Creating Awareness of Sleep-Wake Hours by Gamification

This book constitutes the refereed proceedings of the 11th International Conference on Persuasive Technology, PERSUASIVE 2016, held in Salzburg, Austria, in April 2016

Exploring the lived experience of Long Covid in black and minority ethnic groups in the UK: Protocol for qualitative interviews and art-based methods.

Some people experience prolonged symptoms following an acute COVID-19 infection including fatigue, chest pain and breathlessness, headache and cognitive impairment. When symptoms persist for over 12 weeks following the initial infection, and are not explained by an alternative diagnosis, the term post-COVID-19 syndrome is used, or the patient-defined term of Long Covid. Understanding the lived experiences of Long Covid is crucial to supporting its management. However, research on patient experiences of Long Covid is currently not ethnically diverse enough. The study aim is to explore the lived experience of Long Covid, using qualitative interviews and art-based methods, among people from ethnically diverse backgrounds (in the UK), to better understand wider systems of support and healthcare support needs. Co-created artwork will be used to build on the interview findings. A purposive sampling strategy will be used to gain diverse experiences of Long Covid, sampling by demographics, geographic locations and experiences of Long Covid. Individuals (aged >18 years) from Black and ethnic minority backgrounds, who self-report Long Covid symptoms, will be invited to take part in a semi-structured interview. Interviews will be analysed thematically. A sub-sample of participants will be invited to co-create visual artwork to further explore shared narratives of Long Covid, enhance storytelling and increase understanding about the condition. A patient advisory group, representing diversity in ethnicity and experiences of Long Covid, will inform all research stages. Stakeholder workshops with healthcare professionals and persons, systems or networks important to people's management of Long Covid, will advise on the integration of findings to inform management of Long Covid. The study will use patient narratives from people from diverse ethnic backgrounds, to raise awareness of Long Covid and help inform management of Long Covid and how wider social systems and networks may inform better healthcare service access and experiences

Estimating Receptive Fields from Responses to Natural Stimuli with Asymmetric Intensity Distributions

The reasons for using natural stimuli to study sensory function are quickly mounting, as recent studies have revealed important differences in neural responses to natural and artificial stimuli. However, natural stimuli typically contain strong correlations and are spherically asymmetric (i.e. stimulus intensities are not symmetrically distributed around the mean), and these statistical complexities can bias receptive field (RF) estimates when standard techniques such as spike-triggered averaging or reverse correlation are used. While a number of approaches have been developed to explicitly correct the bias due to stimulus correlations, there is no complementary technique to correct the bias due to stimulus asymmetries. Here, we develop a method for RF estimation that corrects reverse correlation RF estimates for the spherical asymmetries present in natural stimuli. Using simulated neural responses, we demonstrate how stimulus asymmetries can bias reverse-correlation RF estimates (even for uncorrelated stimuli) and illustrate how this bias can be removed by explicit correction. We demonstrate the utility of the asymmetry correction method under experimental conditions by estimating RFs from the responses of retinal ganglion cells to natural stimuli and using these RFs to predict responses to novel stimuli

Nitrosothiols in the immune system: Signaling and protection

Antioxidants and Redox Signaling 18.3 (2013): 288-308Significance: In the immune system, nitric oxide (NO) has been mainly associated with antibacterial defenses exerted through oxidative, nitrosative, and nitrative stress and signal transduction through cyclic GMP-dependent mechanisms. However, S-nitrosylation is emerging as a post-translational modification (PTM) involved in NO-mediated cell signaling. Recent Advances: Precise roles for S-nitrosylation in signaling pathways have been described both for innate and adaptive immunity. Denitrosylation may protect macrophages from their own S-nitrosylation, while maintaining nitrosative stress compartmentalized in the phagosomes. Nitrosothiols have also been shown to be beneficial in experimental models of autoimmune diseases, mainly through their role in modulating T-cell differentiation and function. Critical Issues: Relationship between S-nitrosylation, other thiol redox PTMs, and other NO-signaling pathways has not been always taken into account, particularly in the context of immune responses. Methods for assaying S-nitrosylation in individual proteins and proteomic approaches to study the S-nitrosoproteome are constantly being improved, which helps to move this field forward. Future Directions: Integrated studies of signaling pathways in the immune system should consider whether S-nitrosylation/denitrosylation processes are among the PTMs influencing the activity of key signaling and adaptor proteins. Studies in pathophysiological scenarios will also be of interest to put these mechanisms into broader contexts. Interventions modulating nitrosothiol levels in autoimmune disease could be investigated with a view to developing new therapiesFinanced by the Spanish Government grants CSD2007-00020 (RosasNet, Consolider-Ingenio 2010 programme), CP07/00143 (Miguel Servet programme), and PS09/00101; and PI10/0213

Financial considerations in the conduct of multi-centre randomised controlled trials: evidence from a qualitative study.

National Coordinating Centre for Research Methodology; Medical Research Council, UK Department of Health; Chief Scientist OfficeNot peer reviewedPublisher PD

Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

<p>Abstract</p> <p>Background</p> <p>For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or <it>in-silico </it>deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.</p> <p>Results</p> <p>Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.</p> <p>Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.</p> <p>Conclusions</p> <p>The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.</p

Compensatory Evolution of Gene Regulation in Response to Stress by Escherichia coli Lacking RpoS

The RpoS sigma factor protein of Escherichia coli RNA polymerase is the master transcriptional regulator of physiological responses to a variety of stresses. This stress response comes at the expense of scavenging for scarce resources, causing a trade-off between stress tolerance and nutrient acquisition. This trade-off favors non-functional rpoS alleles in nutrient-poor environments. We used experimental evolution to explore how natural selection modifies the regulatory network of strains lacking RpoS when they evolve in an osmotically stressful environment. We found that strains lacking RpoS adapt less variably, in terms of both fitness increase and changes in patterns of transcription, than strains with functional RpoS. This phenotypic uniformity was caused by the same adaptive mutation in every independent population: the insertion of IS10 into the promoter of the otsBA operon. OtsA and OtsB are required to synthesize the osmoprotectant trehalose, and transcription of otsBA requires RpoS in the wild-type genetic background. The evolved IS10 insertion rewires expression of otsBA from RpoS-dependent to RpoS-independent, allowing for partial restoration of wild-type response to osmotic stress. Our results show that the regulatory networks of bacteria can evolve new structures in ways that are both rapid and repeatable

Satellite remote sensing data can be used to model marine microbial metabolite turnover

Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km2) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes’ predicted relative abundance was highly correlated (Pearson Correlation 0.72, P-value <10−6) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO2 were significantly correlated with observed surface CO2 fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ~3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology