Malcolm John

Abstract:

The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward. © 2012 Tatem et al.; licensee BioMed Central Ltd.

Abstract:

Delayed HIV-1 disease progression is associated with a single nucleotide polymorphism upstream of the HLA-C gene that correlates with differential expression of the HLA-C Ag. This polymorphism was recently shown to be a marker for a protective variant in the 3'UTR of HLA-C that disrupts a microRNA binding site, resulting in enhanced HLA-C expression at the cell surface. Whether individuals with "high" HLA-C expression show a stronger HLA-C-restricted immune response exerting better viral control than that of their counterparts has not been established. We hypothesized that the magnitude of the HLA-C-restricted immune pressure on HIV would be greater in subjects with highly expressed HLA-C alleles. Using a cohort derived from a unique narrow source epidemic in China, we identified mutations in HIV proviral DNA exclusively associated with HLA-C, which were used as markers for the intensity of the immune pressure exerted on the virus. We found an increased frequency of mutations in individuals with highly expressed HLA-C alleles, which also correlated with IFN-γ production by HLA-C-restricted CD8(+) T cells. These findings show that immune pressure on HIV is stronger in subjects with the protective genotype and highlight the potential role of HLA-C-restricted responses in HIV control. This is, to our knowledge, the first in vivo evidence supporting the protective role of HLA-C-restricted responses in nonwhites during HIV infection.

Abstract:

First observations of the Cabibbo-suppressed decays B¯0 → D+K-π+π- and B- → D0K-π+π- are reported using 35 pB-1 of data collected with the LHCb detector. Their branching fractions are measured with respect to the corresponding Cabibbo-favored decays, from which we obtain B(B¯0→ D+K-π+π-)/B(B¯0→D+π-π+π-)=(5.9±1.1±0.5)×10-2 and B(B-→D0K-π+π-)/B(B-→ D0π-π+π-)=(9.4±1.3±0.9)×10-2, where the uncertainties are statistical and systematic, respectively. The B- → D0K-π+π- decay is particularly interesting, as it can be used in a similar way to B- → D0K- to measure the Cabibbo-Kobayashi-Maskawa phase γ. © 2012 CERN.

Abstract:

The decay B̄s0→J/ψK +K - is investigated using 0.16fb -1 of data collected with the LHCb detector using 7 TeV pp collisions. Although the J/ψ channel is well known, final states at higher K +K - masses have not previously been studied. In the K +K - mass spectrum we observe a significant signal in the f2′(1525) region as well as a nonresonant component. After subtracting the nonresonant component, we find B(B̄s0→J/ψf2′(1525))/ B(B̄s0→J/ψ)=(26.4±2.7±2.4)%. © 2012 CERN.

Abstract:

The decays Bs0 --> Ds(*)+ Ds(*)- are reconstructed in a data sample corresponding to an integrated luminosity of 6.8 fb-1 collected by the CDF II detector at the Tevatron p\bar{p} collider. All decay modes are observed with a significance of more than 10 sigma, and we measure the Bs0 production rate times Bs0 --> Ds(*)+ Ds(*)- branching ratios relative to the normalization mode B0 --> Ds+ D- to be $0.183 \pm 0.021 \pm 0.017$ for Bs0 --> Ds+ Ds-, $0.424 \pm 0.046 \pm 0.035$ for Bs0 --> Ds*+- Ds-+, $0.654 \pm 0.072 \pm 0.065$ for Bs0 --> Ds*+ Ds*-, and $1.261 \pm 0.095 \pm 0.112$ for the inclusive decay Bs0 --> Ds(*)+ Ds(*)-, where the uncertainties are statistical and systematic. These results are the most precise single measurements to date and provide important constraints for indirect searches for non-standard model physics in Bs0 mixing.