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

Full-wave modeling of broadband near field scanning microwave microscopy

The authors would like to thank professor Dr. Gabriel Gomila from Institut de Bioenginyeria de Catalunya (IBEC) and Universitat de Barcelona for the fruitful discussion and support, as well as to Dr. Georg Gramse from Johannes Kepler University Linz for the experimental data. B.W. thanks the funding from the China Scholarship Council (CSC) for the support of his research at Queen Mary University of London, UK. Y.H. would like to thank EU-FP7 Nanomicrowave project for the financial support

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

Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields

Even in V1, where neurons have well characterized classical receptive fields (CRFs), it has been difficult to deduce which features of natural scenes stimuli they actually respond to. Forward models based upon CRF stimuli have had limited success in predicting the response of V1 neurons to natural scenes. As natural scenes exhibit complex spatial and temporal correlations, this could be due to surround effects that modulate the sensitivity of the CRF. Here, instead of attempting a forward model, we quantify the importance of the natural scenes surround for awake macaque monkeys by modeling it non-parametrically. We also quantify the influence of two forms of trial to trial variability. The first is related to the neuron’s own spike history. The second is related to ongoing mean field population activity reflected by the local field potential (LFP). We find that the surround produces strong temporal modulations in the firing rate that can be both suppressive and facilitative. Further, the LFP is found to induce a precise timing in spikes, which tend to be temporally localized on sharp LFP transients in the gamma frequency range. Using the pseudo R[superscript 2] as a measure of model fit, we find that during natural scene viewing the CRF dominates, accounting for 60% of the fit, but that taken collectively the surround, spike history and LFP are almost as important, accounting for 40%. However, overall only a small proportion of V1 spiking statistics could be explained (R[superscript 2]~5%), even when the full stimulus, spike history and LFP were taken into account. This suggests that under natural scene conditions, the dominant influence on V1 neurons is not the stimulus, nor the mean field dynamics of the LFP, but the complex, incoherent dynamics of the network in which neurons are embedded.National Institutes of Health (U.S.) (K25 NS052422-02)National Institutes of Health (U.S.) (DP1 ODOO3646

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