Understanding The Use Of Social Technologies During A Life Transition: Men's Experience with Fertility Problems

Transition to parenthood is a common experience but one that can be particularly challenging for people who have difficulty in conceiving. Men are reportedly likely to feel isolated and stigmatised when they experience fertility problems and are more likely to turn to sources of social technology for support than approach healthcare services. We share our findings from two studies with data from two different sources; online forum comments and semi-structured qualitative interviews to explore how and why men use technology when they experience fertility problems. We report our findings in relation to the proposed workshop themes

Sigmoid Neural Transfer Function Realised by Percolation

An experiment using the phenomenon of percolation has been conducted to demonstrate the implementation of neural functionality (summing and sigmoid transfer). A simple analog approximation to digital percolation is implemented. The device consists of a piece of amorphous silicon with stochastic bit-stream optical inputs, in which a current percolating from one end to the other defines the neuron output, also in the form of a stochastic bit stream. Preliminary experimental results are presented

General practitioners' knowledge, attitudes and views of providing preconception care: a qualitative investigation

Background: Preconception health and care aims to reduce parental risk factors before pregnancy through health promotion and intervention. Little is known about the preconception interventions that general practitioners (GPs) provide. The aim of this study was to examine GPs’ knowledge, attitudes, and views towards preconception health and care in the general practice setting. Methods: As part of a large mixed-methods study to explore preconception care in England, we surveyed 1,173 women attending maternity units and GP services in London and interviewed women and health professionals. Seven GPs were interviewed, and the framework analysis method was used to analyse the data. Findings: Seven themes emerged from the data: Knowledge of preconception guidelines; Content of preconception advice; Who should deliver preconception care?; Targeting provision of preconception care; Preconception health for men; Barriers to providing preconception care; and Ways of improving preconception care. A lack of knowledge and demand for preconception care was found, and although reaching women before they are pregnant was seen as important it was not a responsibility that could be adequately met by GPs. Specialist preconception services were not provided within GP surgeries, and care was mainly targeted at women with medical conditions. GPs described diverse patient groups with very different health needs. Conclusion: Implementation of preconception policy and guidelines is required to engage women and men and to develop proactive delivery of care with the potential to improve pregnancy and neonatal outcomes. The role of education and of nurses in improving preconception health was acknowledged but remains under-developed

"I feel like only half a man"

Infertility can place a significant burden on couples and individuals when trying to conceive. Approximately 20-30% of all cases of infertility are due to male-related factors. Whatever the cause of difficulty in conceiving, little is known about how men find support when dealing with fertility issues, or when or how online resources are being used. This paper reports on a qualitative study of anonymous online posts (N = 603) from forums related to fertility that are used by men. We analysed this data using thematic analysis to understand how men are using online forums as a resource when experiencing fertility issues. We found that online forums play a valued role in facilitating connections between men experiencing an often stigmatised condition. These forums offer men accessible and private spaces which allow for more open discussion, helping them to make sense of their situation. We discuss our findings in relation to Genuis and Bronstein’s model of finding a "new normal" and present our elaborated model of finding a "new normal" in the context of experiencing fertility problems

Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria

Biomarker discovery aims to find small subsets of relevant variables in ‘omics data that correlate with the clinical syndromes of interest. Despite the fact that clinical phenotypes are usually characterized by a complex set of clinical parameters, current computational approaches assume univariate targets, e.g. diagnostic classes, against which associations are sought for. We propose an approach based on asymmetrical sparse canonical correlation analysis (SCCA) that finds multivariate correlations between the ‘omics measurements and the complex clinical phenotypes. We correlated plasma proteomics data to multivariate overlapping complex clinical phenotypes from tuberculosis and malaria datasets. We discovered relevant ‘omic biomarkers that have a high correlation to profiles of clinical measurements and are remarkably sparse, containing 1.5–3% of all ‘omic variables. We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. Our approach finds proteomic-biomarkers that correlate with complex combinations of clinical-biomarkers. Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. Our approach makes it feasible to use omics' data to build accurate diagnostic algorithms that can be deployed to community health centres lacking the expensive ‘omics measurement capabilities

Evaluation of the Psychometric Properties of a Version of the London Measure of Unplanned Pregnancy for Women’s Partners

BackgroundThe role of women’s partners in pregnancy planning has gained importance with the development of preconception care. The measurement of pregnancy planning/intention has also changed in the last two decades with the development of psychometric measures such as the London Measure of Unplanned Pregnancy (LMUP). This analysis aimed to evaluate the psychometric properties of a version of the LMUP for women’s partners in the UK.&#x0D; MethodsThe LMUP items, adapted for completion by partners, were piloted and included in a survey of (mainly male) partners in three antenatal clinics in London, UK, as part of a study of pre-pregnancy health and care. The psychometric properties of the partner LMUP were assessed according to the principles of Classical Test Theory.&#x0D; ResultsThere were 575 partners of pregnant women in the sample, 573 (99.7%) being men. There were high comple-tion rates for all the LMUP items. The distribution of LMUP scores ranged from 1–12, with a negative skew (biased towards planned/intended pregnancies). In terms of reliability (internal consistency), Cronbach’s alpha was 0.69, item-rest correlations were &gt;0.2 for five items, and all inter-item correlations were positive. In terms of construct validity, principal components analysis showed that measurement was unidimensional, confirmatory factor analysis showed good model fit, and the convergent validity hypothesis of non-perfect, moderate-to-good agreement between couples’ LMUP scores was met.&#x0D; ConclusionsThe partner LMUP performed well in terms of reliability and validity according to internationally-accepted criteria for the performance of psychometric measures and can be used in future research on men and couples. However, we recommend further research relating to the concept of pregnancy planning/inten-tion among partners of all gender identities to understand whether additional content would enhance the measurement of the construct. In particular, we recommend further conceptual exploration with men who have experienced unplanned pregnancies.&#x0D;  </jats:p

Distributed variance regularized Multitask Learning

Past research on Multitask Learning (MTL) has focused mainly on devising adequate regularizers and less on their scalability. In this paper, we present a method to scale up MTL methods which penalize the variance of the task weight vectors. The method builds upon the alternating direction method of multipliers to decouple the variance regularizer. It can be efficiently implemented by a distributed algorithm, in which the tasks are first independently solved and subsequently corrected to pool information from other tasks. We show that the method works well in practice and convergences in few distributed iterations. Furthermore, we empirically observe that the number of iterations is nearly independent of the number of tasks, yielding a computational gain of O(T) over standard solvers. We also present experiments on a large URL classification dataset, which is challenging both in terms of volume of data points and dimensionality. Our results confirm that MTL can obtain superior performance over either learning a common model or independent task learning

A framework for space-efficient string kernels

String kernels are typically used to compare genome-scale sequences whose length makes alignment impractical, yet their computation is based on data structures that are either space-inefficient, or incur large slowdowns. We show that a number of exact string kernels, like the $k$-mer kernel, the substrings kernels, a number of length-weighted kernels, the minimal absent words kernel, and kernels with Markovian corrections, can all be computed in $O(nd)$ time and in $o(n)$ bits of space in addition to the input, using just a $\mathtt{rangeDistinct}$ data structure on the Burrows-Wheeler transform of the input strings, which takes $O(d)$ time per element in its output. The same bounds hold for a number of measures of compositional complexity based on multiple value of $k$, like the $k$-mer profile and the $k$-th order empirical entropy, and for calibrating the value of $k$ using the data

Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

In recent work we have developed a novel variational inference method for partially observed systems governed by stochastic differential equations. In this paper we provide a comparison of the Variational Gaussian Process Smoother with an exact solution computed using a hybrid Monte Carlo approach to path sampling, applied to a stochastic double well potential model. It is demonstrated that the variational smoother provides us a very accurate estimate of mean path while marginal variance is slightly underestimated. We conclude with some remarks as to the advantages and disadvantages of the variational smoother