Sexual abuse in childhood and postoperative depression in women with breast cancer who opt for immediate reconstruction after mastectomy

INTRODUCTION Breast reconstruction is routinely offered to women who undergo mastectomy for breast cancer. However, patient-reported outcomes are mixed. Child abuse has enduring effects on adults’ well-being and body image. As part of a study into damaging effects of abuse on adjustment to breast cancer, we examined: (i) whether women with history of abuse would be more likely than other women to opt for reconstruction; and (ii) whether mood problems in women opting for reconstruction can be explained by greater prevalence of abuse. PATIENTS AND METHODS We recruited 355 women within 2-4 days after surgery for primary breast cancer; 104 had mastectomy alone and 29 opted for reconstruction. Using standardised questionnaires, women self-reported emotional distress and recollections of childhood sexual abuse. Self-report of distress was repeated 12 months later. RESULTS Women who had reconstruction were younger than those who did not. Controlling for this, they reported greater prevalence of abuse and more distress than those having mastectomy alone. They were also more depressed postoperatively, and this effect remained significant after controlling for abuse. CONCLUSIONS One interpretation of these findings is that history of abuse influences women's decisions about responding to the threat of mastectomy, but it is premature to draw inferences for practice until the findings are replicated. If they are replicated, it will be important to recognise increased vulnerability of some patients who choose reconstruction. Studying the characteristics and needs of women who opt for immediate reconstruction and examining the implications for women's adjustment should be a priority for research.

Information and punitiveness: trial reconstruction in Ireland

Purpose – The purpose of this paper is to report results from a rape trial reconstruction in Ireland. Design/methodology/approach – A studio audience of 100 members of the Irish public were selected to attend a TV programme by the Republic of Ireland’s national broadcasting organisation. This involved the examination of the sentencing of a rape case. The audience’s sentencing preferences were measured at the outset, when they had been given only summary information about the case, and later, when full details had been disclosed. Findings – Previous research examining changes in public attitudes to crime and punishment has shown that deliberation, including the provision of new information and discussion with others and experts, tends to decrease public punitiveness and increase public leniency towards sentencing. An experiment in Ireland, however, showed that providing information does not invariably and necessarily moderate punitive attitudes. This paper presents the results, and offers some explanations for the anomalous outcome. Research limitations/implications – The pre/post design, in which the audience served as their own controls, is a weak one, and participants may have responded to what they took to be the agenda of the producers. Due to the quality of the sample, the results may not be generalisable to the broader Irish population. Practical implications – Policy makers should recognise that the public is not uniformly punitive for all crimes. There is good research evidence to show that the apparent public appetite for tough punishment is illusory, and is a function of the way that polls measure public attitudes to punishment. Sentencers and those responsible for sentencing policy would benefit from a fuller understanding of the sorts of cases which illicit strong punitive responses from the public, and the reasons for this response. However any such understanding should not simply translate into responsiveness to the public’s punitive sentiments – where these exist. Innovative survey methods – like this experiment – which attempt to look beyond the top-of-the-head opinions by providing information and opportunities for deliberation should be welcomed and used more widely. Originality/value – There have been limited research studies which reports factors which may increase punitiveness through the provision of information and deliberation.

Pippi Longstocking, juvenile delinquent? Hachette, self-censorship and the moral reconstruction of postwar France

This paper looks at the blockages to the publication of children’s literature caused by the intellectual climate of the postwar era, through a case study of the editorial policy of Hachette, the largest publisher for children at this time. This period witnessed heightened tensions surrounding the social and humanitarian responsibilities of literature. Writers were blamed for having created a culture of defeatism, and collaborationist authors were punished harshly in the purges. In the case of children’s literature, the discourse on responsibility was made more urgent by the assumption that children were easily influenced by their reading material, and by the centrality of the young to the discourse on the moral reconstruction of France. As the politician and education reformer Gustave Monod put it: “penser l’avenir, c’est penser le sort des enfants et de la jeunesse.” These concerns led to the expansion of associations and publications dedicated to protecting children and promoting “good” reading matter for them, and, famously, to the 1949 law regulating publications for children, which banned the depiction of crime, debauchery and violence that might demoralise young readers. Using the testimonials of former employees, along with readers’ reports and editorial correspondence preserved in the Hachette archives, this paper will examine how individual editorial decisions and self-censorship strategies were shaped by the 1949 law with its attendant discourse of moral panic on children’s reading, and how national concerns for future citizens were balanced with commercial imperatives.

Evaluation of the realism of climate reconstruction using the Coexistence Approach with modern pollen samples from the Tibetan Plateau

We apply the Coexistence Approach (CoA) to reconstruct mean annual precipitation (MAP), mean annual temperature (MAT), mean temperature of thewarmestmonth (MTWA) and mean temperature of the coldest month (MTCO) at 44 pollen sites on the Qinghai–Tibetan Plateau. The modern climate ranges of the taxa are obtained (1) from county-level presence/absence data and (2) from data on the optimum and range of each taxon from Lu et al. (2011). The CoA based on the optimumand range data yields better predictions of observed climate parameters at the pollen sites than that based on the county-level data. The presence of arboreal pollen, most of which is derived fromoutside the region, distorts the reconstructions. More reliable reconstructions are obtained using only the non-arboreal component of the pollen assemblages. The root mean-squared error (RMSE) of the MAP reconstructions are smaller than the RMSE of MAT, MTWA and MTCO, suggesting that precipitation gradients are the most important control of vegetation distribution on the Qinghai–Tibetan Plateau. Our results show that CoA could be used to reconstruct past climates in this region, although in areas characterized by open vegetation the most reliable estimates will be obtained by excluding possible arboreal contaminants.

Sparse space-time boundary element methods for the heat equation

The goal of this work is the efficient solution of the heat equation with Dirichlet or Neumann boundary conditions using the Boundary Elements Method (BEM). Efficiently solving the heat equation is useful, as it is a simple model problem for other types of parabolic problems. In complicated spatial domains as often found in engineering, BEM can be beneficial since only the boundary of the domain has to be discretised. This makes BEM easier than domain methods such as finite elements and finite differences, conventionally combined with time-stepping schemes to solve this problem. The contribution of this work is to further decrease the complexity of solving the heat equation, leading both to speed gains (in CPU time) as well as requiring smaller amounts of memory to solve the same problem. To do this we will combine the complexity gains of boundary reduction by integral equation formulations with a discretisation using wavelet bases. This reduces the total work to O(h

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.

Sparse density estimator with tunable kernels

A new sparse kernel density estimator with tunable kernels is introduced within a forward constrained regression framework whereby the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Based on the minimum integrated square error criterion, a recursive algorithm is developed to select significant kernels one at time, and the kernel width of the selected kernel is then tuned using the gradient descent algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing very sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

Sparse density estimation on multinomial manifold combining local component analysis

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since the constraint on the mixing coefficients of a finite mixture model is on the multinomial manifold, we then use the well-known Riemannian trust-region algorithm to find the set of sparse mixing coefficients. The first and second order Riemannian geometry of the multinomial manifold are utilized in the Riemannian trust-region algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.