Electrochemical sensing of 2D condensation in amyloid peptides

The interfacial behavior of the model amyloid peptide octamer YYKLVFFC (peptide 1) and two other amyloid peptides YEVHHQKLVFF (peptide 2) and KKLVFFA (peptide 3) at the metal|aqueous solution interface was studied by voltammetric and constant current chronopotentiometric stripping (CPS). All three peptides are adsorbed in a wide potential range and exhibit different interfacial organizations depending on the electrode potential. At the least negative potentials, chemisorption of peptide 1 occurs through the formation of a metal sulfur bond. This bond is broken close to −0.6 V. The peptide undergoes self-association at more negative potentials, leading to the formation of a “pit” characteristic of a 2D condensed film. Under the same conditions the other peptides do not produce such a pit. Formation of the 2D condensed layer in peptide 1 is supported by the time, potential and temperature dependences of the interfacial capacity and it is shown that presence of the 2D layer is reflected by the peptide CPS signals due to the catalytic hydrogen evolution. The ability of peptide 1 to form the potential-dependent 2D condensed layer has been reported neither for any other peptide nor for any protein molecule. This ability might be related to the well-known oligomerization and aggregation of Alzheimer amyloid peptides.

Sea surface temperature climate change initiative: alternative image classification algorithms for sea-ice affected oceans

We present a Bayesian image classification scheme for discriminating cloud, clear and sea-ice observations at high latitudes to improve identification of areas of clear-sky over ice-free ocean for SST retrieval. We validate the image classification against a manually classified dataset using Advanced Along Track Scanning Radiometer (AATSR) data. A three way classification scheme using a near-infrared textural feature improves classifier accuracy by 9.9 % over the nadir only version of the cloud clearing used in the ATSR Reprocessing for Climate (ARC) project in high latitude regions. The three way classification gives similar numbers of cloud and ice scenes misclassified as clear but significantly more clear-sky cases are correctly identified (89.9 % compared with 65 % for ARC). We also demonstrate the poetential of a Bayesian image classifier including information from the 0.6 micron channel to be used in sea-ice extent and ice surface temperature retrieval with 77.7 % of ice scenes correctly identified and an overall classifier accuracy of 96 %.

Cloud clearing techniques over land for land surface temperature retrieval from the Advanced Along Track Scanning Radiometer

We present five new cloud detection algorithms over land based on dynamic threshold or Bayesian techniques, applicable to the Advanced Along Track Scanning Radiometer (AATSR) instrument and compare these with the standard threshold based SADIST cloud detection scheme. We use a manually classified dataset as a reference to assess algorithm performance and quantify the impact of each cloud detection scheme on land surface temperature (LST) retrieval. The use of probabilistic Bayesian cloud detection methods improves algorithm true skill scores by 8-9 % over SADIST (maximum score of 77.93 % compared to 69.27 %). We present an assessment of the impact of imperfect cloud masking, in relation to the reference cloud mask, on the retrieved AATSR LST imposing a 2 K tolerance over a 3x3 pixel domain. We find an increase of 5-7 % in the observations falling within this tolerance when using Bayesian methods (maximum of 92.02 % compared to 85.69 %). We also demonstrate that the use of dynamic thresholds in the tests employed by SADIST can significantly improve performance, applicable to cloud-test data to provided by the Sea and Land Surface Temperature Radiometer (SLSTR) due to be launched on the Sentinel 3 mission (estimated 2014).

Global climatology of surface water temperatures of large lakes by remote sensing

Lake surface water temperatures (LSWTs) of 246 globally distributed large lakes were derived from Along-Track Scanning Radiometers (ATSR) for the period 1991–2011. The climatological cycles of mean LSWT derived from these data quantify on a global scale the responses of large lakes' surface temperatures to the annual cycle of forcing by solar radiation and the ambient meteorological conditions. LSWT cycles reflect the twice annual peak in net solar radiation for lakes between 1°S to 12°N. For lakes without a lake-mean seasonal ice cover, LSWT extremes exceed air temperatures by 0.5–1.7 °C for maximum and 0.7–1.9 °C for minimum temperature. The summer maximum LSWTs of lakes from 25°S to 35°N show a linear decrease with increasing altitude; −3.76 ± 0.17 °C km−1 (inline image = 0.95), marginally lower than the corresponding air temperature decrease with altitude −4.15 ± 0.24 °C km−1 (inline image = 0.95). Lake altitude of tropical lakes account for 0.78–0.83 (inline image) of the variation in the March to June LSWT–air temperature differences, with differences decreasing by 1.9 °C as the altitude increases from 500 to 1800 m above sea level (a.s.l.) We define an ‘open water phase’ as the length of time the lake-mean LSWT remains above 4 °C. There is a strong global correlation between the start and end of the lake-mean open water phase and the spring and fall 0 °C air temperature transition days, (inline image = 0.74 and 0.80, respectively), allowing for a good estimation of timing and length of the open water phase of lakes without LSWT observations. Lake depth, lake altitude and distance from coast further explain some of the inter-lake variation in the start and end of the open water phase.

Exploiting existing ground-based remote sensing networks to improve high-resolution weather forecasts

A new generation of high-resolution (1 km) forecast models promises to revolutionize the prediction of hazardous weather such as windstorms, flash floods, and poor air quality. To realize this promise, a dense observing network, focusing on the lower few kilometers of the atmosphere, is required to verify these new forecast models with the ultimate goal of assimilating the data. At present there are insufficient systematic observations of the vertical profiles of water vapor, temperature, wind, and aerosols; a major constraint is the absence of funding to install new networks. A recent research program financed by the European Union, tasked with addressing this lack of observations, demonstrated that the assimilation of observations from an existing wind profiler network reduces forecast errors, provided that the individual instruments are strategically located and properly maintained. Additionally, it identified three further existing European networks of instruments that are currently underexploited, but with minimal expense they could deliver quality-controlled data to national weather services in near–real time, so the data could be assimilated into forecast models. Specifically, 1) several hundred automatic lidars and ceilometers can provide backscatter profiles associated with aerosol and cloud properties and structures with 30-m vertical resolution every minute; 2) more than 20 Doppler lidars, a fairly new technology, can measure vertical and horizontal winds in the lower atmosphere with a vertical resolution of 30 m every 5 min; and 3) about 30 microwave profilers can estimate profiles of temperature and humidity in the lower few kilometers every 10 min. Examples of potential benefits from these instruments are presented.

Independent uncertainty estimates for coefficient based sea surface temperature retrieval from the Along-Trck Scanning Radiometer instruments

We establish a methodology for calculating uncertainties in sea surface temperature estimates from coefficient based satellite retrievals. The uncertainty estimates are derived independently of in-situ data. This enables validation of both the retrieved SSTs and their uncertainty estimate using in-situ data records. The total uncertainty budget is comprised of a number of components, arising from uncorrelated (eg. noise), locally systematic (eg. atmospheric), large scale systematic and sampling effects (for gridded products). The importance of distinguishing these components arises in propagating uncertainty across spatio-temporal scales. We apply the method to SST data retrieved from the Advanced Along Track Scanning Radiometer (AATSR) and validate the results for two different SST retrieval algorithms, both at a per pixel level and for gridded data. We find good agreement between our estimated uncertainties and validation data. This approach to calculating uncertainties in SST retrievals has a wider application to data from other instruments and retrieval of other geophysical variables.

Sampling uncertainty in gridded sea surface temperature products and Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data

Sea surface temperature (SST) data are often provided as gridded products, typically at resolutions of order 0.05 degrees from satellite observations to reduce data volume at the request of data users and facilitate comparison against other products or models. Sampling uncertainty is introduced in gridded products where the full surface area of the ocean within a grid cell cannot be fully observed because of cloud cover. In this paper we parameterise uncertainties in SST as a function of the percentage of clear-sky pixels available and the SST variability in that subsample. This parameterisation is developed from Advanced Along Track Scanning Radiometer (AATSR) data, but is applicable to all gridded L3U SST products at resolutions of 0.05-0.1 degrees, irrespective of instrument and retrieval algorithm, provided that instrument noise propagated into the SST is accounted for. We also calculate the sampling uncertainty of ~0.04 K in Global Area Coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) products, using related methods.

Transcription of the Hsp30, Hsp70, and Hsp90 heat shock protein genes is modulated by the PalA protein in response to acid pH-sensing in the fungus Aspergillus nidulans

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The role of hierarchical morphologies in the superior gas sensing performance of CuO-based chemiresistors

The development of gas sensors with innovative designs and advanced functional materials has attracted considerable scientific interest given their potential for addressing important technological challenges. This work presents new insight towards the development of high-performance p-type semiconductor gas sensors. Gas sensor test devices, based on copper (II) oxide (CuO) with innovative and unique designs (urchin-like, fiber-like, and nanorods), are prepared by a microwave-assisted synthesis method. The crystalline composition, surface area, porosity, and morphological characteristics are studied by X-ray powder diffraction, nitrogen adsorption isotherms, field-emission scanning electron microscopy and high-resolution transmission electron microscopy. Gas sensor measurements, performed simultaneously on multiple samples, show that morphology can have a substantial influence on gas sensor performance. An assembly of urchin-like structures is found to be most effective for hydrogen detection in the range of parts-per-million at 200 °C with 300-fold larger response than the previously best reported values for semiconducting CuO hydrogen gas sensors. These results show that morphology plays an important role in the gas sensing performance of CuO and can be effectively applied in the further development of gas sensors based on p-type semiconductors. High-performance gas sensors based on CuO hierarchical morphologies with in situ gas sensor comparison are reported. Urchin-like morphologies with high hydrogen sensitivity and selectivity that show chemical and thermal stability and low temperature operation are analyzed. The role of morphological influences in p-type gas sensor materials is discussed. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Soft-chemical synthesis, characterization and humidity sensing behavior of WO3/TiO2 nanopowders

Tungsten oxide/titania (WO3/TiO2) nanopowders were synthesized by the polymeric precursor method which varied the WO3 content between 0 and 10 mol%. The powders were thermally treated in a conventional furnace and their structural, microstructural and electric properties were evaluated by X-ray diffraction (XRD), Raman spectrometry, N 2 physisorption, NH3 chemisorption, temperature-programmed reduction (TPR), X-ray absorption near-edge spectroscopy (XANES) in situ XANES and extended X-ray absorption fine structure spectroscopy (EXAFS) and transmission electron microscopy (TEM). XRD and Raman spectrometry confirmed the homogeneous distribution of an amorphous WO3 phase in the TiO 2 matrix which stabilized the anatase phase through the generation of [TiO5·V0] or [TiO5·V 0] complex sites. Conventional TPR-H2 (temperature programmed reduction) along with XANES TPR-H2 and XANES TPR-EtOH showed that WO3/TiO2 sample reduction occurs through the formation of these complex clusters. Moreover, the addition of WO3 promoted an increase in the surface acidity of doped samples as revealed by NH3 chemisorption. The WO3/TiO2 bulk-ceramic samples were further used to estimate their potential application in a humidity sensor in the range of 15-85% relative humidity. Probable reasons that lead to the different humidity sensor responses of samples were given based on the structural and surface characterizations. Correlation between the sensing performance of the sensor and its structural features are also discussed. Although all samples responded as a humidity sensor, the W2T sample (2 mol% added WO3) excelled for sensitivity due to the increase in acid sites, optimum mean pore size and pore size distribution. © 2013 Elsevier B.V.

Development of technologies and methods for monitoring the spatial variability of air temperature in greenhouse environment

Climatic factors directly influence growth and productivity of plants inside greenhouses, where temperature can be considered one of the major parameter in this context. Thus, the aim of this research was to develop a low cost device for thermal sensing and data acquisition, and use it in data collection and analysis of spatial variability of temperature inside a greenhouse with tropical climate. The developed equipment for thermal measurements showed a high degree of accuracy and fast responses in measurements, proving its efficiency. The data analysis interpretations were made from the elaborations of variograms and of tridimensional maps generated by a geostatistical software. The processed data analysis presented that a greenhouse without thermal control has spatial variations of air temperature, both in the sampled horizontals layers as in the three analyzed vertical columns, presenting variations of up to 3.6 ºC in certain times.

Evaluating the performance of polypyrrole nanowires on the electrochemical sensing of ammonia in solution

This work encompasses the direct electrodeposition of polypyrrole nanowires onto Au substrates using different electrochemical techniques: normal pulse voltammetry (NPV) and constant potential method with the aim in applying these films for the first time in ammonia sensing in solution. The performance of these nanowire-based sensors are compared and evaluated in terms of: film morphology (analyzed with scanning electron microscopy); their sensitivity towards ammonia; electrochemical and contact angle measurements. For nanowires prepared by NPV, the sensitivity towards ammonia increases with increasing amount of electrodeposited polypyrrole, as expected due to the role of polypyrrole as electrochemical transducer for ammonia oxidation. On the other hand, nanowires prepared potentiostatically displayed an unexpected opposite behavior, attributed to the lower conductivity of longer polypyrrole nanowires obtained through this technique. These results evidenced that the analytical and physico-chemical features of nanostructured sensors can differ greatly from those of their conventional bulky analogous. (C) 2012 Elsevier B.V. All rights reserved.

Interannual variation in commercial oyster (Crassostrea gigas) farming in the sea (Florianopolis, Brazil, 27 degrees 44 ' S; 48 degrees 33 ' W) in relation to temperature, chlorophyll a and associated oceanographic conditions

Aquaculture of filter-feeding bivalve mollusks involves the fruitful conversion of marine particulate organic matter into premium protein of high nutritive value. Culture performance of bivalves is largely dependent on hydrological conditions and directly affected by e. g. temperature and chlorophyll levels. Accordingly, these parameters may be related with seasonality but also with oceanographic features combined with climate events. Yields of Pacific cupped oyster (Crassostrea gigas) reared at commercial procedures in suspended structures (long-lines) in a sheltered bay in Southern Brazil (Santa Catarina State, 27S 43'; 48 W 30') were evaluated in relation to local environmental conditions: sea surface temperature, chlorophyll a concentration, and associate effects of cold fronts events and El Nino and La Nina periods. Outputs from four consecutive commercial crop years were analyzed (2005/06, 2006/07, 2007/08, 2008/09) in terms of oyster survival and development time during the following grow-out phases of the culture cycle: seed to juvenile, juvenile to adult, adult to marketable. Since culture management and genetics were standardized significant differences verified among crop performance could be mostly related to environmental effects. Time series of temperature and chlorophyll a (remote sensing data) from crop periods displayed significant seasonal and interannual variation. As expected, performance during initial grow-out stages (seed to juvenile) was critical for final crop yield. Temperature was the main factor affecting survival in these initial stages with a trend of negative correlation, though not statistically significant. On the other hand, oyster development rate was significantly and positively affected by chlorophyll a concentration. Chlorophyll a values could be increased by upwelled cold nutrient-rich South Atlantic Central Water (SACW, related to predominant Northern winds) though further dependent on occurrence of Southern winds (cold fronts) to assist seawater penetration into the sheltered farming area. Lower salinity nutrient-rich northward drifted waters from La Plata River discharge may also result in chlorophyll a rise in the farming area. The El Nino period (July 2006 to February 2007) coincided with lower chlorophyll a levels in the farming site that may be related to both decreased number of cold fronts as well as predominance of Northern winds that retain northward spreading of La Plata River discharge waters. In contrast, the La Nina period (August 2007 to June 2008) corresponded to higher chlorophyll a values in the farming area by both upwelling of SACW and penetration of La Plata River discharge water assisted by increased occurrence of Southern winds and cold fronts. The recognition of the potentially changing climate and effects upon the environment will be an important step in planning future development of bivalve aquaculture.