Structural and successional variability of periphytic algal community in a Amazonian lake during the dry and rainy season (Rio Branco, Acre)

The colonization process and successional patterns of a periphytic algal community were evaluated in a Amazonian Viveiro Lake (Rio Branco, Acre, Brazil). Sampling was performed over a period of 35 days; at four-day intervals for 20 days, and then at five-day intervals. Water sampling for physical, chemical and biological analyses was done during the dry and rainy season. Glass slides were used as artificial substrates for periphyton colonization. The structural community was evaluated through population density, algae class, diversity indices and descriptive species. Species richness, diversity and evenness increased as succession progressed. While density of Bacillariophyceae, Euglenophyceae and Zygnemaphyceae increased with succession, Cyanobacteria remained dominant. Synechocystis aquatilis, Synechocystis diplococcus and Navicula pseudolanceolata were the main descriptive species in both the dry and rainy season. Cymbela tumida, Frustulia rhomboides, Trachelomonas lacustris and Closterium acicularis was correlated with an increase in hydrologic level during the rainy season. Conversely, the density of Chlamydomonas sp., Chroomonas nordstedtii, Trachelomonas volvocinopsis, Trachelomonas volvocina and Synechococcus linearis was correlated with an increase in water transparency during the dry season. In general, the periphytic algal community showed high diversity and species richness independent of season. Season also had little influence on representation of algae class and main descriptive species. However, successional patterns varied by season, and changes in hydrologic levels acted directly on the succession path of periphytic algae. More research on periphyton dynamics is needed to improve our understanding of tropical lake ecosystems, especially in Amazonian.

Grasses and legumes as cover crop in no-tillage system in northeastern Pará Brazil

Studies to select one or more species of coverage plants adapted to Amazonian soil and climate conditions of the Amazon are a promising strategy for the improvement of environmental quality, establishing no-till agricultural systems, and thereby reducing the impacts of monoculture farming. The aim of this study was to assess the persistence time, half-life time, macronutrient content and accumulation, and C:N ratio of straw coverage in a Ultisol in northeastern Pará. Experimental design was randomized blocks with five treatments and five replicates. Plants were harvested after 105 days, growth and biomass production was quantified. After 84 days, soil coverage was 97, 85, 52, 50, and 15% for signalgrass (Brachiaria brizantha) (syn. Urochloa), dense crowngrass (Panicum purpurascens), jack bean (Canavalia ensiformes), pearl millet (Pennisetum americanum) and sunn hemp (Crotalaria juncea,), respectively. Signalgrass yielded the greatest dry matter production (9,696 kg ha-1). It also had high C:N ratio (38.4), long half-life (86.5 days) and a high persistence in the field. Jack bean also showed high dry matter production (8,950 kg ha-1), but it had low C:N ratio (17.4) and lower half-life time (39 days) than the grasses. These attributes indicate that signalgrass and jack bean have a high potential for use as cover plants in no-till agricultural systems in the State of Pará.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

Evaluating sampling strategies and logistic regression methods for modelling complex land cover changes

The role of land cover change as a significant component of global change has become increasingly recognized in recent decades. Large databases measuring land cover change, and the data which can potentially be used to explain the observed changes, are also becoming more commonly available. When developing statistical models to investigate observed changes, it is important to be aware that the chosen sampling strategy and modelling techniques can influence results. We present a comparison of three sampling strategies and two forms of grouped logistic regression models (multinomial and ordinal) in the investigation of patterns of successional change after agricultural land abandonment in Switzerland. Results indicated that both ordinal and nominal transitional change occurs in the landscape and that the use of different sampling regimes and modelling techniques as investigative tools yield different results. Synthesis and applications. Our multimodel inference identified successfully a set of consistently selected indicators of land cover change, which can be used to predict further change, including annual average temperature, the number of already overgrown neighbouring areas of land and distance to historically destructive avalanche sites. This allows for more reliable decision making and planning with respect to landscape management. Although both model approaches gave similar results, ordinal regression yielded more parsimonious models that identified the important predictors of land cover change more efficiently. Thus, this approach is favourable where land cover change pattern can be interpreted as an ordinal process. Otherwise, multinomial logistic regression is a viable alternative.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

Review of the effectiveness of caps on indemnity cover for doctors in private practice

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Don't cover up your problems

This pocket-sized leaflet advises young men not to cover up their problems and on the steps they can take to promote good mental health, such as keeping active, talking through problems and taking time to relax.

Policy for annual leave/ leave of absence cover for the Local Supervising Authority Midwifery Officer (LSAMO)

This guideline relating to cover for the absence of the LSAMO for annual leave covers the period from 2012-2015.The policy sets out the arrangements to be put in place for cover for the LSAMO role and the contact details.

From the Cover: Nanoparticle conjugation of antigen enhances cytotoxic T-cell responses in pulmonary vaccination.

The ability of vaccines to induce memory cytotoxic T-cell responses in the lung is crucial in stemming and treating pulmonary diseases caused by viruses and bacteria. However, most approaches to subunit vaccines produce primarily humoral and only to a lesser extent cellular immune responses. We developed a nanoparticle (NP)-based carrier that, upon delivery to the lung, specifically targets pulmonary dendritic cells, thus enhancing antigen uptake and transport to the draining lymph node; antigen coupling via a disulfide link promotes highly efficient cross-presentation after uptake, inducing potent protective mucosal and systemic CD8(+) T-cell immunity. Pulmonary immunization with NP-conjugated ovalbumin (NP-ova) with CpG induced a threefold enhancement of splenic antigen-specific CD8(+) T cells displaying increased CD107a expression and IFN-γ production compared with immunization with soluble (i.e., unconjugated) ova with CpG. This enhanced response was accompanied by a potent Th17 cytokine profile in CD4(+) T cells. After 50 d, NP-ova and CpG also led to substantial enhancements in memory CD8(+) T-cell effector functions. Importantly, pulmonary vaccination with NP-ova and CpG induced as much as 10-fold increased frequencies of antigen-specific effector CD8(+) T cells to the lung and completely protected mice from morbidity following influenza-ova infection. Here, we highlight recruitment to the lung of a long-lasting pool of protective effector memory cytotoxic T-cells by our disulfide-linked antigen-conjugated NP formulation. These results suggest the reduction-reversible NP system is a highly promising platform for vaccines specifically targeting intracellular pathogens infecting the lung.

The cover basement contact beneath the Rawil axial depression (Western Alps) - True amplitude seismic processing, petrophysical properties, and modeling

Since 1986, several near-vertical seismic reflection profiles have been recorded in Switzerland in order to map the deep geologic structure of the Alps. One objective of this endeavour has been to determine the geometries of the autochthonous basement and of the external crystalline massifs, important elements for understanding the geodynamics of the Alpine orogeny. The PNR-20 seismic line W1, located in the Rawil depression of the western Swiss Alps, provides important information on this subject. It extends northward from the `'Penninic front'' across the Helvetic nappes to the Prealps. The crystalline massifs do not outcrop along this profile. Thus, the interpretation of `'near-basement'' reflections has to be constrained by down-dip projections of surface geology, `'true amplitude'' processing, rock physical property studies and modelling. 3-D seismic modelling has been used to evaluate the seismic response of two alternative down-dip projection models. To constrain the interpretation in the southern part of the profile, `'true amplitude'' processing has provided information on the strength of the reflections. Density and velocity measurements on core samples collected up-dip from the region of the seismic line have been used to evaluate reflection coefficients of typical lithologic boundaries in the region. The cover-basement contact itself is not a source of strong reflections, but strong reflections arise from within the overlaying metasedimentary cover sequence, allowing the geometry of the top of the basement to be determined on the basis of `'near-basement'' reflections. The front of the external crystalline massifs is shown to extend beneath the Prealps, about 6 km north of the expected position. A 2-D model whose seismic response shows reflection patterns very similar to the observed is proposed.

Malaria entomological risk factors in relation to land cover in the Lower Caura River Basin, Venezuela

To explore the effects of deforestation and resulting differences in vegetation and land cover on entomological parameters, such as anopheline species composition, abundance, biting rate, parity and entomological inoculation rate (EIR), three villages were selected in the Lower Caura River Basin, state of Bolívar, Venezuela. All-night mosquito collections were conducted between March 2008-January 2009 using CDC light traps and Mosquito Magnet(r) Liberty Plus. Human landing catches were performed between 06:00 pm-10:00 pm, when anophelines were most active. Four types of vegetation were identified. The Annual Parasite Index was not correlated with the type of vegetation. The least abundantly forested village had the highest anopheline abundance, biting rate and species diversity. Anopheles darlingi and Anopheles nuneztovari were the most abundant species and were collected in all three villages. Both species showed unique biting cycles. The more abundantly forested village of El Palmar reported the highest EIR. The results confirmed previous observations that the impacts of deforestation and resulting changes in vegetation cover on malaria transmission are complex and vary locally.

Fluid flow through the sedimentary cover in northern Switzerland recorded by calcite-celestite veins (Oftringen borehole, Olten)

Abundant veins filled by calcite, celestite and pyrite were found in the core of a 719 m deep borehole drilled in Oftringen near Olten, located in the north-western Molasse basin, close to the thrust of the Folded Jura. Host rocks are calcareous marl, argillaceous limestone and limestone of the Dogger and Malm. The delta O-18 values of vein calcite are lower than in host rock carbonate and, together with microthermometric data from fluid inclusions in vein calcite, indicate precipitation from a seawater-dominated fluid at average temperatures of 56-68A degrees C. Such temperatures were reached at the time of maximum burial of the sedimentary pile in the late Miocene. The depth profile of delta C-13 and Sr-87/Sr-86 values and Sr content of both whole-rock carbonate and vein calcite show marked trends towards negative delta C-13, high Sr-87/Sr-86, and low Sr content in the uppermost 50-150 m of the Jurassic profile (upper Oxfordian). The Sr-87/Sr-86 of vein minerals is generally higher than that of host rock carbonate, up to very high values corresponding to Burdigalian seawater (Upper Marine Molasse, Miocene), which represents the last marine incursion in the region. No evidence for internally derived radiogenic Sr (clay minerals) has been found and so an external source is required. S and O isotope composition of vein celestite and pyrite can be explained by bacterial reduction of Miocene seawater sulphate. The available data set suggests the vein mineralization precipitated from descending Burdigalian seawater and not from a fluid originating in the underlying Triassic evaporites.

Cross recurrence quantification for cover song identification

There is growing evidence that nonlinear time series analysis techniques can be used to successfully characterize, classify, or process signals derived from realworld dynamics even though these are not necessarily deterministic and stationary. In the present study we proceed in this direction by addressing an important problem our modern society is facing, the automatic classification of digital information. In particular, we address the automatic identification of cover songs, i.e. alternative renditions of a previously recorded musical piece. For this purpose we here propose a recurrence quantification analysis measure that allows tracking potentially curved and disrupted traces in cross recurrence plots. We apply this measure to cross recurrence plots constructed from the state space representation of musical descriptor time series extracted from the raw audio signal. We show that our method identifies cover songs with a higher accuracy as compared to previously published techniques. Beyond the particular application proposed here, we discuss how our approach can be useful for the characterization of a variety of signals from different scientific disciplines. We study coupled Rössler dynamics with stochastically modulated mean frequencies as one concrete example to illustrate this point.

Predictability of music descriptor time series and its application to cover song detection

Intuitively, music has both predictable and unpredictable components. In this work we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e. different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.