Deposition History of Polychlorinated Biphenyls to the Lomonosovfonna Glacier, Svalbard: A 209 Congener Analysis

A 37 m deep ice core representing 1957–2009 and snow from 2009 to 2010 were collected on the Lomonosovfonna glacier, Svalbard (78.82° N; 17.43° E) and analyzed for 209 polychlorinated biphenyl (PCB) congeners using high-resolution mass spectrometry. Congener profiles in all samples showed the prevalence of tetra- and pentachlorobiphenyls, dominated in all samples by PCB-44, PCB-52, PCB-70 + PCB-74, PCB-87 + PCB-97, PCB-95, PCB-99, PCB-101, and PCB-110. The ∑PCB flux varied over time, but the peak flux, 19 pg cm–2 year–1 from 1957 to 1966, recurred in 1974–1983, 1998–2009, and 2009–2010. The minimum was 5.75 pg cm–2 year–1 in 1989–1998, following a 15 year decline. Peak ∑PCB fluxes here are lower than measured in the Canadian Arctic. The analysis of all 209 congeners revealed that PCB-11 (3,3′-dichlorobiphenyl) was present in all samples, representing 0.9–4.5% of ∑PCB. PCB-11 was not produced in a commercial PCB product, and its source to the Arctic has not been well-characterized; however, our results confirm that the sources to Lomonosovfonna have been active since 1957. The higher fluxes of ∑PCB correspond to periods when average 5 day air mass back trajectories have a frequency of 8–10% and reach 60° N or beyond over northern Europe and western Russia or the North Sea into the U.K

Unified detection and tracking of instruments during retinal microsurgery

Methods for tracking an object have generally fallen into two groups: tracking by detection and tracking through local optimization. The advantage of detection-based tracking is its ability to deal with target appearance and disappearance, but it does not naturally take advantage of target motion continuity during detection. The advantage of local optimization is efficiency and accuracy, but it requires additional algorithms to initialize tracking when the target is lost. To bridge these two approaches, we propose a framework for unified detection and tracking as a time-series Bayesian estimation problem. The basis of our approach is to treat both detection and tracking as a sequential entropy minimization problem, where the goal is to determine the parameters describing a target in each frame. To do this we integrate the Active Testing (AT) paradigm with Bayesian filtering, and this results in a framework capable of both detecting and tracking robustly in situations where the target object enters and leaves the field of view regularly. We demonstrate our approach on a retinal tool tracking problem and show through extensive experiments that our method provides an efficient and robust tracking solution.