Differentiating patterns of violence in the family

The feasibility and prevalence of Reciprocal, Hierarchical and Paternal patterns of family aggression hypothesised by Dixon and Browne (2003) were explored within a sample of maltreating families. The psychological reports of 67 families referred to services for alleged child maltreatment that evidenced concurrent physical intimate partner violence and child maltreatment were investigated. Of these, 29 (43.3%) cases were characterised by hierarchical; 28 (41.8%) Reciprocal and 10 (14.9%) Paternal patterns. Significant differences in the form of child maltreatment perpetrated by mothers and fathers and parent dyads living in different patterns were found. In Hierarchical sub-patterns, fathers were significantly more likely to have been convicted for a violent and/or sexual offence than mothers and were significantly less likely to be biologically related to the child. The findings demonstrate the existence of the different patterns in a sample of families involved in the Child Care Protection process in England and Wales, supporting the utility of a holistic approach to understanding aggression in the family

Output-only full-field modal testing

© 2017 The Authors. Published by Elsevier Ltd. Operational modal analysis has become the focus of much research attention in the last two decades. Instead of an artificial force, the ambient excitation is considered as white-noise input to the structure and modal properties are calculated only from measured responses. In terms of the measurement technique, full-field optical methods, for example: electronic speckle pattern interferometry and digital image correlation have become popular and there is now much interest in applying these methods in structural dynamics. In this case the generated data is a full displacement map of the object, therefore there is no necessity to select specific measurement locations in order to visualise the deformation. However, there are generally large volumes of data to be processed, which makes the computation expensive and time-consuming, especially for engineering structures with large surface areas. Thanks to image decomposition techniques, huge amounts of data can be compressed into tens of shape descriptors with acceptably small distortion. In this paper, operational modal analysis and full-field methods are combined together, and the analysis is done in the shape descriptor domain to reduce the required computation time. Simulated responses from a finite element model of a clamped plate (under random excitation) serve to illustrate the methodology. Several different operational modal analysis methods are applied to analyse the data, and results are provided for purposes of comparison

The geography of recent genetic ancestry across Europe

The recent genealogical history of human populations is a complex mosaic formed by individual migration, large-scale population movements, and other demographic events. Population genomics datasets can provide a window into this recent history, as rare traces of recent shared genetic ancestry are detectable due to long segments of shared genomic material. We make use of genomic data for 2,257 Europeans (the POPRES dataset) to conduct one of the first surveys of recent genealogical ancestry over the past three thousand years at a continental scale. We detected 1.9 million shared genomic segments, and used the lengths of these to infer the distribution of shared ancestors across time and geography. We find that a pair of modern Europeans living in neighboring populations share around 10-50 genetic common ancestors from the last 1500 years, and upwards of 500 genetic ancestors from the previous 1000 years. These numbers drop off exponentially with geographic distance, but since genetic ancestry is rare, individuals from opposite ends of Europe are still expected to share millions of common genealogical ancestors over the last 1000 years. There is substantial regional variation in the number of shared genetic ancestors: especially high numbers of common ancestors between many eastern populations likely date to the Slavic and/or Hunnic expansions, while much lower levels of common ancestry in the Italian and Iberian peninsulas may indicate weaker demographic effects of Germanic expansions into these areas and/or more stably structured populations. Recent shared ancestry in modern Europeans is ubiquitous, and clearly shows the impact of both small-scale migration and large historical events. Population genomic datasets have considerable power to uncover recent demographic history, and will allow a much fuller picture of the close genealogical kinship of individuals across the world.Comment: Full size figures available from http://www.eve.ucdavis.edu/~plralph/research.html; or html version at http://ralphlab.usc.edu/ibd/ibd-paper/ibd-writeup.xhtm

Uncertainty quantification in DIC with Kriging regression

© 2015 Elsevier Ltd. All rights reserved. A Kriging regression model is developed as a post-processing technique for the treatment of measurement uncertainty in classical subset-based Digital Image Correlation (DIC). Regression is achieved by regularising the sample-point correlation matrix using a local, subset-based, assessment of the measurement error with assumed statistical normality and based on the Sum of Squared Differences (SSD) criterion. This leads to a Kriging-regression model in the form of a Gaussian process representing uncertainty on the Kriging estimate of the measured displacement field. The method is demonstrated using numerical and experimental examples. Kriging estimates of displacement fields are shown to be in excellent agreement with 'true' values for the numerical cases and in the experimental example uncertainty quantification is carried out using the Gaussian random process that forms part of the Kriging model. The root mean square error (RMSE) on the estimated displacements is produced and standard deviations on local strain estimates are determined

Establishing the precise evolutionary history of a gene improves prediction of disease-causing missense mutations

PURPOSE: Predicting the phenotypic effects of mutations has become an important application in clinical genetic diagnostics. Computational tools evaluate the behavior of the variant over evolutionary time and assume that variations seen during the course of evolution are probably benign in humans. However, current tools do not take into account orthologous/paralogous relationships. Paralogs have dramatically different roles in Mendelian diseases. For example, whereas inactivating mutations in the NPC1 gene cause the neurodegenerative disorder Niemann-Pick C, inactivating mutations in its paralog NPC1L1 are not disease-causing and, moreover, are implicated in protection from coronary heart disease. METHODS: We identified major events in NPC1 evolution and revealed and compared orthologs and paralogs of the human NPC1 gene through phylogenetic and protein sequence analyses. We predicted whether an amino acid substitution affects protein function by reducing the organism’s fitness. RESULTS: Removing the paralogs and distant homologs improved the overall performance of categorizing disease-causing and benign amino acid substitutions. CONCLUSION: The results show that a thorough evolutionary analysis followed by identification of orthologs improves the accuracy in predicting disease-causing missense mutations. We anticipate that this approach will be used as a reference in the interpretation of variants in other genetic diseases as well. Genet Med 18 10, 1029–1036