Geophysical Observations of Taliks Below Drained Lake Basins on the Arctic Coastal Plain of Alaska

Lakes and drained lake basins (DLBs) together cover up to ∼80% of the western Arctic Coastal Plain of Alaska. The formation and drainage of lakes in this continuous permafrost region drive spatial and temporal landscape dynamics. Postdrainage processes including vegetation succession and permafrost aggradation have implications for hydrology, carbon cycling, and landscape evolution. Here, we used surface nuclear magnetic resonance (NMR) and transient electromagnetic (TEM) measurements in conjunction with thermal modeling to investigate permafrost aggradation beneath eight DLBs on the western Arctic Coastal Plain of Alaska. We also surveyed two primary surface sites that served as nonlake affected control sites. Approximate timing of lake drainage was estimated based on historical aerial imagery. We interpreted the presence of taliks based on either unfrozen water estimated with surface NMR and/or TEM resistivities in DLBs compared to measurements on primary surface sites and borehole resistivity logs. Our results show evidence of taliks below several DLBs that drained before and after 1949 (oldest imagery). We observed depths to the top of taliks between 9 and 45 m. Thermal modeling and geophysical observations agree about the presence and extent of taliks at sites that drained after 1949. Lake drainage events will likely become more frequent in the future due to climate change and our modeling results suggest that warmer and wetter conditions will limit permafrost aggradation in DLBs. Our observations provide useful information to predict future evolution of permafrost in DLBs and its implications for the water and carbon cycles in the Arctic

When Is a Principal Charged With an Agent’s Knowledge?

Question: Detecting species presence in vegetation and making visual assessment of abundances involve a certain amount of skill, and therefore subjectivity. We evaluated the magnitude of the error in data, and its consequences for evaluating temporal trends. Location: Swedish forest vegetation. Methods: Vegetation data were collected independently by two observers in 342 permanent 100-m2 plots in mature boreal forests. Each plot was visited by one observer from a group of 36 and one of two quality assessment observers. The cover class of 29 taxa was recorded, and presence/absence for an additional 50. Results: Overall, one third of each occurrence was missed by one of the two observers, but with large differences among species. There were more missed occurrences at low abundances. Species occurring at low abundance when present tended to be frequently overlooked. Variance component analyses indicated that cover data on 5 of 17 species had a significant observer bias. Observer-explained variance was < 10% in 15 of 17 species. Conclusion: The substantial number of missed occurrences suggests poor power in detecting changes based on presence/absence data. The magnitude of observer bias in cover estimates was relatively small, compared with random error, and therefore potentially analytically tractable. Data in this monitoring system could be improved by a more structured working model during field work.Original publication: Milberg, P., Bergstedt, J., Fridman, J., Odell, G & Westerberg, L., Systematic and random variation in vegetation monitoring data, 2008, Journal of Vegetation Science, (19), 633-644. http://dx.doi.org/10.3170/2008-8-18423. Copyright: Opulus Press, http://www.opuluspress.se/index.ph

Machine learning can identify newly diagnosed patients with CLL at high risk of infection

Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors

First-in-Man Open Clinical Trial of a Combined rdESAT-6 and rCFP-10 Tuberculosis Specific Skin Test Reagent

Tuberculin is still the only available skin test reagent for the diagnosis of mycobacterial infection. The product has a remarkable sensitivity, but poor specificity. Previous studies, including two human phase I clinical trials, have indicated that rdESAT-6 has a potential as an improved skin test reagent. Animal studies have shown that the sensitivity may be increased by inclusion of the genetically related CFP-10 antigen in the preparation without loosing specificity.In this study a Lactococcus fermented, recombinant skin test reagent consisting of a 1ratio1 wt/wt of rdESAT-6 and CFP-10 was manufactured according to GMP standards and tested for the first time in 42 healthy adult volunteers. The two doses of 0.01 microg or 0.1 microg were injected intradermally by the Mantoux technique with 6 or 12 weeks interval. No serious adverse events and only mild adverse reactions were reported. The reagent elicited a positive skin test reaction after the first injection in one participant, who most likely was latently infected with M. tuberculosis as indicated by an appreciable IFN gamma response just below the Quantiferon(R) cut-off level at the screening visit. None of the remaining participants in the four groups had any skin test reactions and sensitisation by the reagent could therefore be excluded.The investigational skin test reagent rdESAT-6 and CFP-10 appeared safe and non-sensitising in this first-in-man clinical trial in human volunteers and can now be tested in larger clinical trials involving individuals with latent M. tuberculosis infection or active TB disease.ClinicalTrials.gov NCT00793702

Population ecology of the sea lamprey (Petromyzon marinus) as an invasive species in the Laurentian Great Lakes and an imperiled species in Europe

The sea lamprey Petromyzon marinus (Linnaeus) is both an invasive non-native species in the Laurentian Great Lakes of North America and an imperiled species in much of its native range in North America and Europe. To compare and contrast how understanding of population ecology is useful for control programs in the Great Lakes and restoration programs in Europe, we review current understanding of the population ecology of the sea lamprey in its native and introduced range. Some attributes of sea lamprey population ecology are particularly useful for both control programs in the Great Lakes and restoration programs in the native range. First, traps within fish ladders are beneficial for removing sea lampreys in Great Lakes streams and passing sea lampreys in the native range. Second, attractants and repellants are suitable for luring sea lampreys into traps for control in the Great Lakes and guiding sea lamprey passage for conservation in the native range. Third, assessment methods used for targeting sea lamprey control in the Great Lakes are useful for targeting habitat protection in the native range. Last, assessment methods used to quantify numbers of all life stages of sea lampreys would be appropriate for measuring success of control in the Great Lakes and success of conservation in the native range