The Next Frontier in Forensic Genetics:
Single-Cell Genetic Analysis and its Expanding Role
Jack Ballantyne, Catherine M. Grgicak, Michael A. Marciano
Preliminary determination of a prior information for
forensic lubricant analysis in sexual assault cases
Candice Bridge, Brooke Baugarten, Liansheng Tang, Ngoc Ty Nguyen
From novice to expert: Student conceptual growth in
understanding experimental error using a hands-on laboratory activity
Charolette Disney, Dana-Marie Dennis, and Tamra Legron-Rodriguez
Exploring Scientific Literature Using Topic Modeling: A Practical
Framework for Discovery and Classification
Amir Alipour Yengejeh, Larry Tang, Candice M. Bridge and Chandra Kundu
Detection and quantification of trace technetium in the presence of
molybdenum using laser-induced breakdown spectroscopy
Hunter B. Andrews, Zachary Murphy, Mauro Martinez, John Lucchi, Vasileios Anagnostopoulos and Matthieu Baudelet
Challenges in mRNA-based time since deposition estimation:
A multiplex MPS primer panel investigation
Nadescha Viviane Hanggi, Melisa Walliser, Ane Elida Fonnelop, Robert Lagace, Erin Hanson, Jack Ballantyne, Cordula Haas
Build-a-bone: development of a matrix-matched reference material
for quantitative analysis of bone with portable LIBS
Kristen M. Livingston, Amanda T. Williams, and Matthieu Baudelet
Development of a matrix-matched standard for the
elemental analysis of human hair by LA-ICP-MS
Kaitlyn Bonilla, Ashley Fox, Chloe Phillips, Matthieu Baudelet
Probabilistic genotyping replicate analysis of FaSTR clustered
single sperm aSTR haplotypes reconstitutes probative
diploid DNA genotypes from complex semen mixtures
Morgan Peters, Kaitlin Huffman, Jack Ballantyne, Erin Hanson
mRNA profiling and donor association of mock casework samples:
Results of a 3rd and 4th EDNAP collaborative exercise
NCFS participants are Erin Hanson and Jack Ballantyne
Determining viability of image processing models for forensic analysis of
hair for related individuals
Zyuqing Olivia Wang, Davide S. H. Funes, and Candice Bridge
Evaluating machine learning methods on a largescale of in silico fire debris data
Larry Tang, Slun Booppasiri, Michael Sigman, Mary Williams
In silico created fire debris data for Machine learning
Michael Sigman, Mary Williams, Larry Tang, Slun Booppasiri, and Nikhil Prakash
Data used in this study and resulting in silico data are available at the ILRC-Substrate-Fire Debris databases website
