships between environmental factors and FIB inactivation rates, and for reducing uncertainty in water resource management decisions.

Relationship between PM2.5 collected at residential outdoor locations and a central site.

Regression models are developed to describe the relationship between ambient PM2.5 (particulate matter [PM] < or = 2.5 microm in aerodynamic diameter) mass concentrations measured at a central-site monitor with those at residential outdoor monitors. Understanding the determinants and magnitude of variability and uncertainty in this relationship is critical for understanding personal exposures in the evaluation of epidemiological data. The repeated measures regression models presented here address temporal and spatial characteristics of data measured in the 2004-2007 Detroit Exposure and Aerosol Research Study, and they take into account missing data and other data features. The models incorporate turbulence kinetic energy and planetary boundary layer height, meteorological data that are not routinely considered in models that relate central-site concentrations to exposure to health effects. It was found that turbulence kinetic energy was highly statistically significant in explaining the relationship of PM2.5 measured at a particular stationary outdoor air monitoring site with PM2.5 measured outside nearby residences for the temporal coverage of the data.