Untangling Neolithic and Bronze Age mitochondrial lineages in South Asia.

Two key moments shaped the extant South Asian gene pool within the last 10 thousand years (ka): the Neolithic period, with the advent of agriculture and the rise of the Harappan/Indus Valley Civilisation; and Late Bronze Age events that witnessed the abrupt fall of the Harappan Civilisation and the arrival of Indo-European speakers. This study focuses on the phylogeographic patterns of mitochondrial haplogroups H2 and H13 in the Indian Subcontinent and incorporates evidence from recently released ancient genomes from Central and South Asia. It found signals of Neolithic arrivals from Iran and later movements in the Bronze Age from Central Asia that derived ultimately from the Steppe. This study shows how a detailed mtDNA phylogeographic approach, combining both modern and ancient variation, can provide evidence of population movements, even in a scenario of strong male bias such as in the case of the Bronze Age Steppe dispersals.

Linking remote sensing, land cover and disease.

Land cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover; the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field.

Unmixing aggregate data: estimating the social composition of enumeration districts.

"In this paper the authors address the problem of interpreting and classifying aggregate data sources and draw parallels between tasks commonly encountered in image processing and census analysis. Both of these fields already have a range of standard classification tools which are applied in such situations, but these are hindered by the aggregate nature of the input data. An approach to ¿unmixing' aggregate data, and thus revealing the nature of the subunit variation masked by aggregation, is introduced. This approach has already shown considerable success in Earth Observation applications, and in this paper the authors present the adaptation and application of the approach to Census small area statistics data for Southampton, Hants, [in England] revealing something of the social composition of Southampton's enumeration districts. The unmixing technique utilises an artificial neural network."