Polymerization-dependent activation of porcine γδ T-cells by proanthocyanidins

Plant-derived proanthocyanidins (PAC) have been promoted as a natural method of improving health and immune function in livestock. It has previously been shown that PAC are effective agonists for activating ruminant γδ T-cells in vitro, however effects on other livestock species are not yet clear. Moreover, the fine structural characteristics of the PAC which contribute to this stimulatory effect have not been elucidated. Here, we demonstrate activation of porcine γδ T-cells by PAC via up-regulation of CD25 (IL-2Rα) and show that 1) activation is dependent on degree of polymerization (DP), with PAC fractions containing polymers with mean DP >6 significantly more effective than fractions with mean DP <6, whilst flavan-3-ol monomers (the constituent monomeric units of PAC) did not induce CD25 expression and 2) both procyanidin and prodelphinidin-type PAC are effective agonists. Furthermore, we show that this effect of PAC is restricted to the γδ T-cell population within porcine peripheral mononuclear cells as significant CD25 up-regulation was not observed in non γδ T-cells, and no activation (via CD80/86 up-regulation) was evident in monocytes. Our results show that dietary PAC may contribute to enhancement of innate immunity in swine via activation of γδ T-cells.

Effectiveness of mobile apps in teaching field-based identification skills

It has been suggested that few students graduate with the skills required for many ecological careers, as field-based learning is said to be in decline in academic institutions. Here, we asked if mobile technology could improve field-based learning, using ability to identify birds as the study metric. We divided a class of ninety-one undergraduate students into two groups for field-based sessions where they were taught bird identification skills. The first group has access to a traditional identification book and the second group were provided with an identification app. We found no difference between the groups in the ability of students to identify birds after three field sessions. Furthermore, we found that students using the traditional book were significantly more likely to identify novel species. Therefore, we find no evidence that mobile technology improved students’ ability to retain what they experienced in the field; indeed, there is evidence that traditional field guides were more useful to students as they attempted to identify new species. Nevertheless, students felt positively about using their own smartphone devices for learning, highlighting that while apps did not lead to an improvement in bird identification ability, they gave greater accessibility to relevant information outside allocated teaching times.

Essays on robust portfolio selection and pension finance

This thesis examines three different, but related problems in the broad area of portfolio management for long-term institutional investors, and focuses mainly on the case of pension funds. The first idea (Chapter 3) is the application of a novel numerical technique – robust optimization – to a real-world pension scheme (the Universities Superannuation Scheme, USS) for first time. The corresponding empirical results are supported by many robustness checks and several benchmarks such as the Bayes-Stein and Black-Litterman models that are also applied for first time in a pension ALM framework, the Sharpe and Tint model and the actual USS asset allocations. The second idea presented in Chapter 4 is the investigation of whether the selection of the portfolio construction strategy matters in the SRI industry, an issue of great importance for long term investors. This study applies a variety of optimal and naïve portfolio diversification techniques to the same SRI-screened universe, and gives some answers to the question of which portfolio strategies tend to create superior SRI portfolios. Finally, the third idea (Chapter 5) compares the performance of a real-world pension scheme (USS) before and after the recent major changes in the pension rules under different dynamic asset allocation strategies and the fixed-mix portfolio approach and quantifies the redistributive effects between various stakeholders. Although this study deals with a specific pension scheme, the methodology can be applied by other major pension schemes in countries such as the UK and USA that have changed their rules.

In silico identification and characterization of protein-ligand binding sites

Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation.

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort

Background Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers (“biomarkers”) of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. Methods CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10–20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2–10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. Discussion From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants) has been evaluated at baseline. The clinical characteristics of this cohort are similar to other studies of MDD. Recruitment at all sites is ongoing to a target sample of 290 participants. CAN-BIND will identify biomarkers of treatment response in MDD through extensive clinical, molecular, and imaging assessments, in order to improve treatment practice and clinical outcomes. It will also create an innovative, robust platform and database for future research.