Phylogenetic relationships among the "spiny solanums" (Solanum subgenus Leptostemonum, Solanaceae)

Journal ArticleSpecies of Solanum subgenus Leptostemonum comprise almost one third of the genus and are distributed worldwide. Members of this group are defined by their sharp epidermal prickles; thus, they are commonly referred to as the ˜spiny solanums." This subgenus includes a number of economically important species such as the Old World eggplants, as well as locally cultivated New World species such as the naranjilla and cocona

Four-gene study of evolutionary relationships in Solanum section Acanthophora

Journal ArticleThe "spiny solanums," Solanum subgenus Leptostemonum (Solanaceae), comprise a large lineage with over 350 species and include the cultivated eggplant, Solanum melongena. Despite the importance of this subgenus, phylogenetic relationships among these taxa are currently unclear. The present research contributes to this understanding while focusing on Solanum section Acanthophora, a group of ca. 19 species defined by the presence of simple hairs, rather than the stellate hairs common across the rest of subgenus Leptostemonum

Impact of Stepping Stones on incidence of HIV and HSV-2 and sexual behaviour in rural South Africa: cluster randomised controlled trial

Objective To assess the impact of Stepping Stones, a HIV prevention programme, on incidence of HIV and herpes simplex type 2 (HSV-2) and sexual behaviour

Health gains and fi nancial risk protection aff orded by public fi nancing of selected interventions in Ethiopia: an extended cost-eff ectiveness analysis

Background The way in which a government chooses to fi nance a health intervention can aff ect the uptake of health interventions and consequently the extent of health gains. In addition to health gains, some policies such as public fi nance can insure against catastrophic health expenditures. We aimed to evaluate the health and fi nancial risk protection benefi ts of selected interventions that could be publicly fi nanced by the government of Ethiopia. Methods We used extended cost-eff ectiveness analysis to assess the health gains (deaths averted) and fi nancial risk protection aff orded (cases of poverty averted) by a bundle of nine (among many other) interventions that the Government of Ethiopia aims to make universally available. These nine interventions were measles vaccination, rotavirus vaccination, pneumococcal conjugate vaccination, diarrhoea treatment, malaria treatment, pneumonia treatment, caesarean section surgery, hypertension treatment, and tuberculosis treatment. Findings Our analysis shows that, per dollar spent by the Ethiopian Government, the interventions that avert the most deaths are measles vaccination (367 deaths averted per $100 000 spent), pneumococcal conjugate vaccination (170 deaths averted per$100 000 spent), and caesarean section surgery (141 deaths averted per $100 000 spent). The interventions that avert the most cases of poverty are caesarean section surgery (98 cases averted per$100 000 spent), tuberculosis treatment (96 cases averted per $100 000 spent), and hypertension treatment (84 cases averted per$100 000 spent). Interpretation Our approach incorporates fi nancial risk protection into the economic evaluation of health interventions and therefore provides information about the effi ciency of attainment of both major objectives of a health system: improved health and fi nancial risk protection. One intervention might rank higher on one or both metrics than another, which shows how intervention choice—the selection of a pathway to universal health coverage—might involve weighing up of sometimes competing objectives. This understanding can help policy makers to select interventions to target specifi c policy goals (ie, improved health or fi nancial risk protection). It is especially relevant for the design and sequencing of universal health coverage to meet the needs of poor populations

Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

Background: Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression. Methods: We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics. Results: Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822–0.972]; DB from DV with AUC of 0.825 [0.691–0.959] (signature-1) and 0.867 [0.753–0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787–0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808–1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%). Conclusions: Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups

New perspectives on realism, tractability, and complexity in economics

Fuzzy logic and genetic algorithms are used to rework more realistic (and more complex) models of competitive markets. The resulting equilibria are significantly different from the ones predicted from the usual static analysis; the methodology solves the Walrasian problem of how markets can reach equilibrium, starting with firms trading at disparate prices. The modified equilibria found in these complex market models involve some mutual self-restraint on the part of the agents involved, relative to economically rational behaviour. Research (using similar techniques) into the evolution of collaborative behaviours in economics, and of altruism generally, is summarized; and the joint significance of these two bodies of work for public policy is reviewed. The possible extension of the fuzzy/ genetic methodology to other technical aspects of economics (including international trade theory, and development) is also discussed, as are the limitations to the usefulness of any type of theory in political domains. For the latter purpose, a more differentiated concept of rationality, appropriate to ill-structured choices, is developed. The philosophical case for laissez-faire policies is considered briefly; and the prospects for change in the way we ‘do economics’ are analysed

Exploring the Genetic Basis of Variation in Gene Predictions with a Synthetic Association Study

Identifying DNA polymorphisms that affect molecular processes like transcription, splicing, or translation typically requires genotyping and experimentally characterizing tissue from large numbers of individuals, which remains expensive and time consuming. Here we introduce an alternative strategy: a “synthetic association study” in which we computationally predict molecular phenotypes on artificial genomes containing randomly sampled combinations of polymorphic alleles, and perform a classical association study to identify genotypes underlying variation in these computationally predicted annotations. We applied this method to characterize the effects on gene structure of 32,792 single-nucleotide polymorphisms between two strains of the antibiotic producing fungus Penicilium chrysogenum. Although these SNPs represent only 0.1 percent of the nucleotides in the genome, they collectively altered 1.8 percent of predicted gene models between these strains. To determine which SNPs or combinations of SNPs were responsible for this variation, we predicted protein-coding genes in 500 intermediate genomes, each identical except for randomly chosen alleles at each SNP position. Of 30,468 gene models in the genome, 557 varied across these 500 genomes. 226 of these polymorphic gene models (40%) were perfectly correlated with individual SNPs, all of which were within or immediately proximal to the affected gene. The genetic architectures of the other 321 were more complex, with several examples of SNP epistasis that would have been difficult to predict a priori. We expect that many of the SNPs that affect computational gene structure reflect a biologically unrealistic sensitivity of the gene prediction algorithm to sequence changes, and we propose that genome annotation algorithms could be improved by minimizing their sensitivity to natural polymorphisms. However, many of the SNPs we identified are likely to affect transcript structure in vivo, and the synthetic association study approach can be easily generalized to any computed genome annotation to uncover relationships between genotype and important molecular phenotypes

Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations.

Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies of asthma in 5,416 individuals with asthma (cases) including individuals of European American, African American or African Caribbean, and Latino ancestry, with replication in an additional 12,649 individuals from the same ethnic groups. We identified five susceptibility loci. Four were at previously reported loci on 17q21, near IL1RL1, TSLP and IL33, but we report for the first time, to our knowledge, that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a new asthma susceptibility locus at PYHIN1, with the association being specific to individuals of African descent (P = 3.9 × 10(-9)). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma

Mapping and modelling the geographical distribution and environmental limits of podoconiosis in Ethiopia

BACKGROUND Ethiopia is assumed to have the highest burden of podoconiosis globally, but the geographical distribution and environmental limits and correlates are yet to be fully investigated. In this paper we use data from a nationwide survey to address these issues. METHODOLOGY Our analyses are based on data arising from the integrated mapping of podoconiosis and lymphatic filariasis (LF) conducted in 2013, supplemented by data from an earlier mapping of LF in western Ethiopia in 2008-2010. The integrated mapping used woreda (district) health offices' reports of podoconiosis and LF to guide selection of survey sites. A suite of environmental and climatic data and boosted regression tree (BRT) modelling was used to investigate environmental limits and predict the probability of podoconiosis occurrence. PRINCIPAL FINDINGS Data were available for 141,238 individuals from 1,442 communities in 775 districts from all nine regional states and two city administrations of Ethiopia. In 41.9% of surveyed districts no cases of podoconiosis were identified, with all districts in Affar, Dire Dawa, Somali and Gambella regional states lacking the disease. The disease was most common, with lymphoedema positivity rate exceeding 5%, in the central highlands of Ethiopia, in Amhara, Oromia and Southern Nations, Nationalities and Peoples regional states. BRT modelling indicated that the probability of podoconiosis occurrence increased with increasing altitude, precipitation and silt fraction of soil and decreased with population density and clay content. Based on the BRT model, we estimate that in 2010, 34.9 (95% confidence interval [CI]: 20.2-51.7) million people (i.e. 43.8%; 95% CI: 25.3-64.8% of Ethiopia's national population) lived in areas environmentally suitable for the occurrence of podoconiosis. CONCLUSIONS Podoconiosis is more widespread in Ethiopia than previously estimated, but occurs in distinct geographical regions that are tied to identifiable environmental factors. The resultant maps can be used to guide programme planning and implementation and estimate disease burden in Ethiopia. This work provides a framework with which the geographical limits of podoconiosis could be delineated at a continental scale

Genome-wide association and HLA fine-mapping studies identify risk loci and genetic pathways underlying allergic rhinitis

Allergic rhinitis is the most common clinical presentation of allergy, affecting 400 million people worldwide, with increasing incidence in westernized countries1,2. To elucidate the genetic architecture and understand the underlying disease mechanisms, we carried out a meta-analysis of allergic rhinitis in 59,762 cases and 152,358 controls of European ancestry and identified a total of 41 risk loci for allergic rhinitis, including 20 loci not previously associated with allergic rhinitis, which were confirmed in a replication phase of 60,720 cases and 618,527 controls. Functional annotation implicated genes involved in various immune pathways, and fine mapping of the HLA region suggested amino acid variants important for antigen binding. We further performed genome-wide association study (GWAS) analyses of allergic sensitization against inhalant allergens and nonallergic rhinitis, which suggested shared genetic mechanisms across rhinitis-related traits. Future studies of the identified loci and genes might identify novel targets for treatment and prevention of allergic rhinitis