Framework for better living with HIV in England

Duration: April 2007 - May 2009 Sigma Research was funded by Terrence Higgins Trust to co-ordinate the development of a framework to address the health, social care, support and information needs of people with diagnosed HIV in England. It has now been published as the Framework for better living with HIV in England. The over-arching goal of the framework is that all people with diagnosed HIV in England "are enabled to have the maximum level of health, well-being, quality of life and social integration". In its explanation of how this should occur the document presents a road map for social care, support and information provision to people with diagnosed HIV in England. By establishing and communicating aims and objectives, the framework should build consensus and provide a means to establish how interventions could be prioritised and coordinated. The key drivers for the framework were clearly articulated ethical principles, agreed by all those who sign up to it, and an inclusive social development / health promotion approach. Sigma Research worked on the framework with a range of other organisations who sent representatives to a Framework Development Group (see below for membership). The framework is evidence-based and seeks to: Promote and protect the rights and well-being of all people with HIV in England. Maximise the capacity of individuals and groups of people with HIV to care for, advocate and represent themselves effectively. Improve and protect access to appropriate information, social support, social care and clinical services. Minimise social, economic, governmental and judicial change detrimental to the health and well being of people with HIV. Alongside the development of the framework, Sigma Research undertook a national needs assessment among people with diagnosed HIV across the UK called What do you need?. These two projects informed and supported each other. Framework Development Group included: African HV Policy Network Black Health Agency George House Trust NAM NAT (National AIDS Trust) Positively Women Terrence Higgins Trus

Generalized Sagnac Effect

Experiments were conducted to study light propagation in a light waveguide loop consisting of linearly and circularly moving segments. We found that any segment of the loop contributes to the total phase difference between two counterpropagating light beams in the loop. The contribution is proportional to a product of the moving velocity v and the projection of the segment length Deltal on the moving direction, Deltaphi=4pivDeltal/clambda. It is independent of the type of motion and the refractive index of waveguides. The finding includes the Sagnac effect of rotation as a special case and suggests a new fiber optic sensor for measuring linear motion with nanoscale sensitivity.Comment: 3 pages (including 3 figures

SNP-PHAGE – High throughput SNP discovery pipeline

BACKGROUND: Single nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable. RESULTS: We developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at . CONCLUSION: SNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers

Application of machine learning in SNP discovery

<p>Abstract</p> <p>Background</p> <p>Single nucleotide polymorphisms (SNP) constitute more than 90% of the genetic variation, and hence can account for most trait differences among individuals in a given species. Polymorphism detection software PolyBayes and PolyPhred give high false positive SNP predictions even with stringent parameter values. We developed a machine learning (ML) method to augment PolyBayes to improve its prediction accuracy. ML methods have also been successfully applied to other bioinformatics problems in predicting genes, promoters, transcription factor binding sites and protein structures.</p> <p>Results</p> <p>The ML program C4.5 was applied to a set of features in order to build a SNP classifier from training data based on human expert decisions (True/False). The training data were 27,275 candidate SNP generated by sequencing 1973 STS (sequence tag sites) (12 Mb) in both directions from 6 diverse homozygous soybean cultivars and PolyBayes analysis. Test data of 18,390 candidate SNP were generated similarly from 1359 additional STS (8 Mb). SNP from both sets were classified by experts. After training the ML classifier, it agreed with the experts on 97.3% of test data compared with 7.8% agreement between PolyBayes and experts. The PolyBayes positive predictive values (PPV) (i.e., fraction of candidate SNP being real) were 7.8% for all predictions and 16.7% for those with 100% posterior probability of being real. Using ML improved the PPV to 84.8%, a 5- to 10-fold increase. While both ML and PolyBayes produced a similar number of true positives, the ML program generated only 249 false positives as compared to 16,955 for PolyBayes. The complexity of the soybean genome may have contributed to high false SNP predictions by PolyBayes and hence results may differ for other genomes.</p> <p>Conclusion</p> <p>A machine learning (ML) method was developed as a supplementary feature to the polymorphism detection software for improving prediction accuracies. The results from this study indicate that a trained ML classifier can significantly reduce human intervention and in this case achieved a 5–10 fold enhanced productivity. The optimized feature set and ML framework can also be applied to all polymorphism discovery software. ML support software is written in Perl and can be easily integrated into an existing SNP discovery pipeline.</p

Reviews

Reviews of International and comparative industrial relations, Tatau Tatau - one big union altogether, Remedy for present evils: a history of the New Zealand Public Service Association from 1890, Sexual harassment in the workplace, Employee selection, Legislating for workplace hazards in New Zealand: overseas experience and our present and future needs, People and enterprises - human behaviour in New Zealand organisations and From school to unemployment? The labour market for young peopl

Arf6 controls beta-amyloid production by regulating macropinocytosis of the Amyloid Precursor Protein to lysosomes

Alzheimer\u27s disease (AD) is characterized by the deposition of Beta-Amyloid (Aβ) peptides in the brain. Aβ peptides are generated by cleavage of the Amyloid Precursor Protein (APP) by the β - and γ - secretase enzymes. Although this process is tightly linked to the internalization of cell surface APP, the compartments responsible are not well defined. We have found that APP can be rapidly internalized from the cell surface to lysosomes, bypassing early and late endosomes. Here we show by confocal microscopy and electron microscopy that this pathway is mediated by macropinocytosis. APP internalization is enhanced by antibody binding/crosslinking of APP suggesting that APP may function as a receptor. Furthermore, a dominant negative mutant of Arf6 blocks direct transport of APP to lysosomes, but does not affect classical endocytosis to endosomes. Arf6 expression increases through the hippocampus with the development of Alzheimer\u27s disease, being expressed mostly in the CA1 and CA2 regions in normal individuals but spreading through the CA3 and CA4 regions in individuals with pathologically diagnosed AD. Disruption of lysosomal transport of APP reduces both Aβ40 and Aβ42 production by more than 30 %. Our findings suggest that the lysosome is an important site for Aβ production and that altering APP trafficking represents a viable strategy to reduce Aβ production

Phytophthora Root Rot Resistance in Soybean E00003

Phytophthora root rot (PRR) is a devastating disease in soybean [Glycine max (L.) Merr.] production. Michigan elite soybean E00003 is resistant to Phytophthora sojae and has been used as a resistance source in breeding. Genetic control of PRR resistance in this source is unknown. To facilitate marker-assisted selection (MAS), the PRR resistance loci in E00003 and their map locations need to be determined. In this study, a genetic mapping approach was used to identify major PRR -resistant loci in E00003. The mapping population consists of 240 F4–derived lines developed by crossing E00003 with the P. sojae susceptible line PI 567543C. In 2009 and 2010, the mapping population was evaluated in the greenhouse for PRR resistance against P. sojae races 1, 4, and 7, using modified rice (Oryza sativa L.) grain inoculation method. The population was genotyped with seven simple sequence repeat (SSR) and three single nucleotide polymorphism (SNP) markers derived from bulk segregant analysis. The heritability of resistance in the population ranged from 83 to 94%. A major locus, contributing 50 to 76% of the phenotypic variation, was mapped within a 3 cM interval in the Rps1 region. The interval was further saturated with more BARCSOY SSRs and SNPs with TaqMan assays. Two SSRs and three SNPs within the Rps1k gene were highly associated with PRR resistance in the mapping population. The major resistance gene in E00003 is either allelic or tightly linked to Rps1k.The molecular markers located in the Rps1k gene can be used to improve MAS for PRR resistance