Touch, grasp, deliver and control : functional cross-talk between microtubules and cell adhesions

Cross-talk between microtubule networks and sites of cell-matrix and cell-cell adhesion has profound impact on these structures and is essential for proper cell organization, polarization and motility. Components of adhesion sites can interact directly with microtubules or with proteins that specifically associate with microtubule plus ends and minus ends and in this way capture, stabilize or destabilize microtubules. In their turn, microtubules can serve as routes for delivery of structural and regulatory factors that control adhesion site turnover. In addition, the microtubule lattice or growing microtubule plus ends can serve as diffusional sinks that accumulate and scaffold regulatory molecules, thereby affecting their activity in the vicinity of adhesions. Combination of these mechanisms underlies the functional co-operation between microtubules and adhesion sites and defines their dynamic behavior.

Compensation for higher order mode coupling between inclined coupling slots and radiating slots in planar slotted waveguide arrays

Summary form only given. Geometric simplicity, efficiency and polarization purity make slot antenna arrays ideal solutions for many radar, communications and navigation applications, especially when high power, light weight and limited scan volume are priorities. Resonant arrays of longitudinal slots have a slot spacing of one-half guide wavelength at the design frequency, so that the slots are located at the standing wave peaks. Planar arrays are implemented using a number of rectangular waveguides (branch line guides), arranged side-by-side, while waveguides main lines located behind and at right angles to the branch lines excite the radiating waveguides via centered-inclined coupling slots. Planar slotted waveguide arrays radiate broadside beams and all radiators are designed to be in phase.

Cathodic corrosion of Cu substrates as a route to nanostructured Cu/M (M = Ag, Au, Pd) surfaces

The electrochemical formation of nanostructured materials is generally achieved by reduction of a metal salt onto a substrate that does not influence the composition of the deposit. In this work we report that Ag, Au and Pd electrodeposited onto Cu under conditions where galvanic replacement is not viable and hydrogen gas is evolved results in the formation of nanostructured surfaces that unexpectedly incorporate a high concentration of Cu in the final material. Under cathodic polarization conditions the electrodissolution/corrosion of Cu occurs which provides a source of ionic copper that is reduced at the surface-electrolyte interface. The nanostructured Cu/M (M = Ag, Au and Pd) surfaces are investigated for their catalytic activity for the reduction of 4 nitrophenol by NaBH4 where Cu/Ag was found to be extremely active. This work indicates that a substrate electrode can be utilized in an interesting manner t make bimetallic nanostructures with enhanced catalytic activity.

Ecosystem management along ephemeral rivers: Trading off socio-economic water supply and vegetation conservation under flood regime uncertainty

In ecosystems driven by water availability, plant community dynamics depend on complex interactions between vegetation, hydrology, and human water resources use. Along ephemeral rivers—where water availability is erratic—vegetation and people are particularly vulnerable to changes in each other's water use. Sensible management requires that water supply be maintained for people, while preserving ecosystem health. Meeting such requirements is challenging because of the unpredictable water availability. We applied information gap decision theory to an ecohydrological system model of the Kuiseb River environment in Namibia. Our aim was to identify the robustness of ecosystem and water management strategies to uncertainties in future flood regimes along ephemeral rivers. We evaluated the trade-offs between alternative performance criteria and their robustness to uncertainty to account for both (i) human demands for water supply and (ii) reducing the risk of species extinction caused by water mining. Increasing uncertainty of flood regime parameters reduced the performance under both objectives. Remarkably, the ecological objective (species coexistence) was more sensitive to uncertainty than the water supply objective. However, within each objective, the relative performance of different management strategies was insensitive to uncertainty. The ‘best’ management strategy was one that is tuned to the competitive species interactions in the Kuiseb environment. It regulates the biomass of the strongest competitor and, thus, at the same time decreases transpiration, thereby increasing groundwater storage and reducing pressure on less dominant species. This robust mutually acceptable strategy enables species persistence without markedly reducing the water supply for humans. This study emphasises the utility of ecohydrological models for resource management of water-controlled ecosystems. Although trade-offs were identified between alternative performance criteria and their robustness to uncertain future flood regimes, management strategies were identified that help to secure an ecologically sustainable water supply.

Silylation of mechanically ground kaolinite

Silylated kaolinites were synthesized at 80°C without the use of inert gas protection. The method presented started with mechanical grinding of kaolinite, followed by grafting with 3-aminopropyltriethoxysilane (APTES). The mechanical grinding treatment destroyed the ordered sheets of kaolinite, formed fine fragments and generated broken bonds (undercoordinated metal ions). These broken bonds served as new sites for the condensation with APTES. Fourier transform infrared spectroscopy (FTIR) confirmed the existence of –CH2 from APTES. 29Si cross-polarization magic-angle spinning nuclear magnetic resonance spectroscopy (29Si CP/MAS NMR) showed that the principal bonding mechanism between APTES and kaolinite fitted a tridentate silylation model (T3) with a chemical shift at 66.7 ppm. The silane loadings of the silylated samples were estimated from the mass loss obtained by TG-DTG curves. The results showed that the 6-hour ground kaolinite could be grafted with the most APTES (7.0%) using cyclohexane as solvent. The loaded amount of APTES in the silylated samples obtained in different solvents decreased in the order as: nonpolar solvent > polar solvent with low dielectric constant (toluene) > polar solvent with high dielectric constant (ethanol).

Carbon nanotube-based super nanotubes: Tunable thermal conductivity in three dimensions

Advances in nanomaterials/nanostructures offer the possibility of fabricating multifunctional materials for use in engineering applications. Carbon nanotube (CNT)-based nanostructures are a representative building block for these multifunctional materials. Based on a series of in silico studies, we investigated the possibility of tuning the thermal conductivity of a three-dimensional CNT-based nanostructure: a single-walled CNT-based super-nanotube. The thermal conductivity of the super-nanotubes was shown to vary with different connecting carbon rings and super-nanotubes with longer constituent single-walled CNTs and larger diameters had a smaller thermal conductivity. The inverse of the thermal conductivity of the super-nanotubes showed a good linear relationship with the inverse of the length. The thermal conductivity was approximately proportional to the inverse of the temperature, but was insensitive to the axial strain as a result of the Poisson ratio. These results provide a fundamental understanding of the thermal conductivity of the super-nanotubes and will guide their future design/fabrication and engineering applications.

Effect of covalent functionalisation on thermal transport across graphene-polymer interfaces

This paper is concerned with the interfacial thermal resistance for polymer composites reinforced by various covalently functionalised graphene. By using molecular dynamics simulations, the obtained results show that the covalent functionalisation in graphene plays a significant role in reducing the graphene-paraffin interfacial thermal resistance. This reduction is dependent on the coverage and type of functional groups. Among the various functional groups, butyl is found to be the most effective in reducing the interfacial thermal resistance, followed by methyl, phenyl and formyl. The other functional groups under consideration such as carboxyl, hydroxyl and amines are found to produce negligible reduction in the interfacial thermal resistance. For multilayer graphene with a layer number up to four, the interfacial thermal resistance is insensitive to the layer number. The effects of the different functional groups and the layer number on the interfacial thermal resistance are also elaborated using the vibrational density of states of the graphene and the paraffin matrix. The present findings provide useful guidelines in the application of functionalised graphene for practical thermal management.

Progressive dysgraphia in a case of posterior cortical atrophy

Dysgraphia (agraphia) is a common feature of posterior cortical atrophy (PCA). However, detailed analyses of these spelling and writing impairments are infrequently conducted. LM is a 59-year-old woman with dysgraphia associated with PCA. She presented with a two-year history of decline in her writing and dressmaking skills. A 3D T1-weighted MRI scan confirmed selective bi-parietal atrophy, with relative sparing of the hippocampi and other cortical regions. Analyses of LM's preserved and impaired spelling abilities indicated mild physical letter distortions and a significant spelling deficit characterised by letter substitutions, insertions, omissions, and transpositions that was systematically sensitive to word length while insensitive to real word versus nonword category, word frequency, regularity, imagery, grammatical class and ambiguity. Our findings suggest a primary graphemic buffer disorder underlies LM's spelling errors, possibly originating from disruption to the operation of a fronto-parietal network implicated in verbal working memory.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.