Exploring the Factors Affecting the Solubility of Chitosan in Water

We explore the role of crystallinity and inter- or intramolecular forces in chitosan for its solubility in water and demonstrate the expansion of its solubility to a wider pH range. Due to its semicrystalline nature, derived mainly from inter- and intramolecular hydrogen bonds, chitosan is water-soluble only at pH < 6. In acidic conditions, its amino groups can be partially protonated resulting in repulsion between positively charged macrochains, thereby allowing diffusion of water molecules and subsequent solvation of macromolecules. We show that chemical disruption of chitosan crystallinity by partial re-acetylation or physical disruption caused by the addition of urea and guanidine hydrochloride broadens the pH-solubility range for this biopolymer.

Effect of pH on the antimicrobial activity and oxidative stability of oil-in-water emulsions containing caffeic acid

Antioxidant properties in food are dependent on various parameters. These include the pH value and interactions with food components, including proteins or metal ions. food components affect antioxidant stability and also influence the properties of microorganisms and their viability. This paper describes an investigation of the effect of pH on the antioxidant and antibacterial properties of caffeic acid in different media. The pH values studied, using an oil-in-water emulsion as model system, were 3, 5 (with and without phosphate buffer), and 9. Effects of mixtures of caffeic acid, bovine serum albumin (BSA), and Fe (III) on oxidative deterioration in the emulsion samples were studied. The results show that the antioxidant activity of caffeic acid was increased by the presence of BSA. This effect was pH dependent and was affected by the presence of iron Ions. Antibacterial properties were also pH dependent. The minimum concentration of caffeic acid required to inhibit some microorganisms in the pH range of 5 to 7 was determined. A concentration of 0.41% (w/w) caffeic acid was enough to inhibit the growth of some of the studied microorganisms in the pH range of 5 to 7. However, near-neutral pH concentrations higher than 0.4% were needed to inhibit some microorganisms, including Listeria monocytogenes, E. coli, and Staphylococcus aureus, in the medium.

Evaluation by volatile profile of the synergistic antioxidant effect between bovine serum albumin and virgin olive oil phenolic compounds in an oil-in-water emulsion

THE OXIDATIVE STABILITY OF OIL-IN-WATER EMULSIONS, CONTAINING BOVINE SERUM ALBUMIN (BSA) AND VIRGIN OLIVE OIL PHENOLIC COMPOUNDS, WAS STUDIED BY THE DETERMINATION OF THE FORMATION OF VOLATILE OXIDATION PRODUCTS. FOUR OIL-IN-WATER EMULSIONS WITH AND WITHOUT PHENOLS ISOLATED FROM VIRGIN OLIVE OIL AND BSA WERE PREPARED. THESE MODEL SYSTEMS WERE STORED AT 60 degrees C TO ACCELERATE LIPID OXIDATION. VOLATILE OXIDATION PRODUCTS WERE MONITORED EVERY THREE DAYS BY HEADSPACE SOLID-PHASE MICROEXTRACTION COUPLED WITH GAS CHROMATOGRAPHY. ALTHOUGH INDIVIDUALLY OLIVE OIL PHENOLIC COMPOUNDS AND BSA SHOWED A SIGNIFICANT ANTIOXIDANT ACTIVITY, THE COMBINATION OF THESE COMPONENTS SHOWED A VERY GOOD SYNERGY, QUANTIFIED AS 127%. IN FACT, THE EMULSION CONTAINING BOTH PHENOLIC COMPOUNDS AND BSA SHOWED A VERY LOW LEVEL OF OXIDATIVE DETERIORATION AFTER 45 DAYS STORAGE.

Bovine serum albumin produces a synergistic increase in the antioxidant activity of virgin olive oil phenolic compounds in oil-in-water emulsions

Virgin olive oil is valued for its flavor, but unacceptable off-flavors may develop on storage in food products containing this oil due to oxidation. The oxidative stability of oil-in-water emulsions containing bovine serum albumin (BSA) and virgin olive oil phenolic compounds was studied. Four oil-in-water emulsions with and without BSA and phenols isolated from virgin olive oil were prepared. These model systems were stored at 60 degrees C to speed up lipid oxidation. Primary and secondary oxidation products were monitored every three days. Peroxide values and conjugated diene contents were determined as measures of the primary oxidation products. p-Anisidine values and volatile compounds were determined as measures of the secondary oxidation products. This latter determination was carried out by headspace solid-phase microextraction coupled with gas chromatography. Although olive oil phenolic compounds and BSA contributed some antioxidant activity when present as individual additives, the combination of BSA with phenols in an emulsion showed 58-127% synergy, depending on which analytical method was used in the calculation. The emulsion containing phenolic compounds and BSA showed a low level of deterioration after 45 days of storage at 60 degrees C.

Solid phase microextraction of volatile oxidation compounds in oil-in-water emulsions

Headspace solid phase microextraction (HS-SPME) has been used to isolate the headspace volatiles formed during oxidation of oil-in-water emulsions. Qualitative and quantitative analyses with an internal standard were performed by GC-FID. Four sample temperatures for adsorption (30, 40, 50 and 60 C) and adsorption times in the range 10-25 min were tested to determine the conditions for the volatile concentration to reach equilibrium. The optimum conditions were at 50 C for 20 min. The method was applied to monitor changes in volatile composition during oxidation of an o/w emulsion. SPME was a simple, reproducible and sensitive method for the analysis of volatile oxidation products in oil-in-water emulsions. (c) 2004 Elsevier Ltd. All rights reserved.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

Role of Nrf2 in the regulation of CD36 and stress protein expression in murine macrophages - Activation by oxidatively modified LDL and 4-hydroxynonenal

CD36 is an important scavenger receptor mediating uptake of oxidized low- density lipoproteins ( oxLDLs) and plays a key role in foam cell formation and the pathogenesis of atherosclerosis. We report the first evidence that the transcription factor Nrf2 is expressed in vascular smooth muscle cells, and demonstrate that oxLDLs cause nuclear accumulation of Nrf2 in murine macrophages, resulting in the activation of genes encoding CD36 and the stress proteins A170, heme oxygenase- 1 ( HO- 1), and peroxiredoxin I ( Prx I). 4- Hydroxy- 2- nonenal ( HNE), derived from lipid peroxidation, was one of the most effective activators of Nrf2. Using Nrf2- deficient macrophages, we established that Nrf2 partially regulates CD36 expression in response to oxLDLs, HNE, or the electrophilic agent diethylmaleate. In murine aortic smooth muscle cells, expressing negligible levels of CD36, both moderately and highly oxidized LDL caused only limited Nrf2 translocation and negligible increases in A170, HO- 1, and Prx I expression. However, treatment of smooth muscle cells with HNE significantly enhanced nuclear accumulation of Nrf2 and increased A170, HO- 1, and Prx I protein levels. Because PPAR-gamma can be activated by oxLDLs and controls expression of CD36 in macrophages, our results implicate Nrf2 as a second important transcription factor involved in the induction of the scavenger receptor CD36 and antioxidant stress genes in atherosclerosis.

Physics-based modelling for remote sensing of snow

Remote sensing is the only practicable means to observe snow at large scales. Measurements from passive microwave instruments have been used to derive snow climatology since the late 1970’s, but the algorithms used were limited by the computational power of the era. Simplifications such as the assumption of constant snow properties enabled snow mass to be retrieved from the microwave measurements, but large errors arise from those assumptions, which are still used today. A better approach is to perform retrievals within a data assimilation framework, where a physically-based model of the snow properties can be used to produce the best estimate of the snow cover, in conjunction with multi-sensor observations such as the grain size, surface temperature, and microwave radiation. We have developed an existing snow model, SNOBAL, to incorporate mass and energy transfer of the soil, and to simulate the growth of the snow grains. An evaluation of this model is presented and techniques for the development of new retrieval systems are discussed.

Mass spectrometry imaging of glucosinolates in arabidopsis flowers and siliques

Glucosinolates are multi-functional plant secondary metabolites which play a vital role in plant defence and are, as dietary compounds, important to human health and livestock well-being. Knowledge of the tissue-specific regulation of their biosynthesis and accumulation is essential for plant breeding programs. Here, we report that in Arabidopsis thaliana, glucosinolates are accumulated differentially in specific cells of reproductive organs. Using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), distribution patterns of three selected compounds, 4-methylsulfinylbutyl (glucoraphanin), indol-3-ylmethyl (glucobrassicin), and 4-benzoyloxybutyl glucosinolates, were mapped in the tissues of whole flower buds, sepals and siliques. The results show that tissue localization patterns of aliphatic glucosinolate glucoraphanin and 4-benzoyloxybutyl glucosinolate were similar, but indole glucosinolate glucobrassicin had different localisation, indicating a possible difference in function. The high resolution images obtained by a complementary approach, cryo-SEM Energy Dispersive X-ray analysis (cryo-SEM-EDX), confirmed increased concentration of sulphur in areas with elevated amounts of glucosinolates, and allowed identifying the cell types implicated in accumulation of glucosinolates. High concentration of sulphur was found in S-cells adjacent to the phloem in pedicels and siliques, indicating the presence of glucosinolates. Moreover, both MALDI MSI and cryo-SEM-EDX analyses indicated accumulation of glucosinolates in cells on the outer surface of the sepals, suggesting that a layer of glucosinolate-accumulating epidermal cells protects the whole of the developing flower, in addition to the S-cells, which protect the phloem. This research demonstrates the high potential of MALDI MSI for understanding the cell-specific compartmentation of plant metabolites and its regulation.

Effect of ENSO phase on large-scale snow water equivalent distribution in a GCM

Understanding links between the El Nino-Southern Oscillation (ENSO) and snow would be useful for seasonal forecasting, but also for understanding natural variability and interpreting climate change predictions. Here, a 545-year run of the general circulation model HadCM3, with prescribed external forcings and fixed greenhouse gas concentrations, is used to explore the impact of ENSO on snow water equivalent (SWE) anomalies. In North America, positive ENSO events reduce the mean SWE and skew the distribution towards lower values, and vice versa during negative ENSO events. This is associated with a dipole SWE anomaly structure, with anomalies of opposite sign centered in western Canada and the central United States. In Eurasia, warm episodes lead to a more positively skewed distribution and the mean SWE is raised. Again, the opposite effect is seen during cold episodes. In Eurasia the largest anomalies are concentrated in the Himalayas. These correlations with February SWE distribution are seen to exist from the previous June-July-August (JJA) ENSO index onwards, and are weakly detected in 50-year subsections of the control run, but only a shifted North American response can be detected in the anaylsis of 40 years of ERA40 reanalysis data. The ENSO signal in SWE from the long run could still contribute to regional predictions although it would be a weak indicator only

Global estimates of snow water equivalent from passive microwave instruments: history, challenges and future developments

Snow properties have been retrieved from satellite data for many decades. While snow extent is generally felt to be obtained reliably from visible-band data, there is less confidence in the measurements of snow mass or water equivalent derived from passive microwave instruments. This paper briefly reviews historical passive microwave instruments and products, and compares the large-scale patterns from these sources to those of general circulation models and leading reanalysis products. Differences are seen to be large between the datasets, particularly over Siberia. A better understanding of the errors in both the model-based and measurement-based datasets is required to exploit both fully. Techniques to apply to the satellite measurements for improved large-scale snow data are suggested.

A 2000-year annual record of snow accumulation rates for Law Dome, East Antarctica

Accurate high-resolution records of snow accumulation rates in Antarctica are crucial for estimating ice sheet mass balance and subsequent sea level change. Snowfall rates at Law Dome, East Antarctica, have been linked with regional atmospheric circulation to the mid-latitudes as well as regional Antarctic snowfall. Here, we extend the length of the Law Dome accumulation record from 750 years to 2035 years, using recent annual layer dating that extends to 22 BCE. Accumulation rates were calculated as the ratio of measured to modelled layer thicknesses, multiplied by the long-term mean accumulation rate. The modelled layer thicknesses were based on a power-law vertical strain rate profile fitted to observed annual layer thickness. The periods 380–442, 727–783 and 1970–2009 CE have above-average snow accumulation rates, while 663–704, 933–975 and 1429–1468 CE were below average, and decadal-scale snow accumulation anomalies were found to be relatively common (74 events in the 2035-year record). The calculated snow accumulation rates show good correlation with atmospheric reanalysis estimates, and significant spatial correlation over a wide expanse of East Antarctica, demonstrating that the Law Dome record captures larger-scale variability across a large region of East Antarctica well beyond the immediate vicinity of the Law Dome summit. Spectral analysis reveals periodicities in the snow accumulation record which may be related to El Niño–Southern Oscillation (ENSO) and Interdecadal Pacific Oscillation (IPO) frequencies.

Seasonal trends in the stable isotopic composition of snow and meltwater runoff in a subarctic catchment at Okstindan, Norway

Seasonal variations in the stable isotopic composition of snow and meltwater were investigated in a sub-arctic, mountainous, but non-glacial, catchment at Okstindan in northern Norway based on analyses of delta(18)O and deltaD. Samples were collected during four field periods (August 1998; April 1999; June 1999 and August 1999) at three sites lying on an altitudinal transect (740-970 m a.s.l.). Snowpack data display an increase in the mean values of delta(18)O (increasing from a mean value of - 13.51 to - 11.49% between April and August), as well as a decrease in variability through the melt period. Comparison with a regional meteoric water line indicates that the slope of the delta(18)O - deltaD line for the snowpacks decreases over the same period, dropping from 7.49 to approximately 6.2. This change points to the role of evaporation in snowpack ablation and is confirmed by the vertical profile of deuterium excess. Snowpack seepage data, although limited, also suggest reduced values of deltaD, as might be associated with local evaporation during meltwater generation. In general, meltwaters were depleted in delta(18)O relative to the source snowpack at the peak of the melt (June), but later in the year (August) the difference between the two was not statistically significant. The diurnal pattern of isotopic composition indicates that the most depleted meltwaters coincide with the peak in temperature and, hence, meltwater production.

Evaluation of ozone and water vapor fields from the ECMWF reanalysis ERA-40 during 1991-1999 in comparison with UARS satellite and MOZAIC aircraft observations

[ 1] The European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40) ozone and water vapor reanalysis fields during the 1990s have been compared with independent satellite data from the Halogen Occultation Experiment (HALOE) and Microwave Limb Sounder (MLS) instruments on board the Upper Atmosphere Research Satellite (UARS). In addition, ERA-40 has been compared with aircraft data from the Measurements of Ozone and Water Vapour by Airbus In-Service Aircraft (MOZAIC) program. Overall, in comparison with the values derived from the independent observations, the upper stratosphere in ERA-40 has about 5 - 10% more ozone and 15 - 20% less water vapor. This dry bias in the reanalysis appears to be global and extends into the middle stratosphere down to 40 hPa. Most of the discrepancies and seasonal variations between ERA-40 and the independent observations occur within the upper troposphere over the tropics and the lower stratosphere over the high latitudes. ERA-40 reproduces a weaker Antarctic ozone hole, and of less vertical extent, than the independent observations; values in the ozone maximum in the tropical stratosphere are lower for the reanalysis. ERA-40 mixing ratios of water vapor are considerably larger than those for MOZAIC, typically by 20% in the tropical upper troposphere, and they may exceed 60% in the lower stratosphere over high latitudes. The results imply that the Brewer-Dobson circulation in the ECMWF reanalysis system is too fast, as is also evidenced by deficiencies in the way ERA-40 reproduces the water vapor "tape recorder'' signal in the tropical stratosphere. Finally, the paper examines the biases and their temporal variation during the 1990s in the way ERA-40 compares to the independent observations. We also discuss how the evaluation results depend on the instrument used, as well as on the version of the data.