Seasonality of tuberculosis in India: is it real and what does it tell us?

India has a third of the world’s tuberculosis cases. Large-scale expansion of a national program in 1998 has allowed for population-based analyses of data from tuberculosis registries. We assessed seasonal trends using quarterly reports from districts with stable tuberculosis control programs (population 115 million). In northern India, tuberculosis diagnoses peaked between April and June, and reached a nadir between October and December, whereas no seasonality was reported in the south. Overall, rates of new smear-positive tuberculosis cases were 57 per 100 000 population in peak seasons versus 46 per 100 000 in trough seasons. General health-seeking behavior artifact was ruled out. Seasonality was highest in paediatric cases, suggesting variation in recent transmission.

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks—the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity—generating novel, testable predictions—and generality—spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.

Radiocarbon Dating, Chronologic Framework, and Changes in Accumulation Rates of Holocene Estuarine Sediments from Chesapeake Bay

Rapidly accumulating Holocene sediments in estuaries commonly are difficult to sample and date. In Chesapeake Bay, we obtained sediment cores as much as 20min length and used numerous radiocarbon ages measured by accelerator mass spectrometry methods to provide the first detailed chronologies of Holocene sediment accumulation in the bay. Carbon in these sediments is a complex mixture of materials from a variety of sources. Analyses of different components of the sediments show that total organic carbon ages are largely unreliable, because much of the carbon (including coal) has been transported to the bay from upstream sources and is older than sediments in which it was deposited. Mollusk shells (clams, oysters) and foraminifera appear to give reliable results, although reworking and burrowing are potential problems. Analyses of museum specimens collected alive before atmospheric nuclear testing suggest that the standard reservoir correction for marine samples is appropriate for middle to lower Chesapeake Bay. The biogenic carbonate radiocarbon ages are compatible with 210Pb and 137Cs data and pollen stratigraphy from the same sites. Post-settlement changes in sediment transport and accumulation is an important environmental issue in many estuaries, including the Chesapeake. Our data show that large variations in sediment mass accumulation rates occur among sites. At shallow water sites, local factors seem to control changes in accumulation rates with time. Our two relatively deep-water sites in the axial channel of the bay have different long-term average accumulation rates, but the history of sediment accumulation at these sites appears to reflect overall conditions in the bay. Mass accumulation rates at the two deep-water sites rapidly increased by about fourfold coincident with widespread land clearance for agriculture in the Chesapeake watershed.