First contribution of mites (Acari) to the forensic analysis of hanged corpses: a case study from Spain

This case study from North Spain, highlights the importance of the collection of mites in addition to insects, from crime scenes or corpses subjected to environmental constraints that reduce or minimise insect activity, such as hanged corpses. In addition, this analysis highlights the relevance of arthropods’ collection in the field, even after the corpse has been moved away for autopsy. Four species of mites, phoretic on carrion (Silphidae) and rove (Staphylinidae) beetles, complemented and reinforced the autopsy analysis as well as the scarce information provided by insect activity. Poecilochirus carabi Canestrini & Canestrini, 1882 and Poecilochirus (Physoparasitus) davydovae Hyatt, 1980 (Mesostigmata: Parasitidae) were found in association with two Silphidae, Nicrophorus Fabricius, 1775 and Necrodes Leach, 1815, only when sampled in the autopsy room; this is suggestive of host-switching of mites and was likely due to the lack of availability of specific carriers in the field. The interpretation of the activity of Parasitidae mites both in the field and the autopsy room allows a better understanding of the timing and circumstances of decomposition. Phoretic deutonymphs of Pelzneria Scheucher 1957 (Astigmata: Histiostomatidae) were highly abundant, mostly P. crenulata Oudemans, 1909 and are reported for the first time on a Staphylinidae rove beetle, Creophilus maxillosus (L., 1758). Surprisingly, in this case study no Pelzneria were associated with the Silphidae found, which are their most common hosts, such as Necrodes littoralis (L., 1758) and Nicrophorus interruptus (Stephens, 1830). All histiostomatids were removed from the staphylinid (rove beetle) collected from the soil, at the scene of death, suggesting a recent arrival of the beetle. The occurrence of Staphylinidae beetles and their associated mites, such as Parasitidae and Pelzneria, and the information they provided would have been easily overlooked or lost if only the autopsy sampling would have been considered in the analysis of the case. The four mite species are reported for the first time for the Iberian Peninsula.

Tensor regression based on linked multiway parameter analysis

Classical regression methods take vectors as covariates and estimate the corresponding vectors of regression parameters. When addressing regression problems on covariates of more complex form such as multi-dimensional arrays (i.e. tensors), traditional computational models can be severely compromised by ultrahigh dimensionality as well as complex structure. By exploiting the special structure of tensor covariates, the tensor regression model provides a promising solution to reduce the model’s dimensionality to a manageable level, thus leading to efficient estimation. Most of the existing tensor-based methods independently estimate each individual regression problem based on tensor decomposition which allows the simultaneous projections of an input tensor to more than one direction along each mode. As a matter of fact, multi-dimensional data are collected under the same or very similar conditions, so that data share some common latent components but can also have their own independent parameters for each regression task. Therefore, it is beneficial to analyse regression parameters among all the regressions in a linked way. In this paper, we propose a tensor regression model based on Tucker Decomposition, which identifies not only the common components of parameters across all the regression tasks, but also independent factors contributing to each particular regression task simultaneously. Under this paradigm, the number of independent parameters along each mode is constrained by a sparsity-preserving regulariser. Linked multiway parameter analysis and sparsity modeling further reduce the total number of parameters, with lower memory cost than their tensor-based counterparts. The effectiveness of the new method is demonstrated on real data sets.

Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles

Background: The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. New method: We propose a complete pipeline for the cluster analysis of ERP data. To increase the signalto-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA)to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). Results: After validating the pipeline on simulated data, we tested it on data from two experiments – a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership.

Extracting planetary waves from geomagnetic time series using Empirical Mode Decomposition

Empirical Mode Decomposition is presented as an alternative to traditional analysis methods to decompose geomagnetic time series into spectral components. Important comments on the algorithm and its variations will be given. Using this technique, planetary wave modes of 5-, 10-, and 16-day mean periods can be extracted from magnetic field components of three different stations in Germany. In a second step, the amplitude modulation functions of these wave modes can be shown to contain significant contribution from solar cycle variation through correlation with smoothed sunspot numbers. Additionally, the data indicate connections with geomagnetic jerk occurrences, supported by a second set of data providing reconstructed near-Earth magnetic field for 150 years. Usually attributed to internal dynamo processes within the Earth's outer core, the question of who is impacting whom will be briefly discussed here.

Human versus animal: contrasting decomposition dynamics of mammalian analogues in experimental taphonomy

Taphonomic studies regularly employ animal analogues for human decomposition due to ethical restrictions relating to the use of human tissue. However, the validity of using animal analogues in soil decomposition studies is still questioned. This study compared the decomposition of skeletal muscle tissues (SMTs) from human (Homo sapiens), pork (Sus scrofa), beef (Bos taurus), and lamb (Ovis aries) interred in soil microcosms. Fixed interval samples were collected from the SMT for microbial activity and mass tissue loss determination; samples were also taken from the underlying soil for pH, electrical conductivity, and nutrient (potassium, phosphate, ammonium, and nitrate) analysis. The overall patterns of nutrient fluxes and chemical changes in nonhuman SMT and the underlying soil followed that of human SMT. Ovine tissue was the most similar to human tissue in many of the measured parameters. Although no single analogue was a precise predictor of human decomposition in soil, all models offered close approximations in decomposition dynamics.

Freezing skeletal muscle tissue does not affect its decomposition in soil: Evidence from temporal changes in tissue mass, microbial activity and soil chemistry based on excised samples

The study of decaying organisms and death assemblages is referred to as forensic taphonomy, or more simply the study of graves. This field is dominated by the fields of entomology, anthropology and archaeology. Forensic taphonomy also includes the study of the ecology and chemistry of the burial environment. Studies in forensic taphonomy often require the use of analogues for human cadavers or their component parts. These might include animal cadavers or skeletal muscle tissue. However, sufficient supplies of cadavers or analogues may require periodic freezing of test material prior to experimental inhumation in the soil. This study was carried out to ascertain the effect of freezing on skeletal muscle tissue prior to inhumation and decomposition in a soil environment under controlled laboratory conditions. Changes in soil chemistry were also measured. In order to test the impact of freezing, skeletal muscle tissue (Sus scrofa) was frozen (−20 °C) or refrigerated (4 °C). Portions of skeletal muscle tissue (∼1.5 g) were interred in microcosms (72 mm diameter × 120 mm height) containing sieved (2 mm) soil (sand) adjusted to 50% water holding capacity. The experiment had three treatments: control with no skeletal muscle tissue, microcosms containing frozen skeletal muscle tissue and those containing refrigerated tissue. The microcosms were destructively harvested at sequential periods of 2, 4, 6, 8, 12, 16, 23, 30 and 37 days after interment of skeletal muscle tissue. These harvests were replicated 6 times for each treatment. Microbial activity (carbon dioxide respiration) was monitored throughout the experiment. At harvest the skeletal muscle tissue was removed and the detritosphere soil was sampled for chemical analysis. Freezing was found to have no significant impact on decomposition or soil chemistry compared to unfrozen samples in the current study using skeletal muscle tissue. However, the interment of skeletal muscle tissue had a significant impact on the microbial activity (carbon dioxide respiration) and chemistry of the surrounding soil including: pH, electroconductivity, ammonium, nitrate, phosphate and potassium. This is the first laboratory controlled study to measure changes in inorganic chemistry in soil associated with the decomposition of skeletal muscle tissue in combination with microbial activity.

Heterogeneous tensor decomposition for clustering via manifold optimization

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.