Drs. Grgicak and Lun along with Sarah E. Norsworthy publish an article entitled “Determining the number of contributors to DNA mixtures in the low-template regime: Exploring the impacts of sampling and detection effects” in Legal Medicine (DOI: https://doi.org/10.1016/j.legalmed.2018.02.001).
We show that capturing all of the information has positive impacts on inference while allele counting methods lead to underestimations.
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The Forensic Technology Center of Excellence (FTCoE) will assist the National Institute of Justice (NIJ) in hosting the annual NIJ Forensic Science Research and Development (R&D) Symposium on February 20th, 2018 at the 70th Annual American Academy of Forensic Sciences (AAFS) meeting in Seattle, WA. The NIJ Forensic Science R&D Symposium is a free and open meeting where attendees learn about NIJ-funded research across a variety of forensic science areas.
Afternoon Session II: Forensic Biology/DNA
Dr Grgicak will present LFTDI’s work pertaining to ValiDNA in a talk entitled
? Production of High-Fidelity Electropherograms Results in Improved and Consistent Match-Statistics: Standardizing Forensic Validation by Coupling Laboratory Specific Experimental Data with an In Silico DNA Pipeline
We modify our full in silico DNA pipeline to focus on resolving signal-to-noise within the single-copy regime.
The paper describes system design and ways in which the system can be implemented into forensic DNA validation and optimization.
Lauren Alfonse, Jennifer Sheehan and Catherine Grgicak will present our most recent work at the upcoming NEAFS 2017 conference.
Lauren presents her work on CleanIt: An automated artifact filtering macro; Jennifer delves into double-back stutter; and Catherine introduces ReSOLVIt.
On October 26th, 2017 we will speak at a conference hosted by the Legal Aid Society. See our calendar of events for location and other details.
Dr. Desmond Lun will be speaking on our research pertaining to the development of computational methods in forensic science.
Dr. Catherine Grgicak speaks about signal quality and effects on continuous Likelihood Ratios.
Producing an empirical data set large enough to efficiently compare, contrast, and validate forensic DNA computational systems is costly, labor-intensive, and requires the amplification of many samples that may not be readily available. In an effort to provide continued support to the forensic science community and to foster growth in both forensic research and operations, we announce the PROVEDIt initiative: Project Research Openness for Validation with Empirical Data.
First announced at the 2016 International Symposium on Human Identification, PROVEDIt comprises 25,000 .fsa and .hid profiles as well as a suite of analysis, interpretation, and in silico software systems/procedures and models developed in a variety of environments by a multi-disciplinary, inter- institutional team.