Perceptions and discourses relating to genetic testing : interviews with people with Down syndrome

Background: The perceptions of individuals with Down syndrome are conspicuously absent in discussions about the use of prenatal testing. Method: Eight individuals with Down syndrome were interviewed about their views and experience of the topic of prenatal testing. Results: Interpretative Phenomenological Analysis revealed two major themes with sub themes: 1) A devalued condition and a valued life and 2) A question of ‘want?’ Foucauldian Discourse Analysis highlighted two main discursive practices: 1) Social deviance and 2) Tragedy and catastrophe of the birth of a baby with Down syndrome. Conclusions: The findings suggest that individuals with intellectual disabilities can make a valuable contribution to discussions surrounding the use of prenatal testing. Implications for clinical practice include the use of information about Down syndrome given to prospective parents, and the possible psychological impact of prenatal testing practices on individuals with Down syndrome

Effects of fatty acid oxidation products (‘Green odour’) on rumen bacterial populations and lipid metabolism in vitro

This study investigated the effects of green odor fatty acid oxidation products (FAOP) from cut grass on lipid metabolism and microbial ecology using in vitro incubations of rumen microorganisms. These compounds have antimicrobial roles in plant defense, and we hypothesized that they may influence rumen lipid metabolism. Further, they may partially explain the higher levels of conjugated linoleic acid cis-9, trans-11 in milk from cows grazing pasture. The first of 2 batch culture experiments screened 6 FAOP (1 hydroperoxide, 3 aldehydes, 1 ketone, and 1 alcohol) for effects on lipid profile, and in particular C18 polyunsaturated fatty acid biohydrogenation. Experiment 2 used the most potent FAOP to determine effects of varying concentrations and identify relationships with effects on microbial ecology. Batch cultures contained anaerobic buffer, rumen liquor, and FAOP to a final concentration of 100 μM for experiment 1. Triplicates for each compound and controls (water addition) were incubated at 39°C for 6 h. The hydroperoxide (1,2-dimethylethyl hydroperoxide, 1,2-DMEH) and the long chain aldehyde (trans-2 decenal) had the largest effects on lipid metabolism with significant increases in C18:0 and C18:1trans and reductions in C12:0, C14:0, C16:0, C18:1cis, C18:2n-6, C18:3n-3, C20:0 and total branch and odd chain fatty acids compared with the control. This was associated with significantly higher biohydrogenation of C18 polyunsaturated fatty acid. In experiment 2, 1,2-DMEH was incubated at 50, 100, and 200 μM for 2, 6, and 24 h. Increasing 1,2-DMEH concentration resulted in a significant linear increase in C18:1trans-10, trans-11, conjugated linoleic acid, and C18:0 and a linear decrease in C18:2n-6 and C18:3n-3, although the scale of this response declined with time. Microbial profiling techniques showed that 1,2-DMEH at concentrations of 100 and 200 μM changed the microbial community from as early as 2 h after addition, though microbial biomass remained similar. These preliminary studies have shown that FAOP can alter fatty acid biohydrogenation in the rumen. This change was associated with changes in the microbial population that were detected through DNA and branched- and odd-chain fatty acid profiling approaches

Teleworking practice in small and medium-sized firms: Management style and worker autonomy

In an empirical study of teleworking practices amongst small and medium-sized enterprises (SMEs) in West London, organisational factors such as management attitudes, worker autonomy and employment flexibility were found to be more critical than technological provision in facilitating successful implementation. Consequently, we argue that telework in most SMEs appears as a marginal activity performed mainly by managers and specialist mobile workers

What price flexibility? The casualisation of women's employment

FdR – Publicaties niet-programma gebonde

Unsupervised machine learning of integrated health and social care data from the Macmillan Improving the Cancer Journey service in Glasgow

Background: Improving the Cancer Journey (ICJ) was launched in 2014 by Glasgow City Council and Macmillan Cancer Support. As part of routine service, data is collected on ICJ users including demographic and health information, results from holistic needs assessments and quality of life scores as measured by EQ-5D health status. There is also data on the number and type of referrals made and feedback from users on the overall service. By applying artificial intelligence and interactive visualization technologies to this data, we seek to improve service provision and optimize resource allocation.Method: An unsupervised machine-learning algorithm was deployed to cluster the data. The classical k-means algorithm was extended with the k-modes technique for categorical data, and the gap heuristic automatically identified the number of clusters. The resulting clusters are used to summarize complex data sets and produce three-dimensional visualizations of the data landscape. Furthermore, the traits of new ICJ clients are predicted by approximately matching their details to the nearest existing cluster center.Results: Cross-validation showed the model’s effectiveness over a wide range of traits. For example, the model can predict marital status, employment status and housing type with an accuracy between 2.4 to 4.8 times greater than random selection. One of the most interesting preliminary findings is that area deprivation (measured through Scottish Index of Multiple Deprivation-SIMD) is a better predictor of an ICJ client’s needs than primary diagnosis (cancer type).Conclusion: A key strength of this system is its ability to rapidly ingest new data on its own and derive new predictions from those data. This means the model can guide service provision by forecasting demand based on actual or hypothesized data. The aim is to provide intelligent person-centered recommendations. The machine-learning model described here is part of a prototype software tool currently under development for use by the cancer support community.Disclosure: Funded by Macmillan Cancer Support</p

Beehives possess their own distinct microbiomes

Abstract Background Honeybees use plant material to manufacture their own food. These insect pollinators visit flowers repeatedly to collect nectar and pollen, which are shared with other hive bees to produce honey and beebread. While producing these products, beehives accumulate a considerable number of microbes, including bacteria that derive from plants and different parts of the honeybees’ body. Whether bacteria form similar communities amongst beehives, even if located in close proximity, is an ecologically important question that has been addressed in this study. Specific ecological factors such as the surrounding environment and the beekeeping methods used can shape the microbiome of the beehive as a whole, and eventually influence the health of the honeybees and their ecosystem. Results We conducted 16S rRNA meta-taxonomic analysis on honey and beebread samples that were collected from 15 apiaries in the southeast of England to quantify the bacteria associated with different beehives. We observed that honeybee products carry a significant variety of bacterial groups that comprise bee commensals, environmental bacteria and symbionts and pathogens of plants and animals. Remarkably, this bacterial diversity differs not only amongst apiaries, but also between the beehives of the same apiary. In particular, the levels of the bee commensals varied significantly, and their fluctuations correlated with the presence of different environmental bacteria and various apiculture practices. Conclusions Our results show that every hive possesses their own distinct microbiome and that this very defined fingerprint is affected by multiple factors such as the nectar and pollen gathered from local plants, the management of the apiaries and the bacterial communities living around the beehives. Based on our findings, we suggest that the microbiome of beehives could be used as a valuable biosensor informing of the health of the honeybees and their surrounding environment