The effects of the menstrual cycle on social decision making

The cyclic change in hormonal profiles between the two main phases of the menstrual cycle mediate shifts in mate preference. Males who advertise social dominance are preferred over other men by females in the follicular phase of the cycle. The present study explored assignment of high or low status resources to dominant looking men by females in either phase of the menstrual cycle. Thirteen females who reported that they were free from any kind of hormonal intervention and experienced a 28 day cycle, were invited to participate in a mock job negotiation scenario. Participants were asked to assign either a minimum, low, high or maximum social status job package to a series of male 'employees' that were previously rated to look either dominant or non-dominant. The results showed that during the follicular phase of the cycle participants assigned dominant looking men more high status job resources than the non-dominant looking men. However, during the luteal phase the participants assigned low status resources to the non-dominant looking men. Females are not merely passive observers of male status cues but actively manipulate the environment to assign status. © 2006 Elsevier B.V. All rights reserved.

Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.