Foundation degrees in biomedical science: the student experience

The first cohort of students on a University of Westminster foundation degree completed the course recently. Here, Chrystalla Ferrier, Kelly Brookwell and Paul Quinn employ some reflective practice

A four gene signature of chromosome instability (CIN4) predicts for benefit from taxanes in the NCIC-CTG MA21 clinical trial.

Recent evidence demonstrated CIN4 as a predictive marker of anthracycline benefit in early breast cancer. An analysis of the NCIC CTG MA.21 clinical trial was performed to test the role of existing CIN gene expression signatures as prognostic and predictive markers in the context of taxane based chemotherapy.RNA was extracted from patients in cyclophosphamide, epirubicin and flurouracil (CEF) and epirubicin, cyclophosphamide and paclitaxel (EC/T) arms of the NCIC CTG MA.21 trial and analysed using NanoString technology.After multivariate analysis both high CIN25 and CIN70 score was significantly associated with an increased in RFS (HR 1.76, 95%CI 1.07-2.86, p=0.0018 and HR 1.59, 95%CI 1.12-2.25, p=0.0096 respectively). Patients whose tumours had low CIN4 gene expression scores were associated with an increase in RFS (HR: 0.64, 95% CI 0.39-1.03, p=0.06) when treated with EC/T compared to patients treated with CEF.In conclusion we have demonstrated CIN25 and CIN70 as prognostic markers in breast cancer and that CIN4 is a potential predictive maker of benefit from taxane treatment

Gauge symmetry and W-algebra in higher derivative systems

The problem of gauge symmetry in higher derivative Lagrangian systems is discussed from a Hamiltonian point of view. The number of independent gauge parameters is shown to be in general {\it{less}} than the number of independent primary first class constraints, thereby distinguishing it from conventional first order systems. Different models have been considered as illustrative examples. In particular we show a direct connection between the gauge symmetry and the W-algebra for the rigid relativistic particle.Comment: 1+22 pages, 1 figure, LaTeX, v2; title changed, considerably expanded version with new results, to appear in JHE

Supporting the Occupational Therapy Student in the Production and Dissemination of Systematic Reviews: An Interprofessional Collaboration among Librarians and Occupational Therapy Faculty

Objectives This poster describes the outcomes of a curriculum-based collaboration between librarians and OT faculty (‘collaboration’) to enhance graduate student skills for conducting a systematic review (SR); the collaboration included database instruction, bibliographic management software, and culminated in student presentations to healthcare practitioners for continuing education credit. Three outcome areas are discussed: impact of the collaboration on student satisfaction and perceived competence; characteristics of the included literature; and the dissemination of SR findings to healthcare practitioners. Methods Three librarians participated in the instruction and the institutional repository (Jefferson Digital Commons; JDC) deposits. A total of 132 students over a period of two years (2013-2014) completed the curriculum, engaging with librarians and OT faculty to iteratively build on skills. At the conclusion of their curriculum, the capstone presentations were recorded and made freely available through the JDC. Quantitative data were examined with descriptive statistics in SPSS, and qualitative data were thematically coded by hand: course evaluations, practitioner attendance, bibliographic evaluations of the systematic reviews, and download statistics from the institutional repository. Results Students reported on open-ended course evaluation questions that among the top three concepts learned was ‘how to conduct a replicable and effective search.’ On multiple answer questions 83.6% of students selected the ‘collaborative librarian-faculty lecture’ as among the most helpful lectures offered, and 78.2% selected ‘working with librarian staff and course mentors to develop a search strategy’ as highly rated among course activities. Bibliographic data were extracted from 22 of 28 capstone presentations available for analysis (2013-2014) in the institutional repository, which contained 305 citations from 157 journals. The average of age of included articles was 4.8 years (SD=4.2, Range=0-24). Among the top 10 cited journals were 2 occupational therapy, 5 rehabilitation, and 3 specialty. Overall health care practitioner attendance at student capstones from 2012-2014 was 323. JDC recordings (as of 1/6/2015) had been accessed from 25 different countries, and are located most frequently via Google, JDC, and GoogleScholar. The total number of views was 1,446, and the total number of hours viewed was 163 hours. Conclusions Librarian-faculty collaborations resulted in high student perception of competence to conduct systematic reviews, utilization of a broad variety of peer-reviewed journals, and enhanced dissemination of evidence

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

Role of the employment status and education of mothers in the prevalence of intestinal parasitic infections in Mexican rural schoolchildren

<p><b>Background:</b> Intestinal parasitic infections are a public health problem in developing countries such as Mexico. As a result, two governmental programmes have been implemented: a) "National Deworming Campaign" and b) "Opportunities" aimed at maternal care. However, both programmes are developed separately and their impact is still unknown. We independently investigated whether a variety of socio-economic factors, including maternal education and employment levels, were associated with intestinal parasite infection in rural school children.</p> <p><b>Methods:</b> This cross-sectional study was conducted in 12 rural communities in two Mexican states. The study sites and populations were selected on the basis of the following traits: a) presence of activities by the national administration of albendazole, b) high rates of intestinal parasitism, c) little access to medical examination, and d) a population having less than 2,500 inhabitants. A total of 507 schoolchildren (mean age 8.2 years) were recruited and 1,521 stool samples collected (3 per child). Socio-economic information was obtained by an oral questionnaire. Regression modelling was used to determine the association of socio-economic indicators and intestinal parasitism.</p> <p><b>Results:</b> More than half of the schoolchildren showed poliparasitism (52%) and protozoan infections (65%). The prevalence of helminth infections was higher in children from Oaxaca (53%) than in those from Sinaloa (33%) (p < 0.0001). Giardia duodenalis and Hymenolepis nana showed a high prevalence in both states. Ascaris lumbricoides, Trichuris trichiura and Entamoeba hystolitica/dispar showed low prevalence. Children from lower-income families and with unemployed and less educated mothers showed higher risk of intestinal parasitism (odds ratio (OR) 6.0, 95% confidence interval (CI) 1.6–22.6; OR 4.5, 95% CI 2.5–8.2; OR 3.3, 95% CI 1.5–7.4 respectively). Defecation in open areas was also a high risk factor for infection (OR 2.4, 95% CI 2.0–3.0).</p> <p><b>Conclusion:</b> Intestinal parasitism remains an important public health problem in Sinaloa (north-western Mexico) and Oaxaca (south-eastern Mexico). Lower income, defecation in open areas, employment status and a lower education level of mothers were the significant factors related to these infections. We conclude that mothers should be involved in health initiatives to control intestinal parasitism in Mexico.</p&gt

Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes

SMC (Sequential Monte Carlo) is a class of Monte Carlo algorithms for filtering and related sequential problems. Gerber and Chopin (2015) introduced SQMC (Sequential quasi-Monte Carlo), a QMC version of SMC. This paper has two objectives: (a) to introduce Sequential Monte Carlo to the QMC community, whose members are usually less familiar with state-space models and particle filtering; (b) to extend SQMC to the filtering of continuous-time state-space models, where the latent process is a diffusion. A recurring point in the paper will be the notion of dimension reduction, that is how to implement SQMC in such a way that it provides good performance despite the high dimension of the problem.Comment: To be published in the proceedings of MCMQMC 201

The Role of Relationships in the Professional Formation of Physicians

BACKGROUND: Studies of the professional development of physicians highlight the important effect that the learning environment, or \ hidden curriculum,\ has in shaping student attitudes, behaviors, and values. We conducted this study to better understand the role that relationships have in mediating these effects of the hidden curriculum. [See PDF for complete abstract