An Investigation of Counterfactual Thinking in Individuals Diagnoses with Diabetes

Diabetes affects both the physical and emotional well-being of over 29 million Americans. Thus, it is important to investigate the psychological factors that can influence appropriate diabetes self-care. The present study investigates whether counterfactual thoughts might be related to how an individual copes with diabetes. The study utilizes a mixed-methods approach consisting of a quantitative survey assessing psychosocial factors, and a qualitative interview with the participant. The interview includes questions about the participant’s thoughts and feelings with their experience of diabetes, noting when participants spontaneously generate counterfactual thoughts about how things might be different if they hadn’t been diagnosed with diabetes. Currently, 31 people have completed the protocol (11 males and 20 females). These preliminary results suggest that an increase in counterfactual thinking is marginally associated with higher levels of guilt (r(29) = .326, p = .085). Further, these higher levels of guilt are strongly associated with the maladaptive coping mechanisms of self-blame (r(29) = .671, p \u3c .001) and behavioral disengagement (r(29) = .541, p = .002). Notably, high levels of self-blame and behavioral disengagement were marginally associated with lower levels of diabetes self-efficacy (r(29) = -.303, p = .104, and r(29) = -.331, p =.074, respectively). Appropriate diabetes self-care is essential to the prevention of serious complications like blindness and amputation. This preliminary evidence suggests that certain types of counterfactual thoughts may undermine appropriate diabetes self-care. Further research on counterfactual thinking may assist in the design of educational initiatives to encourage successful diabetes self-care

Phylogenetic Resolution and Quantifying the Phylogenetic Diversity and Dispersion of Communities

Conservation biologists and community ecologists have increasingly begun to quantify the phylogenetic diversity and phylogenetic dispersion in species assemblages. In some instances, the phylogenetic trees used for such analyses are fully bifurcating, but in many cases the phylogenies being used contain unresolved nodes (i.e. polytomies). The lack of phylogenetic resolution in such studies, while certainly not preferred, is likely to continue particularly for those analyzing diverse communities and datasets with hundreds to thousands of taxa. Thus it is imperative that we quantify potential biases and losses of statistical power in studies that use phylogenetic trees that are not completely resolved. The present study is designed to meet both of these goals by quantifying the phylogenetic diversity and dispersion of simulated communities using resolved and gradually ‘unresolved’ phylogenies. The results show that: (i) measures of community phylogenetic diversity and dispersion are generally more sensitive to loss of resolution basally in the phylogeny and less sensitive to loss of resolution terminally; and (ii) the loss of phylogenetic resolution generally causes false negative results rather than false positives

Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets

Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation

An Integrated Marketing Communications Campaign For Globe Prepaid Project Virtual Hangouts

The proposed campaign aims to raise awareness for Virtual Hangouts as an interactive platform that offers countless exclusive passion-based experiences. The campaign also intends to drive participation for Virtual Hangouts events through its pillars: GoJAM, GoKOREAN, GoHUSTLE, GoESPORTS, and GoWATCH. With this, the proposed target market is Filipino male and female belonging to the Generation Z, aged 18 to 22 years old, under Social Economic Class (SEC) Lower B and Upper C, residing in Metro Manila. The campaign centers on the tagline “Tara G!” for this encapsulates the spirit of these purposive creators, willing to do something fun or unusual, even through online means

A systematic review investigating interventions that can help reduce consumption of sugar-sweetened beverages in children leading to changes in body fatness

Background Both the prevalence of childhood obesity and the consumption of sugar-sweetened beverages (SSBs) have increased globally. The present review describes interventions that reduce the consumption of SSBs in children and determines whether this leads to subsequent changes in body fatness. Methods Three databases were searched from 2000 to August 2013. Only intervention control trials, ≥6 months in duration, which aimed to reduce the consumption of SSBs in >100 children aged 2–18 years, and reporting changes in body fatness, were included. The quality of selected papers was assessed. Results Eight studies met inclusion criteria. Six interventions achieved significant (P < 0.05) reductions in SSB intake, although this was not always sustained. In the two interventions providing replacement drinks, significant differences in body mass index (12- or 18-month follow-up) were reported (P = 0.001 and 0.045). The risk of being overweight/obesity was reduced (P < 0.05) in three of the five education programmes but in one programme only for girls who were overweight at baseline and in one programme only for pupils perceived to be at greater risk at baseline. In the one study that included both provision of water and education, the risk of being overweight was reduced by 31% (P = 0.04) in the intervention group. Conclusions The evidence suggests that school-based education programmes focusing on reducing SSB consumption, but including follow-up modules, offer opportunities for implementing effective, sustainable interventions. Peer support and changing the school environment (e.g. providing water or replacement drinks) to support educational programmes could improve their effectiveness. Home delivery of more suitable drinks has a big impact on reducing SSB consumption, with associated reductions in body weight

Identification of organophosphorus simulants for the development of next-generation detection technologies

Organophosphorus (OP) chemical warfare agents (CWAs) represent an ongoing threat but the understandable widespread prohibition of their use places limitations on the development of technologies to counter the effects of any OP CWA release. Herein, we describe new, accessible methods for the identification of appropriate molecular simulants to mimic the hydrogen bond accepting capacity of the P[double bond, length as m-dash]O moiety, common to every member of this class of CWAs. Using the predictive methodologies developed herein, we have identified OP CWA hydrogen bond acceptor simulants for soman and sarin. It is hoped that the effective use of these physical property specific simulants will aid future countermeasure developments

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy