Carbon nanotubes for biological and biomedical applications

Ever since the discovery of carbon nanotubes, researchers have been exploring their potential in biological and biomedical applications. The recent expansion and availability of chemical modification and bio-functionalization methods have made it possible to generate a new class of bioactive carbon nanotubes which are conjugated with proteins, carbohydrates, or nucleic acids. The modification of a carbon nanotube on a molecular level using biological molecules is essentially an example of the 'bottom-up' fabrication principle of bionanotechnology. The availability of these biomodified carbon nanotube constructs opens up an entire new and exciting research direction in the field of chemical biology, finally aiming to target and to alter the cell's behaviour at the subcellular or molecular level. This review covers the latest advances of bio-functionalized carbon nanotubes with an emphasis on the development of functional biological nano-interfaces. Topics that are discussed herewith include methods for biomodification of carbon nanotubes, the development of hybrid systems of carbon nanotubes and biomolecules for bioelectronics, and carbon nanotubes as transporters for a specific delivery of peptides and/or genetic material to cells. All of these current research topics aim at translating these biotechnology modified nanotubes into potential novel therapeutic approaches.

Biological, physical and chemical properties at the Subtropical Shelf Front Zone in the SW Atlantic Continental Shelf

The physical aspects of the Subtropical Shelf Front (STSF) for the Southwest Atlantic Continental Shelf were previously described. However, only scarce data on the biology of the front is available in the literature. The main goal of this paper is to describe the physical, chemical and biological properties of the STSF found in winter 2003 and summer 2004. A cross-section was established at the historically determined location of the STSF. Nine stations were sampled in winter and seven in summer. Each section included a series of conductivity-temperature-depth (CTD) stations where water samples from selected depths were filtered for nutrient determination. Surface samples were taken for chlorophyll a (Chl-a) determination and plankton net tows carried out above and below the pycnocline. Results revealed that winter was marked by an inner-shelf salinity front and that the STSF was located on the mid-shelf The low salinity waters in the inner-shelf indicated a strong influence of freshwater, with high silicate (72 mu M), suspended matter (45 mg l(-1)), phosphate (2.70 mu M) and low nitrate (1.0 mu M) levels. Total dissolved nitrogen was relatively high (22.98 mu M), probably due to the elevated levels of organic compound contribution close to the continental margin. Surface Chl-a concentration decreased from coastal well-mixed waters, where values up to 8.0 mg m(-3) were registered, to offshore waters. Towards the open ocean, high subsurface nutrients values were observed, probably associated to South Atlantic Central Waters (SACW). Zooplankton and ichthyoplankton abundance followed the same trend; three different groups associated to the inner-, mid- and outer-shelf region were identified. During summer, diluted waters extended over the shelf to join the STSF in the upper layer; the concentration of inorganic nutrients decreased in shallow waters; however, high values were observed between 40 and 60 m and in deep offshore waters. Surface Chl-a ranged 0.07-1.5 mg m(-3); winter levels were higher. Three groups of zoo and ichthyoplankton, separated by the STSF, were also identified. Results of the study performed suggest that the influence of freshwater was stronger during winter and that abundance distribution of Chl-a, copepods and ichthyoplankton was related to the Plata Plume Waters (PPW), rather than to the presence of the STSF. During summer, when the presence of freshwater decreases, plankton interactions seem to take place in the STSF. (C) 2008 Elsevier Ltd. All rights reserved.

Biological, physical and chemical characteristics of water and sediment samples from the Volga River delta, northern Caspian

Based on the data of synchronous observations of hydrophysical and biogeochemical parameters in the near-mouth and shallow-water areas of the northern Caspian in 2000-2001, the scale of spatiotemporal variability in the following characteristics of the water-bottom system was estimated (1) flow velocity and direction within vortex structures formed by the combined effect of wind, discharge current, and the presence of higher aquatic plants; (2) dependence of the spatial distribution of the content and composition of suspended particulate matter on the hydrodynamic regime of waters and development of phytoplankton; (3) variations in the grain-size, petrographic, mineralogical, and chemical compositions of the upper layer of bottom sediments at several sites in the northern Caspian related to the particular local combination of dominant natural processes; and (4) limits of variability in the group composition of humus compounds in bottom sediments. The acquired data are helpful in estimating the geochemical consequences of a sea level rise and during the planning of preventive environmental protection measures in view of future oil and gas recovery in this region.

Attenuated T2 relaxation by mutual cancellation of dipole–dipole coupling and chemical shift anisotropy indicates an avenue to NMR structures of very large biological macromolecules in solution

Fast transverse relaxation of 1H, 15N, and 13C by dipole-dipole coupling (DD) and chemical shift anisotropy (CSA) modulated by rotational molecular motions has a dominant impact on the size limit for biomacromolecular structures that can be studied by NMR spectroscopy in solution. Transverse relaxation-optimized spectroscopy (TROSY) is an approach for suppression of transverse relaxation in multidimensional NMR experiments, which is based on constructive use of interference between DD coupling and CSA. For example, a TROSY-type two-dimensional 1H,15N-correlation experiment with a uniformly 15N-labeled protein in a DNA complex of molecular mass 17 kDa at a 1H frequency of 750 MHz showed that 15N relaxation during 15N chemical shift evolution and 1HN relaxation during signal acquisition both are significantly reduced by mutual compensation of the DD and CSA interactions. The reduction of the linewidths when compared with a conventional two-dimensional 1H,15N-correlation experiment was 60% and 40%, respectively, and the residual linewidths were 5 Hz for 15N and 15 Hz for 1HN at 4°C. Because the ratio of the DD and CSA relaxation rates is nearly independent of the molecular size, a similar percentagewise reduction of the overall transverse relaxation rates is expected for larger proteins. For a 15N-labeled protein of 150 kDa at 750 MHz and 20°C one predicts residual linewidths of 10 Hz for 15N and 45 Hz for 1HN, and for the corresponding uniformly 15N,2H-labeled protein the residual linewidths are predicted to be smaller than 5 Hz and 15 Hz, respectively. The TROSY principle should benefit a variety of multidimensional solution NMR experiments, especially with future use of yet somewhat higher polarizing magnetic fields than are presently available, and thus largely eliminate one of the key factors that limit work with larger molecules.

Gas production during the pyrolysis and gasification of biological and physico-chemical sludges from oil refinery

Pyrolysis and gasification of two different sludges coming from a Spanish refinery have been performed at different experimental conditions. A physico-chemical (PC) and a biological (BIO) sludge have been studied. Runs at different heating rates (approx. 4 and 10 K/s) and with different contact time between gases and decomposed sludge have been performed. In general, the ratio H2/CO is higher in pyrolytic runs. The highest ratio is obtained in the pyrolysis at low heating rate and parallel flow, using both sludges. The maximum emission of CO, i.e. the worst combustion conditions, is given in the runs where contact time is minimized and at high heating rates.

Collective protection against chemical, biological, and radiological warfare agents.

"1 Oct 58."

Field behavior of chemical, biological, and radiological agents.

April 1969

Nutritional and chemical characterization of food products enriched with biological produced fruits of Vaccinum myrtillus L

Vaccinum myrtillus L. belongs to Ericaceae family, being commonly known for its sweet small fruits: the blueberries. Widely consumed in fresh, these fruits are also used in jams and marmalades due to their digestive and hypoglycemic properties and also due to the presence of several bioactive compounds [!]. Therefore, it has become a very appealing matrix in the development of functional products that, beyond their nutritional properties, will add a long-term beneficial physiological/health effect [2]. In the present work, three novel blueberry based products developed by RBR Foods Company (Portugal), were characterized in terms of their nutritional and chemical properties: carbohydrates, ash, proteins, fat and energetic value (following official methods of food analysis), fatty acids profile (by CG-FID), soluble sugars (by HPLCRI), organic acids (by HPLC-DAD) and tocopherols (by HPLC-fluorescence). The products result from a mixture of the fruits with rose petals (PI), marigold petals (P2) and apple and goji berries (P3). The blueberry fruits were used as control sample. The nutritional profile of the novel products was very similar to the control sample: the carbohydrates were the most abundant macronutrient, followed by proteins and total fat. Regarding sugars, fructose, glucose and sucrose were identified in all the samples. P 1 and P2 didn't show significant differences in comparison to the control, however, P3 revealed a lower concentration of sugars. In terms of fatty acids composition, all the studied samples presented higher contents in polyunsaturated fatty acids, especially due to the contribution of linoleic and alinolenic acids. The results of tocopherols revealed that the control sample only presented two isoforrns of tocopherols, a- and y-tocopherol, being the same observed in P3. However, P 1 revealed the presence of all the isoforrns of tocopherols, while P2 was lacking otocopherol; which is related with the contribution of rose and marigold petals, respectively. The a-tocopherol isoforrn was the most abundant in all the studied samples. Overall, this work contributed to the nutritional characterization of novel blueberry based products and is a part of a wider project that aims the detailed study of these products, namely their potential to be used as functional foods.

Stochastic chemical kinetics and the quasi-steady-state assumption : application to the stochastic simulation algorithm and chemical master equation

Recently the application of the quasi-steady-state approximation (QSSA) to the stochastic simulation algorithm (SSA) was suggested for the purpose of speeding up stochastic simulations of chemical systems that involve both relatively fast and slow chemical reactions [Rao and Arkin, J. Chem. Phys. 118, 4999 (2003)] and further work has led to the nested and slow-scale SSA. Improved numerical efficiency is obtained by respecting the vastly different time scales characterizing the system and then by advancing only the slow reactions exactly, based on a suitable approximation to the fast reactions. We considerably extend these works by applying the QSSA to numerical methods for the direct solution of the chemical master equation (CME) and, in particular, to the finite state projection algorithm [Munsky and Khammash, J. Chem. Phys. 124, 044104 (2006)], in conjunction with Krylov methods. In addition, we point out some important connections to the literature on the (deterministic) total QSSA (tQSSA) and place the stochastic analogue of the QSSA within the more general framework of aggregation of Markov processes. We demonstrate the new methods on four examples: Michaelis–Menten enzyme kinetics, double phosphorylation, the Goldbeter–Koshland switch, and the mitogen activated protein kinase cascade. Overall, we report dramatic improvements by applying the tQSSA to the CME solver.

Sources of ultrafine particles and chemical species along a traffic corridor : comparison of the results from two receptor models

Particulate matter is common in our environment and has been linked to human health problems particularly in the ultrafine size range. A range of chemical species have been associated with particulate matter and of special concern are the hazardous chemicals that can accentuate health problems. If the sources of such particles can be identified then strategies can be developed for the reduction of air pollution and consequently, the improvement of the quality of life. In this investigation, particle number size distribution data and the concentrations of chemical species were obtained at two sites in Brisbane, Australia. Source apportionment was used to determine the sources (or factors) responsible for the particle size distribution data. The apportionment was performed by Positive Matrix Factorisation (PMF) and Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS), and the results were compared with information from the gaseous chemical composition analysis. Although PCA/APCS resolved more sources, the results of the PMF analysis appear to be more reliable. Six common sources identified by both methods include: traffic 1, traffic 2, local traffic, biomass burning, and two unassigned factors. Thus motor vehicle related activities had the most impact on the data with the average contribution from nearly all sources to the measured concentrations higher during peak traffic hours and weekdays. Further analyses incorporated the meteorological measurements into the PMF results to determine the direction of the sources relative to the measurement sites, and this indicated that traffic on the nearby road and intersection was responsible for most of the factors. The described methodology which utilised a combination of three types of data related to particulate matter to determine the sources could assist future development of particle emission control and reduction strategies.

Investigations of the physical and chemical properties of ambient fine particles in urban schools

This thesis reports a comprehensive study on the physical and chemical properties of airborne particles in Brisbane, especially around schools. The sources and potential toxicity of the particles were identified, enabling an assessment of the contributing factors to children's exposure at school. The results from this thesis give a quantitative estimate of the range of airborne particles that children are exposed to at urban schools with different traffic conditions.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.