An observational study of patient characteristics associated with the mode of admission to acute stroke services in North East, England

Objective Effective provision of urgent stroke care relies upon admission to hospital by emergency ambulance and may involve pre-hospital redirection. The proportion and characteristics of patients who do not arrive by emergency ambulance and their impact on service efficiency is unclear. To assist in the planning of regional stroke services we examined the volume, characteristics and prognosis of patients according to the mode of presentation to local services. Study design and setting A prospective regional database of consecutive acute stroke admissions was conducted in North East, England between 01/09/10-30/09/11. Case ascertainment and transport mode were checked against hospital coding and ambulance dispatch databases. Results Twelve acute stroke units contributed data for a mean of 10.7 months. 2792/3131 (89%) patients received a diagnosis of stroke within 24 hours of admission: 2002 arrivals by emergency ambulance; 538 by private transport or non-emergency ambulance; 252 unknown mode. Emergency ambulance patients were older (76 vs 69 years), more likely to be from institutional care (10% vs 1%) and experiencing total anterior circulation symptoms (27% vs 6%). Thrombolysis treatment was commoner following emergency admission (11% vs 4%). However patients attending without emergency ambulance had lower inpatient mortality (2% vs 18%), a lower rate of institutionalisation (1% vs 6%) and less need for daily carers (7% vs 16%). 149/155 (96%) of highly dependent patients were admitted by emergency ambulance, but none received thrombolysis. Conclusion Presentations of new stroke without emergency ambulance involvement were not unusual but were associated with a better outcome due to younger age, milder neurological impairment and lower levels of pre-stroke dependency. Most patients with a high level of pre-stroke dependency arrived by emergency ambulance but did not receive thrombolysis. It is important to be aware of easily identifiable demographic groups that differ in their potential to gain from different service configurations

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

Impact of disaster-related mortality on gross domestic product in the WHO African Region

BACKGROUND: Disaster-related mortality is a growing public health concern in the African Region. These deaths are hypothesized to have a significantly negative effect on per capita gross domestic product (GDP). The objective of this study was to estimate the loss in GDP attributable to natural and technological disaster-related mortality in the WHO African Region. METHODS: The impact of disaster-related mortality on GDP was estimated using double-log econometric model and cross-sectional data on various Member States in the WHO African Region. The analysis was based on 45 of the 46 countries in the Region. The data was obtained from various UNDP and World Bank publications. RESULTS: The coefficients for capital (K), educational enrolment (EN), life expectancy (LE) and exports (X) had a positive sign; while imports (M) and disaster mortality (DS) were found to impact negatively on GDP. The above-mentioned explanatory variables were found to have a statistically significant effect on GDP at 5% level in a t-distribution test. Disaster mortality of a single person was found to reduce GDP by US$0.01828. CONCLUSIONS: We have demonstrated that disaster-related mortality has a significant negative effect on GDP. Thus, as policy-makers strive to increase GDP through capital investment, export promotion and increased educational enrolment, they should always keep in mind that investments made in the strengthening of national capacity to mitigate the effects of national disasters expeditiously and effectively will yield significant economic returns

Formation of regulatory modules by local sequence duplication

Turnover of regulatory sequence and function is an important part of molecular evolution. But what are the modes of sequence evolution leading to rapid formation and loss of regulatory sites? Here, we show that a large fraction of neighboring transcription factor binding sites in the fly genome have formed from a common sequence origin by local duplications. This mode of evolution is found to produce regulatory information: duplications can seed new sites in the neighborhood of existing sites. Duplicate seeds evolve subsequently by point mutations, often towards binding a different factor than their ancestral neighbor sites. These results are based on a statistical analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome, and a comparison set of intergenic regulatory sequence in Saccharomyces cerevisiae. In fly regulatory modules, pairs of binding sites show significantly enhanced sequence similarity up to distances of about 50 bp. We analyze these data in terms of an evolutionary model with two distinct modes of site formation: (i) evolution from independent sequence origin and (ii) divergent evolution following duplication of a common ancestor sequence. Our results suggest that pervasive formation of binding sites by local sequence duplications distinguishes the complex regulatory architecture of higher eukaryotes from the simpler architecture of unicellular organisms

Knowledge-based analysis of microarrays for the discovery of transcriptional regulation relationships

<p>Abstract</p> <p>Background</p> <p>The large amount of high-throughput genomic data has facilitated the discovery of the regulatory relationships between transcription factors and their target genes. While early methods for discovery of transcriptional regulation relationships from microarray data often focused on the high-throughput experimental data alone, more recent approaches have explored the integration of external knowledge bases of gene interactions.</p> <p>Results</p> <p>In this work, we develop an algorithm that provides improved performance in the prediction of transcriptional regulatory relationships by supplementing the analysis of microarray data with a new method of integrating information from an existing knowledge base. Using a well-known dataset of yeast microarrays and the Yeast Proteome Database, a comprehensive collection of known information of yeast genes, we show that knowledge-based predictions demonstrate better sensitivity and specificity in inferring new transcriptional interactions than predictions from microarray data alone. We also show that comprehensive, direct and high-quality knowledge bases provide better prediction performance. Comparison of our results with ChIP-chip data and growth fitness data suggests that our predicted genome-wide regulatory pairs in yeast are reasonable candidates for follow-up biological verification.</p> <p>Conclusion</p> <p>High quality, comprehensive, and direct knowledge bases, when combined with appropriate bioinformatic algorithms, can significantly improve the discovery of gene regulatory relationships from high throughput gene expression data.</p

A Buoyancy-Based Screen of Drosophila Larvae for Fat-Storage Mutants Reveals a Role for Sir2 in Coupling Fat Storage to Nutrient Availability

Obesity has a strong genetic component, but few of the genes that predispose to obesity are known. Genetic screens in invertebrates have the potential to identify genes and pathways that regulate the levels of stored fat, many of which are likely to be conserved in humans. To facilitate such screens, we have developed a simple buoyancy-based screening method for identifying mutant Drosophila larvae with increased levels of stored fat. Using this approach, we have identified 66 genes that when mutated increase organismal fat levels. Among these was a sirtuin family member, Sir2. Sirtuins regulate the storage and metabolism of carbohydrates and lipids by deacetylating key regulatory proteins. However, since mammalian sirtuins function in many tissues in different ways, it has been difficult to define their role in energy homeostasis accurately under normal feeding conditions. We show that knockdown of Sir2 in the larval fat body results in increased fat levels. Moreover, using genetic mosaics, we demonstrate that Sir2 restricts fat accumulation in individual cells of the fat body in a cell-autonomous manner. Consistent with this function, changes in the expression of metabolic enzymes in Sir2 mutants point to a shift away from catabolism. Surprisingly, although Sir2 is typically upregulated under conditions of starvation, Sir2 mutant larvae survive better than wild type under conditions of amino-acid starvation as long as sugars are provided. Our findings point to a Sir2-mediated pathway that activates a catabolic response to amino-acid starvation irrespective of the sugar content of the diet

Dietary Modulation of Drosophila Sleep-Wake Behaviour

Background A complex relationship exists between diet and sleep but despite its impact on human health, this relationship remains uncharacterized and poorly understood. Drosophila melanogaster is an important model for the study of metabolism and behaviour, however the effect of diet upon Drosophila sleep remains largely unaddressed. Methodology/Principal Findings Using automated behavioural monitoring, a capillary feeding assay and pharmacological treatments, we examined the effect of dietary yeast and sucrose upon Drosophila sleep-wake behaviour for three consecutive days. We found that dietary yeast deconsolidated the sleep-wake behaviour of flies by promoting arousal from sleep in males and shortening periods of locomotor activity in females. We also demonstrate that arousal from nocturnal sleep exhibits a significant ultradian rhythmicity with a periodicity of 85 minutes. Increasing the dietary sucrose concentration from 5% to 35% had no effect on total sucrose ingestion per day nor any affect on arousal, however it did lengthen the time that males and females remained active. Higher dietary sucrose led to reduced total sleep by male but not female flies. Locomotor activity was reduced by feeding flies Metformin, a drug that inhibits oxidative phosphorylation, however Metformin did not affect any aspects of sleep. Conclusions We conclude that arousal from sleep is under ultradian control and regulated in a sex-dependent manner by dietary yeast and that dietary sucrose regulates the length of time that flies sustain periods of wakefulness. These findings highlight Drosophila as an important model with which to understand how diet impacts upon sleep and wakefulness in mammals and humans

Reverse Engineering the Yeast RNR1 Transcriptional Control System

Transcription is controlled by multi-protein complexes binding to short non-coding regions of genomic DNA. These complexes interact combinatorially. A major goal of modern biology is to provide simple models that predict this complex behavior. The yeast gene RNR1 is transcribed periodically during the cell cycle. Here, we present a pilot study to demonstrate a new method of deciphering the logic behind transcriptional regulation. We took regular samples from cell cycle synchronized cultures of Saccharomyces cerevisiae and extracted nuclear protein. We tested these samples to measure the amount of protein that bound to seven different 16 base pair sequences of DNA that have been previously identified as protein binding locations in the promoter of the RNR1 gene. These tests were performed using surface plasmon resonance. We found that the surface plasmon resonance signals showed significant variation throughout the cell cycle. We correlated the protein binding data with previously published mRNA expression data and interpreted this to show that transcription requires protein bound to a particular site and either five different sites or one additional sites. We conclude that this demonstrates the feasibility of this approach to decipher the combinatorial logic of transcription