The role of a changing Arctic Ocean and climate for the biogeochemical cycling of dimethyl sulphide and carbon monoxide

Dimethyl sulphide (DMS) and carbon monoxide(CO) are climate-relevant trace gases that play key roles in the radiative budget of the Arctic atmosphere. Under global warming, Arctic sea ice retreats at an unprecedented rate, altering light penetration and biological communities, and potentially affect DMS and CO cycling in the Arctic Ocean. This could have socio-economic implications in and beyond the Arctic region. However, little is known about CO production pathways and emissions in this region and the future development of DMS and CO cycling. Here we summarize the current understanding and assess potential future changes of DMS and CO cycling in relation to changes in sea ice coverage, light penetration, bacterial and microalgal communities, pH and physical properties. We suggest that production of DMS and CO might increase with ice melting, increasing light availability and shifting phytoplankton community. Among others, policy measures should facilitate large scale process studies, coordinated long term observations and modelling efforts to improve our current understanding of the cycling and emissions of DMS and CO in the Arctic Ocean and of global consequences

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Causes of Morbidity in Wild Raptor Populations Admitted at a Wildlife Rehabilitation Centre in Spain from 1995-2007: A Long Term Retrospective Study

Background: Morbidity studies complement the understanding of hazards to raptors by identifying natural or anthropogenic factors. Descriptive epidemiological studies of wildlife have become an important source of information about hazards to wildlife populations. On the other hand, data referenced to the overall wild population could provide a more accurate assessment of the potential impact of the morbidity/mortality causes in populations of wild birds. Methodology/Principal Findings: The present study described the morbidity causes of hospitalized wild raptors and their incidence in the wild populations, through a long term retrospective study conducted at a wildlife rehabilitation centre of Catalonia (1995-2007). Importantly, Seasonal Cumulative Incidences (SCI) were calculated considering estimations of the wild population in the region and trend analyses were applied among the different years. A total of 7021 birds were analysed: 7 species of Strigiformes (n = 3521) and 23 of Falconiformes (n = 3500). The main causes of morbidity were trauma (49.5%), mostly in the Falconiformes, and orphaned/young birds (32.2%) mainly in the Strigiformes. During wintering periods, the largest morbidity incidence was observed in Accipiter gentillis due to gunshot wounds and in Tyto alba due to vehicle trauma. Within the breeding season, Falco tinnunculus (orphaned/young category) and Bubo bubo (electrocution and metabolic disorders) represented the most affected species. Cases due to orphaned/young, infectious/parasitic diseases, electrocution and unknown trauma tended to increase among years. By contrast, cases by undetermined cause, vehicle trauma and captivity decreased throughout the study period. Interestingly, gunshot injuries remained constant during the study period. Conclusions/Significance: Frequencies of morbidity causes calculated as the proportion of each cause referred to the total number of admitted cases, allowed a qualitative assessment of hazards for the studied populations. However, cumulative incidences based on estimated wild raptor population provided a more accurate approach to the potential ecological impact of the morbidity causes in the wild populations

Demonstration of the event identification capabilities of the NEXT-White detector

In experiments searching for neutrinoless double-beta decay, the possibility of identifying the two emitted electrons is a powerful tool in rejecting background events and therefore improving the overall sensitivity of the experiment. In this paper we present the first measurement of the efficiency of a cut based on the different event signatures of double and single electron tracks, using the data of the NEXT-White detector, the first detector of the NEXT experiment operating underground. Using a 228Th calibration source to produce signal-like and background-like events with energies near 1.6 MeV, a signal efficiency of 71.6 ± 1.5 stat± 0.3 sys% for a background acceptance of 20.6 ± 0.4 stat± 0.3 sys% is found, in good agreement with Monte Carlo simulations. An extrapolation to the energy region of the neutrinoless double beta decay by means of Monte Carlo simulations is also carried out, and the results obtained show an improvement in background rejection over those obtained at lower energies. [Figure not available: see fulltext.

Radiogenic backgrounds in the NEXT double beta decay experiment

Natural radioactivity represents one of the main backgrounds in the search for neutrinoless double beta decay. Within the NEXT physics program, the radioactivity- induced backgrounds are measured with the NEXT-White detector. Data from 37.9 days of low-background operations at the Laboratorio Subterráneo de Canfranc with xenon depleted in 136Xe are analyzed to derive a total background rate of (0.84±0.02) mHz above 1000 keV. The comparison of data samples with and without the use of the radon abatement system demonstrates that the contribution of airborne-Rn is negligible. A radiogenic background model is built upon the extensive radiopurity screening campaign conducted by the NEXT collaboration. A spectral fit to this model yields the specific contributions of 60Co, 40K, 214Bi and 208Tl to the total background rate, as well as their location in the detector volumes. The results are used to evaluate the impact of the radiogenic backgrounds in the double beta decay analyses, after the application of topological cuts that reduce the total rate to (0.25±0.01) mHz. Based on the best-fit background model, the NEXT-White median sensitivity to the two-neutrino double beta decay is found to be 3.5σ after 1 year of data taking. The background measurement in a Qββ±100 keV energy window validates the best-fit background model also for the neutrinoless double beta decay search with NEXT-100. Only one event is found, while the model expectation is (0.75±0.12) events. [Figure not available: see fulltext.]

Energy calibration of the NEXT-White detector with 1% resolution near Q ββ of 136Xe

Excellent energy resolution is one of the primary advantages of electroluminescent high-pressure xenon TPCs. These detectors are promising tools in searching for rare physics events, such as neutrinoless double-beta decay (ββ0ν), which require precise energy measurements. Using the NEXT-White detector, developed by the NEXT (Neutrino Experiment with a Xenon TPC) collaboration, we show for the first time that an energy resolution of 1% FWHM can be achieved at 2.6 MeV, establishing the present technology as the one with the best energy resolution of all xenon detectors for ββ0ν searches. [Figure not available: see fulltext.

On the Origin of Indonesian Cattle

Background: Two bovine species contribute to the Indonesian livestock, zebu (Bos indicus) and banteng (Bos javanicus), respectively. Although male hybrid offspring of these species is not fertile, Indonesian cattle breeds are supposed to be of mixed species origin. However, this has not been documented and is so far only supported by preliminary molecular analysis. Methods and Findings: Analysis of mitochondrial, Y-chromosomal and microsatellite DNA showed a banteng introgression of 10-16% in Indonesian zebu breeds. East-Javanese Madura and Galekan cattle have higher levels of autosomal banteng introgression (20-30%) and combine a zebu paternal lineage with a predominant (Madura) or even complete (Galekan) maternal banteng origin. Two Madura bulls carried taurine Y-chromosomal haplotypes, presumably of French Limousin origin. In contrast, we did not find evidence for zebu introgression in five populations of the Bali cattle, a domestic form of the banteng. Conclusions: Because of their unique species composition Indonesian cattle represent a valuable genetic resource, which potentially may also be exploited in other tropical regions. © 2009 Mohamad et al

Nitrous oxide and methane in a changing Arctic Ocean

Human activities are changing the Arctic environment at an unprecedented rate resulting in rapid warming, freshening, sea ice retreat and ocean acidification of the Arctic Ocean. Trace gases such as nitrous oxide (N2O) and methane (CH4) play important roles in both the atmospheric reactivity and radiative budget of the Arctic and thus have a high potential to influence the region’s climate. However, little is known about how these rapid physical and chemical changes will impact the emissions of major climate-relevant trace gases from the Arctic Ocean. The combined consequences of these stressors present a complex combination of environmental changes which might impact on trace gas production and their subsequent release to the Arctic atmosphere. Here we present our current understanding of nitrous oxide and methane cycling in the Arctic Ocean and its relevance for regional and global atmosphere and climate and offer our thoughts on how this might change over the coming decades

Habitat use at fine spatial scale: how does patch clustering criteria explain the use of meadows by red deer ?

Large mammalian herbivores are keystone species in different ecosystems. To mediate the effects of large mammalian herbivores on ecosystems, it is crucial to understand their habitat selection pattern. At finer scales, herbivore patch selection depends strongly on plant community traits and therefore its understanding is constrained by patch definition criteria. Our aim was to assess which criteria for patch definition best explained use of meadows by wild, free-ranging, red deer (Cervus elaphus) in a study area in Northeast Portugal. We used two clustering criteria types based on floristic composition and gross forage classes, respectively. For the floristic criteria, phytosociological approach was used to classify plant communities, and its objectivity evaluated with a mathematical clustering of the floristic relevés. Cover of dominant plant species was tested as a proxy for the phytosociological method. For the gross forage classes, the graminoids/forbs ratio and the percentage cover of legumes were used. For assessing deer relative use of meadows we used faecal accumulation rates. Patches clustered according to floristic classification better explained selection of patches by deer. Plant community classifications based on phytosociology, or proxies of this, used for characterizing meadow patches resulted useful to understand herbivore selection pattern at fine scales and thus potentially suitable to assist wildlife management decisions

Incidence and clinical impact of infective endocarditis after transcatheter aortic valve implantation

Aims: To describe the characteristics of infective endocarditis (IE) after transcatheter aortic valve implantation (TAVI). Methods and results: This study was performed using the GAMES database, a national prospective registry of consecutive patients with IE in 26 Spanish hospitals. Of the 739 cases of IE diagnosed during the study, 1.3% were post-TAVI IE, and these 10 cases, contributed by five centres, represented 1.1% of the 952 TAVIs performed. Mean age was 80 years. All valves were implanted transfemorally. IE appeared a median of 139 days after implantation. The mean age-adjusted Charlson comorbidity index was 5.45. Chronic kidney disease was frequent (five patients), as were atrial fibrillation (five patients), chronic obstructive pulmonary disease (four patients), and ischaemic heart disease (four patients). Six patients presented aortic valve involvement, and four only mitral valve involvement; the latter group had a higher percentage of prosthetic mitral valves (0% vs. 50%). Vegetations were found in seven cases, and four presented embolism. One patient underwent surgery. Five patients died during follow-up: two of these patients died during the admission in which the valve was implanted. Conclusions: IE is a rare but severe complication after TAVI which affects about 1% of patients and entails a relatively high mortality rate. IE occurred during the first year in nine of the 10 patients