Epigenetic modification of the oxytocin and glucocorticoid receptor genes is linked to attachment avoidance in young adults

Attachment in the context of intimate pair bonds is most frequently studied in terms of the universal strategy to draw near, or away, from significant others at moments of personal distress. However, important interindividual differences in the quality of attachment exist, usually captured through secure versus insecure – anxious and/or avoidant – attachment orientations. Since Bowlby’s pioneering writings on the theory of attachment, it has been assumed that attachment orientations are influenced by both genetic and social factors – what we would today describe and measure as gene by environment interaction mediated by epigenetic DNA modification – but research in humans on this topic remains extremely limited. We for the first time examined relations between intra-individual differences in attachment and epigenetic modification of the oxytocin receptor (OXTR) and glucocorticoid receptor (NR3C1) gene promoter in 109 young adult human participants. Our results revealed that attachment avoidance was significantly and specifically associated with increased OXTR and NR3C1 promoter methylation. These findings offer first tentative clues on the possible etiology of attachment avoidance in humans by showing epigenetic modification in genes related to both social stress regulation and HPA axis functioning

Exploring the effect of attachment styles and winning or losing a status contest on testosterone levels

A person's ability to form relationships and seek and attain social status affects their chances of survival. We study how anxious and avoidant-attachment styles and subsequent winning or losing affects the testosterone (T) levels of team members playing two status contests. The first is a management game played by teams striving to earn the most profits. Winners and losers emerge due to the cognitive endeavor of the players, which provokes intense status dynamics. Avoidant-attached winners do not show higher T levels whereas anxious-attached winners do. The second is an economic game which is rigged and favors some teams to become richer than others; teams have the option though to trade with each other and reduce the self-perpetuating rich-poor dynamics embedded in the game. Besides attachment styles, we here also explore how authentic pride as a self-conscious emotion affects team members' T levels as players trade with others to create more fairness. As in the first status contest, players' T levels are not significantly affected by their avoidant attachment style, neither as a main effect nor in interaction with winning or losing the game. However, similar to the first game, players' anxious attachment style affects their T levels: anxious-attached players generate significantly higher T levels when winning the game, but only when experiencing high authentic pride during the game. In short, the moderating effects of attachment style on winners' T levels are partly replicated in both status games which allows us to better understand the functioning of working models of attachment styles during and after status contests and gives us a better understanding of working models of attachment styles in general

Secure and linear cryptosystems using error-correcting codes

A public-key cryptosystem, digital signature and authentication procedures based on a Gallager-type parity-check error-correcting code are presented. The complexity of the encryption and the decryption processes scale linearly with the size of the plaintext Alice sends to Bob. The public-key is pre-corrupted by Bob, whereas a private-noise added by Alice to a given fraction of the ciphertext of each encrypted plaintext serves to increase the secure channel and is the cornerstone for digital signatures and authentication. Various scenarios are discussed including the possible actions of the opponent Oscar as an eavesdropper or as a disruptor

Social Activism in Information Systems Research: Making the World a Better Place

This paper reports on a panel held during the 2006 International Conference on Information Systems (ICIS). The panel titled, Social Activism in IS Research: Making the World a Better Place, was organized to question whether and how Information System (IS) research is making tangible impacts to our society. More specifically, each panelist was asked to address: (1) How can IS research, and researchers, make contributions to underdeveloped societies and underserved communities?; and (2) How can IS researchers learn from the particularities of these communities to inform better research, teaching, and service? While each panel member had different perspectives to offer in relation to these two questions, all agreed that IS academe needs to raise its awareness and efforts considerably with a view to address the needs of underserved communities

Social Activism in IS Research: Making the World a Better Place

Information Systems (IS) can play a salient role in the transformation of our societies, especially in less-developed (or under-served) communities. IS can be used to benefit citizens in these societies through improvements in education, government, healthcare, social, and entrepreneurial systems. It would be a mistake to think that under-served communities can develop without optimal deployment of IS, after all advanced societies depended on IS to boost their development. The realization that IS offers potential benefit to improve the livelihood of the less-privileged is not new or recent. However, what is not clear is what should be the role of IS researchers in addressing the needs of the under-served communities

The dynamics of proving uncolourability of large random graphs I. Symmetric Colouring Heuristic

We study the dynamics of a backtracking procedure capable of proving uncolourability of graphs, and calculate its average running time T for sparse random graphs, as a function of the average degree c and the number of vertices N. The analysis is carried out by mapping the history of the search process onto an out-of-equilibrium (multi-dimensional) surface growth problem. The growth exponent of the average running time is quantitatively predicted, in agreement with simulations.Comment: 5 figure

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Selecting normalization genes for small diagnostic microarrays

BACKGROUND: Normalization of gene expression microarrays carrying thousands of genes is based on assumptions that do not hold for diagnostic microarrays carrying only few genes. Thus, applying standard microarray normalization strategies to diagnostic microarrays causes new normalization problems. RESULTS: In this paper we point out the differences of normalizing large microarrays and small diagnostic microarrays. We suggest to include additional normalization genes on the small diagnostic microarrays and propose two strategies for selecting them from genomewide microarray studies. The first is a data driven univariate selection of normalization genes. The second is multivariate and based on finding a balanced diagnostic signature. Finally, we compare both methods to standard normalization protocols known from large microarrays. CONCLUSION: Not including additional genes for normalization on small microarrays leads to a loss of diagnostic information. Using house keeping genes from the literature for normalization fails to work for certain datasets. While a data driven selection of additional normalization genes works well, the best results were obtained using a balanced signature

Effects of sample size on robustness and prediction accuracy of a prognostic gene signature

<p>Abstract</p> <p>Background</p> <p>Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature.</p> <p>Results</p> <p>A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+) patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures</p> <p>Conclusion</p> <p>Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.</p