Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis: Gunshot backspatters.

ABSTRACT: This is a data set of blood spatter patterns scanned at high resolution, generated in controlled experiments. The spatter patterns were generated with a rifle or a handgun with varying ammunition. The resulting atomized blood droplets travelled opposite to the bullet direction, generating a gunshot backspatter on a poster board target sheet. Fresh blood with anticoagulants was used; its hematocrit and temperature were measured. The main parameters of the study were the bullet shape, size and speed, and the distance between the blood source and target sheet. Several other parameters were explored in a less systematic way. This new and original data set is suitable for training or research purposes in the forensic discipline of bloodstain pattern analysis.

SUBMITTER: Attinger D

PROVIDER: S-EPMC6305896 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis: Gunshot backspatters.

Attinger Daniel D Liu Yu Y Faflak Ricky R Rao Yalin Y Struttman Bryce A BA De Brabanter Kris K Comiskey Patrick M PM Yarin Alexander L AL

Data in brief 20181120

This is a data set of blood spatter patterns scanned at high resolution, generated in controlled experiments. The spatter patterns were generated with a rifle or a handgun with varying ammunition. The resulting atomized blood droplets travelled opposite to the bullet direction, generating a gunshot backspatter on a poster board target sheet. Fresh blood with anticoagulants was used; its hematocrit and temperature were measured. The main parameters of the study were the bullet shape, size and spe ...[more]

PMID: 30815519

Similar Datasets

A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis: Impact beating spatters.

Project description:This is a data set of 61 blood spatter patterns scanned at high resolution, generated by controlled impact events corresponding to forensic beating situations. The spatter patterns were realized with two test rigs, to vary the geometry and speed of the impact of a solid object on a blood source - a pool of blood. The resulting atomized blood droplets travelled a set distance towards a poster board sheet, creating a blood spatter. Fresh swine blood was used; its hematocrit and temperature were measured. Main parameters of the study were the impact velocity and the distance between blood source and target sheet, and several other parameters were explored in a less systematic way. This new and original data set is suitable for training or research purposes in the forensic discipline of bloodstain pattern analysis.

| S-EPMC5996142 | biostudies-literature

Ballistic research techniques: visualizing gunshot wounding patterns.

Project description:There are difficulties associated with mapping gunshot wound (GSW) patterns within opaque models. Depending on the damage measurement parameters required, there are multiple techniques that can provide methods of "seeing" the GSW pattern within an opaque model. The aim of this paper was to test several of these techniques within a cadaveric animal limb model to determine the most effective. The techniques of interest were flash X-ray, ultrasound, physical dissection, and computed-tomography (CT). Fallow deer hind limbs were chosen for the model with four limbs used for each technique tested. Quarantined 7.62?×?39 mm ammunition was used for each shot, and each limb was only shot once, on an outdoor range with shots impacting at muzzle velocity. Flash X-ray provided evidence of yaw within the limb during the projectile's flight; ultrasound though able to visualise the GSW track, was too subjective and was abandoned; dissection proved too unreliable due to the tissue being cadaveric so also too subjective; and lastly, CT with contrast provided excellent imaging in multiple viewing planes and 3D image reconstruction; this allowed versatile measurement of the GSW pattern to collect dimensions of damage as required. Of the different techniques examined in this study, CT with contrast proved the most effective to allow precise GSW pattern analysis within a cadaveric animal limb model. These findings may be beneficial to others wishing to undertake further ballistic study both within clinical and forensic fields.

| S-EPMC7181419 | biostudies-literature

Bloodstain Pattern Analysis: implementation of a fluid dynamic model for position determination of victims.

Project description:Bloodstain Pattern Analysis is a forensic discipline in which, among others, the position of victims can be determined at crime scenes on which blood has been shed. To determine where the blood source was investigators use a straight-line approximation for the trajectory, ignoring effects of gravity and drag and thus overestimating the height of the source. We determined how accurately the location of the origin can be estimated when including gravity and drag into the trajectory reconstruction. We created eight bloodstain patterns at one meter distance from the wall. The origin's location was determined for each pattern with: the straight-line approximation, our method including gravity, and our method including both gravity and drag. The latter two methods require the volume and impact velocity of each bloodstain, which we are able to determine with a 3D scanner and advanced fluid dynamics, respectively. We conclude that by including gravity and drag in the trajectory calculation, the origin's location can be determined roughly four times more accurately than with the straight-line approximation. Our study enables investigators to determine if the victim was sitting or standing, or it might be possible to connect wounds on the body to specific patterns, which is important for crime scene reconstruction.

| S-EPMC4476491 | biostudies-literature

Gene Set Analysis: Challenges, Opportunities, and Future Research.

Project description:Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses. In this paper, we classify gene set analysis methods based on their components, describe the underlying requirements and assumptions for each class, and provide directions for future research in developing and evaluating gene set analysis methods.

| S-EPMC7339292 | biostudies-literature

The effect of military clothing on gunshot wounding patterns in gelatine.

Project description:With no two gunshot wounds (GSW) being the same, novel research into wound ballistics is challenging. It is evident that the majority of previous wound ballistic research has been conducted without the presence of clothing. Whilst the effect of clothing on wound contamination has been explored, there is a paucity of literature examining the effect of clothing on GSW patterns. The aim of this study was to test the effect of Multi-Terrain Pattern (MTP) UK military clothing on GSW patterns within calibrated blocks of 10% by mass gelatine, using two types of ammunition commonly used in recent conflicts-7.62 × 39 mm and 5.45 × 39 mm. In total, 36 blocks were shot, 18 by each projectile type, further divided into 6 with no clothing layers (Cnil), 6 with a single clothing layer (Cmin) and 6 with maximum clothing layers (Cmax) worn on active duty. Blocks were analysed with high-speed video and dissection to capture measurements of damage, and results compared using analysis of variance (ANOVA). Results showed significantly different damage measurements within blocks with Cmax for both ammunition types compared to the other clothing states. This may result in GSWs that require more extensive surgical management, inviting further study.

| S-EPMC6570663 | biostudies-literature

Multidimensional gene set analysis of genomic data.

Project description:Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.

| S-EPMC2860497 | biostudies-literature

SNP set association analysis for familial data.

Project description:Genome-wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single-nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine-based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual-SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score-based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within-family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.

| S-EPMC3683469 | biostudies-literature

Spatial patterns of vascular plant species richness in Poland - a data set.

Project description:Recognition of species richness spatial patterns is important for nature conservation and theoretical studies. Inventorying species richness, especially at a larger spatial extent is challenging, thus different data sources are joined and harmonized to obtain a comprehensive data set. Here we present a new data set showing vascular plant species richness in Poland based on a grid of 10 × 10 km squares. The data set was created using data from two sources: the Atlas of Distribution of Vascular Plants in Poland and the Polish Vegetation Database. Using this data set, we analysed 2,160 species with taxonomical nomenclature according to the Euro + Med PlantBase checklist in 3,283 squares covering the entire territory of Poland (ca. 312,000 km2). The species were divided into groups according to their status and frequency of distribution, and the statistics for each square were obtained. For purposes of analysis, sampling bias was assessed. The data set promotes theoretical analysis on species richness and reinforces the planning of nature conservations.

| S-EPMC10439123 | biostudies-literature

Gene set analysis for longitudinal gene expression data.

Project description:Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. GSA performs statistical tests for independent microarray samples at the level of gene sets rather than individual genes. Nowadays, an increasing number of microarray studies are conducted to explore the dynamic changes of gene expression in a variety of species and biological scenarios. In these longitudinal studies, gene expression is repeatedly measured over time such that a GSA needs to take into account the within-gene correlations in addition to possible between-gene correlations.We provide a robust nonparametric approach to compare the expressions of longitudinally measured sets of genes under multiple treatments or experimental conditions. The limiting distributions of our statistics are derived when the number of genes goes to infinity while the number of replications can be small. When the number of genes in a gene set is small, we recommend permutation tests based on our nonparametric test statistics to achieve reliable type I error and better power while incorporating unknown correlations between and within-genes. Simulation results demonstrate that the proposed method has a greater power than other methods for various data distributions and heteroscedastic correlation structures. This method was used for an IL-2 stimulation study and significantly altered gene sets were identified.The simulation study and the real data application showed that the proposed gene set analysis provides a promising tool for longitudinal microarray analysis. R scripts for simulating longitudinal data and calculating the nonparametric statistics are posted on the North Dakota INBRE website http://ndinbre.org/programs/bioinformatics.php. Raw microarray data is available in Gene Expression Omnibus (National Center for Biotechnology Information) with accession number GSE6085.

| S-EPMC3142525 | biostudies-literature

MAGMA: generalized gene-set analysis of GWAS data.

Project description:By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.

| S-EPMC4401657 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data