Using GIS and Remote Sensing to Map Grassroots Sustainable Development for a Small NGO in Nepal

Geographic information systems, through analysis and visualizations, can aid in pursuing and improving sustainable development. Thousands of Non-Governmental Organizations (NGOs) in developing countries provide a wide range of services and local organizations with fewer resources must often be more efficient to offer their services effectively. The accessibility of spatial data for assessment and, in turn, improved planning could enable these organizations to increase efficiency, thereby maximizing aid and sustainable development, as well as the number of people helped in a variety of ways. Focusing on mapping outreach and quantifying land use for a locally run NGO in Nepal, this study mapped and established a geodatabase of ongoing and completed outreach projects, as well as quantified land use using an unmanned aerial vehicle to gather high-resolution imagery at the site. The NGO, a grassroots foundation south of Kathmandu, provides workshops and funding for sustainable and empowerment projects and opportunities for learning through workshops. They were also able to provide significant disaster relief to surrounding communities following the 2015 Gorkha earthquake. We found that most programs are spatially centered near the foundation, with some programs reaching across districts. Land use at the foundation is predominantly biodynamic gardening followed by education (classrooms). We created a Story Map to share the results with the NGO and local community. The study provides baseline data for the organization that previously had none, with the goal of maximizing efficiency and ultimately increasing capacity

Complement Factor H Levels Associate With Plasmodium falciparum Malaria Susceptibility and Severity.

BACKGROUND: Plasmodium falciparum may evade complement-mediated host defense by hijacking complement Factor H (FH), a negative regulator of the alternative complement pathway. Plasma levels of FH vary between individuals and may therefore influence malaria susceptibility and severity. METHODS: We measured convalescent FH plasma levels in 149 Gambian children who had recovered from uncomplicated or severe P. falciparum malaria and in 173 healthy control children. We compared FH plasma levels between children with malaria and healthy controls, and between children with severe (n = 82) and uncomplicated malaria (n = 67). We determined associations between FH plasma levels and laboratory features of severity and used multivariate analyses to examine associations with FH when accounting for other determinants of severity. RESULTS: FH plasma levels differed significantly between controls, uncomplicated malaria cases, and severe malaria cases (mean [95% confidence interval], 257 [250 to 264], 288 [268 to 309], and 328 [313 to 344] µg/mL, respectively; analysis of variance P < .0001). FH plasma levels correlated with severity biomarkers, including lactate, parasitemia, and parasite density, but did not correlate with levels of PfHRP2, which represent the total body parasite load. Associations with severity and lactate remained significant when adjusting for age and parasite load. CONCLUSIONS: Natural variation in FH plasma levels is associated with malaria susceptibility and severity. A prospective study will be needed to strengthen evidence for causation, but our findings suggest that interfering with FH binding by P. falciparum might be useful for malaria prevention or treatment

One-year delayed effect of fog on malaria transmission: a time-series analysis in the rain forest area of Mengla County, south-west China

Background: Malaria is a major public health burden in the tropics with the potential to significantly increase in response to climate change. Analyses of data from the recent past can elucidate how short-term variations in weather factors affect malaria transmission. This study explored the impact of climate variability on the transmission of malaria in the tropical rain forest area of Mengla County, south-west China. Methods: Ecological time-series analysis was performed on data collected between 1971 and 1999. Auto-regressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Results: At the time scale of months, the predictors for malaria incidence included: minimum temperature, maximum temperature, and fog day frequency. The effect of minimum temperature on malaria incidence was greater in the cool months than in the hot months. The fog day frequency in October had a positive effect on malaria incidence in May of the following year. At the time scale of years, the annual fog day frequency was the only weather predictor of the annual incidence of malaria. Conclusion: Fog day frequency was for the first time found to be a predictor of malaria incidence in a rain forest area. The one-year delayed effect of fog on malaria transmission may involve providing water input and maintaining aquatic breeding sites for mosquitoes in vulnerable times when there is little rainfall in the 6-month dry seasons. These findings should be considered in the prediction of future patterns of malaria for similar tropical rain forest areas worldwide

A Phase 1 Trial of MSP2-C1, a Blood-Stage Malaria Vaccine Containing 2 Isoforms of MSP2 Formulated with Montanide® ISA 720

Background: In a previous Phase 1/2b malaria vaccine trial testing the 3D7 isoform of the malaria vaccine candidate Merozoite surface protein 2 (MSP2), parasite densities in children were reduced by 62%. However, breakthrough parasitemias were disproportionately of the alternate dimorphic form of MSP2, the FC27 genotype. We therefore undertook a dose-escalating, double-blinded, placebo-controlled Phase 1 trial in healthy, malaria-naïve adults of MSP2-C1, a vaccine containing recombinant forms of the two families of msp2 alleles, 3D7 and FC27 (EcMSP2-3D7 and EcMSP2-FC27), formulated in equal amounts with Montanide® ISA 720 as a water-in-oil emulsion. Methodology/Principal Findings: The trial was designed to include three dose cohorts (10, 40, and 80 μg), each with twelve subjects receiving the vaccine and three control subjects receiving Montanide® ISA 720 adjuvant emulsion alone, in a schedule of three doses at 12-week intervals. Due to unexpected local reactogenicity and concern regarding vaccine stability, the trial was terminated after the second immunisation of the cohort receiving the 40 μg dose; no subjects received the 80 μg dose. Immunization induced significant IgG responses to both isoforms of MSP2 in the 10 μg and 40 μg dose cohorts, with antibody levels by ELISA higher in the 40 μg cohort. Vaccine-induced antibodies recognised native protein by Western blots of parasite protein extracts and by immunofluorescence microscopy. Although the induced anti-MSP2 antibodies did not directly inhibit parasite growth in vitro, IgG from the majority of individuals tested caused significant antibody-dependent cellular inhibition (ADCI) of parasite growth. Conclusions/Significance: As the majority of subjects vaccinated with MSP2-C1 developed an antibody responses to both forms of MSP2, and that these antibodies mediated ADCI provide further support for MSP2 as a malaria vaccine candidate. However, in view of the reactogenicity of this formulation, further clinical development of MSP2-C1 will require formulation of MSP2 in an alternative adjuvant. Trial Registration: Australian New Zealand Clinical Trials Registry 12607000552482

Height, selected genetic markers and prostate cancer risk:Results from the PRACTICAL consortium

Background: Evidence on height and prostate cancer risk is mixed, however, recent studies with large data sets support a possible role for its association with the risk of aggressive prostate cancer. Methods: We analysed data from the PRACTICAL consortium consisting of 6207 prostate cancer cases and 6016 controls and a subset of high grade cases (2480 cases). We explored height, polymorphisms in genes related to growth processes as main effects and their possible interactions. Results: The results suggest that height is associated with high-grade prostate cancer risk. Men with height 4180cm are at a 22% increased risk as compared to men with height o173cm (OR 1.22, 95% CI 1.01–1.48). Genetic variants in the growth pathway gene showed an association with prostate cancer risk. The aggregate scores of the selected variants identified a significantly increased risk of overall prostate cancer and high-grade prostate cancer by 13% and 15%, respectively, in the highest score group as compared to lowest score group. Conclusions: There was no evidence of gene-environment interaction between height and the selected candidate SNPs. Our findings suggest a role of height in high-grade prostate cancer. The effect of genetic variants in the genes related to growth is seen in all cases and high-grade prostate cancer. There is no interaction between these two exposures.</p

Copy number signatures and mutational processes in ovarian carcinoma.

The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.NIHR, Ovarian Cancer Action, Cancer Research UK Cambridge Centre, Cambridge Experimental Cancer Medicine Centr

Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10−15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62–4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Peer reviewe

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature.

BackgroundAppropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood.MethodsA multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children.FindingsWe identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures.ConclusionsOur data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis.FundingEuropean Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC