Crime reconstruction and the role of trace materials from crime scene to court

Crime reconstruction takes place in a complex ecosystem and needs to be responsive to the context of each case. For accurate, reproducible and transparent crime reconstructions to take place, a holistic approach is needed that considers the different stakeholders, different types of trace material, integral human decision‐making and interconnected nature of the forensic science process. For robust reconstruction, there needs to be a consideration of both the distinctive types of trace material that can contribute to the reconstruction, and an understanding of the interplay of human decision‐making within reconstruction approaches. In addition, it is also necessary to consider source attribution of a trace material in addition to the activities that led to the generation, identification, transfer, and persistence of the trace. This requires explicit and tacit forms of knowledge, and an incorporation of the inherent uncertainty and risk in the reconstruction approach. The communication of conclusions reached in a crime reconstruction that address what the evidence means is also an important consideration given the different requirements of intelligence and evidence. Therefore, undertaking a crime reconstruction within a holistic framework that seeks to incorporate the complexity of the forensic science ecosystem is valuable for achieving a problem solving approach that offers reproducible, transparent reconstructions with a clear articulation of risk and uncertainty that can be of value to investigators and the courts. This article is categorized under: Forensic Science in Action/Crime Scene Investigation > Crime Scene Reconstruction Forensic Science in Action/Crime Scene Investigation > From Traces to Intelligence and Evidenc

Optimality of Human Contour Integration

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy

The spatial range of peripheral collinear facilitation

Contrast detection thresholds for a central Gabor patch (target) can be modulated by the presence of co-oriented and collinear high contrast Gabors flankers. In foveal vision collinear facilitation can be observed for target-to-flankers relative distances beyond two times the wavelength (λ) of the Gabor's carrier, while for shorter relative distances (<2λ) there is suppression. These modulatory influences seem to disappear after 12λ. In this study, we measured contrast detection thresholds for different spatial frequencies (1, 4 and 6 cpd) and target-to-flankers relative distances ranging from 6 to 16λ, but with collinear configurations presented in near periphery at 4° of eccentricity. Results showed that in near periphery collinear facilitation extends beyond 12λ for the higher spatial frequencies tested (4 and 6 cpd), while it decays already at 10λ for the lowest spatial frequency used (i.e., 1 cpd). In addition, we found that increasing the spatial frequency the peak of collinear facilitation shifts towards larger target-to-flankers relative distances (expressed as multiples of the stimulus wavelength), an effect never reported neither for near peripheral nor for central vision. The results suggest that the peak and the spatial extent of collinear facilitation in near periphery depend on the spatial frequency of the stimuli used

Neocortical Axon Arbors Trade-off Material and Conduction Delay Conservation

The brain contains a complex network of axons rapidly communicating information between billions of synaptically connected neurons. The morphology of individual axons, therefore, defines the course of information flow within the brain. More than a century ago, Ramón y Cajal proposed that conservation laws to save material (wire) length and limit conduction delay regulate the design of individual axon arbors in cerebral cortex. Yet the spatial and temporal communication costs of single neocortical axons remain undefined. Here, using reconstructions of in vivo labelled excitatory spiny cell and inhibitory basket cell intracortical axons combined with a variety of graph optimization algorithms, we empirically investigated Cajal's conservation laws in cerebral cortex for whole three-dimensional (3D) axon arbors, to our knowledge the first study of its kind. We found intracortical axons were significantly longer than optimal. The temporal cost of cortical axons was also suboptimal though far superior to wire-minimized arbors. We discovered that cortical axon branching appears to promote a low temporal dispersion of axonal latencies and a tight relationship between cortical distance and axonal latency. In addition, inhibitory basket cell axonal latencies may occur within a much narrower temporal window than excitatory spiny cell axons, which may help boost signal detection. Thus, to optimize neuronal network communication we find that a modest excess of axonal wire is traded-off to enhance arbor temporal economy and precision. Our results offer insight into the principles of brain organization and communication in and development of grey matter, where temporal precision is a crucial prerequisite for coincidence detection, synchronization and rapid network oscillations

Identification and characterization of textile fibers by thermal analysis

Textile fibers are ubiquitous in the sense that they are present in the fabric of clothing, furniture, floor and wall covering. A remarkable variety of textile fibers with different chemical compositions are produced for many different commercial applications. As fibers are readily transferred they are frequently recovered from crime scenes as trace evidence and may provide useful forensic information for identification purposes. The objective of the experiment described in this paper is to evaluate specific thermal analysis techniques (DSC and TGA) as means by which textile fibers or cloth fragments may be reliably identified using experimentally simple and reproducible instrumental methods. The results are analyzed to determine whether these techniques may serve to identify different textiles on the basis of their thermal behavior, to decide if sample matching is possible and to evaluate detection limits for the sample-instrument combination used.Fundação para a Ciência e a Tecnologia (FCT