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.

Prediction of minimum postmortem submersion interval (PMSI) 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 (PMSI). 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 PMSI 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.

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.

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.

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).

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.

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.