Butler JM (2004) Short tandem repeat analysis for human identity testing. John Wiley, New York. 2001–2020 United States

Book  Google Scholar 

Díez López C, Kayser M, Vidaki A (2021) Estimating the Time since Deposition of Saliva Stains with a targeted bacterial DNA Approach: a proof-of-Principle Study. Front Microbiol 12. https://doi.org/10.3389/fmicb.2021.647933

Rajamannar K (1977) Determination of the age of bloodstains using immunoelectrophoresi. J Forensic Sci 22:159–164

Article  PubMed  Google Scholar 

Das T, Harshey A, Srivastava A et al (2021) Analysis of the ex-vivo transformation of semen, saliva and urine as they dry out using ATR-FTIR spectroscopy and chemometric approach. Sci Rep 11. https://doi.org/10.1038/s41598-021-91009-5

Li B, Beveridge P, O’Hare WT, Islam M (2011) The estimation of the age of a blood stain using reflectance spectroscopy with a microspectrophotometer, spectral pre-processing and linear discriminant analysis. Forensic Sci Int. https://doi.org/10.1016/j.forsciint.2011.05.031

Article  PubMed  Google Scholar 

Rodrigues-Lima F, Hanson EK, Ballantyne J (2010) A Blue Spectral Shift of the Hemoglobin Soret Band Correlates with the age (Time since Deposition) of dried bloodstains. PLoS ONE 5. https://doi.org/10.1371/journal.pone.0012830

Doty KC, McLaughlin G, Lednev IK (2016) A Raman spectroscopic clock for bloodstain age determination: the first week after deposition. Anal Bioanal Chem 408:3993–4001. https://doi.org/10.1007/s00216-016-9486-z

Article  PubMed  Google Scholar 

Zhang R, Wang P, Chen J, Tian Y, Gao J (2023) Age estimation of bloodstains based on Raman spectroscopy and chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 290. https://doi.org/10.1016/j.saa.2022.122284

Edelman G, van Leeuwen TG, Aalders MCG (2012) Hyperspectral imaging for the age estimation of blood stains at the crime scene. Forensic Sci Int 223:72–77. https://doi.org/10.1016/j.forsciint.2012.08.003

Article  PubMed  Google Scholar 

Giulietti N, Discepolo S, Castellini P, Martarelli M (2023) Neural network based hyperspectral imaging for substrate independent bloodstain age estimation. Forensic Sci Int 349. https://doi.org/10.1016/j.forsciint.2023.111742

Li B, Beveridge P, O’Hare WT, Islam M (2013) The age estimation of blood stains up to 30days old using visible wavelength hyperspectral image analysis and linear discriminant analysis. Sci Justice 53:270–277. https://doi.org/10.1016/j.scijus.2013.04.004

Article  PubMed  Google Scholar 

Cadd S, Li B, Beveridge P, O’Hare WT, Campbell A, Islam M (2016) The non-contact detection and identification of blood stained fingerprints using visible wavelength reflectance hyperspectral imaging: part 1. Sci Justice 56:181–190. https://doi.org/10.1016/j.scijus.2016.01.004

Article  PubMed  Google Scholar 

Weber A, Wójtowicz A, Lednev IK (2021) Post deposition aging of bloodstains probed by steady-state fluorescence spectroscopy. Journal of Photochemistry and Photobiology B: Biology 221. https://doi.org/10.1016/j.jphotobiol.2021.112251

Achetib N, Leemberg CC, Geurts MMP et al (2023) Towards Onsite Age Estimation of Semen stains using fluorescence spectroscopy. Sensors 23. https://doi.org/10.3390/s23136148

Thanakiatkrai P, Yaodam A, Kitpipit T (2013) Age estimation of bloodstains using smartphones and digital image analysis. Forensic Sci Int 233:288–297. https://doi.org/10.1016/j.forsciint.2013.09.027

Article  PubMed  Google Scholar 

Oehmichen M KZ (1984) Postmortale DNA and RNA synthesis.Preliminary studies in human cadavers. Int J Legal Med 91:287–294

Asaghiar F, Williams GA (2020) Evaluating the use of hypoxia sensitive markers for body fluid stain age prediction. Sci Justice 60:547–554. https://doi.org/10.1016/j.scijus.2020.09.001

Article  PubMed  Google Scholar 

Sara C, Zapico GR (2023) A spit in time: identification of saliva stains and assessment of total DNA recovery up to 180 days after deposition. Forensic science, medicine, and pathology

Salzmann AP, Russo G, Kreutzer S, Haas C (2021) Degradation of human mRNA transcripts over time as an indicator of the time since deposition (TsD) in biological crime scene traces. Forensic Sci International: Genet 53. https://doi.org/10.1016/j.fsigen.2021.102524

Gosch A, Bhardwaj A, Courts C (2023) TrACES of time: transcriptomic analyses for the contextualization of evidential stains– identification of RNA markers for estimating time-of-day of bloodstain deposition. Forensic Sci International: Genet 67. https://doi.org/10.1016/j.fsigen.2023.102915

Bauer M, Polzin S, Patzelt D (2003) Quantification of RNA degradation by semi-quantitative duplex and competitive RT-PCR: a possible indicator of the age of bloodstains? Forensic Sci Int 138:94–103. https://doi.org/10.1016/j.forsciint.2003.09.008

Article  PubMed  Google Scholar 

Fang C, Zhou P, Li R et al (2023) Development of a novel forensic age estimation strategy for aged blood samples by combining piRNA and miRNA markers. Int J Legal Med 137:1327–1335. https://doi.org/10.1007/s00414-023-03028-8

Article  PubMed  Google Scholar 

Alshehhi S, Haddrill PR (2020) Evaluating the effect of body fluid mixture on the relative expression ratio of blood-specific RNA markers. Forensic Sci Int 307. https://doi.org/10.1016/j.forsciint.2019.110116

Wei Y, Wang J, Wang Q, Cong B, Li S (2022) The estimation of bloodstain age utilizing circRNAs and mRNAs biomarkers. Forensic Sci Int 338. https://doi.org/10.1016/j.forsciint.2022.111408

Anderson S, Howard B, Hobbs GR, Bishop CP (2005) A method for determining the age of a bloodstain. Forensic Sci Int 148:37–45. https://doi.org/10.1016/j.forsciint.2004.04.071

Article  PubMed  Google Scholar 

Alshehhi S, Haddrill PR (2019) Estimating time since deposition using quantification of RNA degradation in body fluid-specific markers. Forensic Sci Int 298:58–63. https://doi.org/10.1016/j.forsciint.2019.02.046

Article  PubMed  Google Scholar 

KD Weinbrecht JF, Payton M, R Allen (2017) Time-dependent loss of mRNA transcripts from forensic stains. Dovepress 7:1–12

Google Scholar 

Mei S, Zhao M, Liu Y et al (2022) Evaluations and comparisons of microbial diversities in four types of body fluids based on two 16S rRNA gene sequencing methods. Forensic Sci Int 331. https://doi.org/10.1016/j.forsciint.2021.111128

Adserias-Garriga J, Quijada NM, Hernandez M, Rodríguez Lázaro D, Steadman D, Garcia‐Gil LJ (2017) Dynamics of the oral microbiota as a tool to estimate time since death. Mol Oral Microbiol 32:511–516. https://doi.org/10.1111/omi.12191

Article  PubMed  Google Scholar 

Cho H-W, Eom Y-B (2021) Forensic analysis of human microbiome in skin and body fluids based on Geographic Location. Frontiers in Cellular and Infection Microbiology 11. https://doi.org/10.3389/fcimb.2021.695191

Dobay A, Haas C, Fucile G et al (2019) Microbiome-based body fluid identification of samples exposed to indoor conditions. Forensic Sci International: Genet 40:105–113. https://doi.org/10.1016/j.fsigen.2019.02.010

Article  Google Scholar 

Salzmann AP, Arora N, Russo G, Kreutzer S, Snipen L, Haas C (2021) Assessing time dependent changes in microbial composition of biological crime scene traces using microbial RNA markers. Forensic Sci International: Genet 53. https://doi.org/10.1016/j.fsigen.2021.102537

Yu H-J, Xiao CJN, Zang X-M, Zhang C-Y, Zhang X, Qu Y-N, Li Y, Tan Q-W (2020) Structural difference analysis of adult’s intestinal flora basing on the 16S rDNA gene sequencing technology. Eur Rev Med Pharmacol Sci 24:12983–12992

PubMed  Google Scholar 

Zeng Q, An S (2021) Identifying the biogeographic patterns of Rare and Abundant Bacterial communities using different primer sets on the Loess Plateau. Microorganisms 9. https://doi.org/10.3390/microorganisms9010139

Kechin A, Boyarskikh U, Kel A, Filipenko M (2017) cutPrimers: a New Tool for Accurate cutting of primers from reads of targeted next generation sequencing. J Comput Biol 24:1138–1143. https://doi.org/10.1089/cmb.2017.0096

Article  PubMed  Google Scholar 

Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016) DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583. https://doi.org/10.1038/nmeth.3869

Article  PubMed  PubMed Central  Google Scholar 

DeSantis TZ, Hugenholtz P, Larsen N et al (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072. https://doi.org/10.1128/aem.03006-05

Article  PubMed  PubMed Central  Google Scholar 

Bokulich NA, Kaehler BD, Rideout JR et al (2018) Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6. https://doi.org/10.1186/s40168-018-0470-z

Chao A (1984) Nonparametric estimation of the number of classes in a Population. Scandinavian J Stat Scandinavian J Stat 11:265–270

Google Scholar 

Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27

Simpson EH (1949) Measurement of Diversity. Nature 163:688

Article  Google Scholar 

Faith DP (1992) Conservation evaluation and phylogenetic diversity. Biol Conserv 61:1–10

Article  Google Scholar 

Pielou EC (1966) The measurement of diversity in different types of biological collections. J Theor Biol 13:131–144

Article  Google Scholar 

Good IJ (1953) The population frequency of species and the estimation of the population parameters. Biometrics 40:237–246

Article  Google Scholar 

Lozupone CA, Hamady M, Kelley ST, Knight R (2007) Quantitative and qualitative β diversity measures lead to different insights into factors that structure Microbial communities. Appl Environ Microbiol 73:1576–1585. https://doi.org/10.1128/aem.01996-06

Article  PubMed  PubMed Central  Google Scholar 

Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing Microbial communities. Appl Environ Microbiol 71:8228–8235. https://doi.org/10.1128/aem.71.12.8228-8235.2005

Article  PubMed  PubMed Central  Google Scholar 

McDonald D, Price MN, Goodrich J et al (2011) An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 6:610–618. https://doi.org/10.1038/ismej.2011.139

Article  PubMed  PubMed Central  Google Scholar 

Kazunori Sagawa AK, Yoshifumi Saito H, Inoue S, Yasuda M, Nosaka T Tsuji (2003) Production and characterization of a monoclonal antibody for sweat-specific protein and its application for sweat identification. Int J Legal Med 117. https://doi.org/10.1007/s00414-002-0341-8

Article  Google Scholar 

Linus Altmeyer KB, Diana H (2024) Differentiation of five forensically relevant body fluids using a small set of microRNA markers. Electrophoresis 45.