Cell adhesion molecule uvomorulin expression in human breast cancer cell lines : relationship to morphology and invasive capacities

Loss of cell-cell adhesion in carcinoma cells may be an important step in the acquisition of an invasive, metastatic phenotype. We have examined the expression of the epithelial-specific cell adhesion molecule uvomorulin (E-cadherin, cell-CAM 120/80, L-CAM) in human breast cancer cell lines. We find that fibroblastoid, highly invasive, vimentin-expressing breast cancer cell lines do not express uvomorulin. Of the more epithelial-appearing, less invasive, keratin-expressing breast cancer cell lines, some express uvomorulin, and some do not. We examined the morphologies of the cell lines in the reconstituted basement membrane matrix Matrigel and measured the ability of the cells to traverse a Matrigel-coated filter as in vitro models for detachment of carcinoma cells from neighboring cells and invasion through basement membrane into surrounding tissue. Colonies of uvomorulin-positive cells have a characteristic fused appearance in Matrigel, whereas uvomorulin-negative cells appear detached. Cells which are uvomorulin negative and vimentin positive have a stellate morphology in Matrigel. We show that uvomorulin is responsible for the fused colony morphology in Matrigel since treatment of uvomorulin-positive MCF-7 cells with an antibody to uvomorulin caused the cells to detach from one another but did not induce invasiveness in these cells, as measured by their ability to cross a Matrigel-coated polycarbonate filter in a modified Boyden chamber assay. Two uvomorulin-negative, vimentin-negative cell lines are also not highly invasive as measured by this assay. We suggest that loss of uvomorulin-mediated cell-cell adhesion may be one of many changes involved in the progression of a carcinoma cell to an invasive phenotype.

A benzothiadiazole end capped donor–acceptor based small molecule for organic electronics

A benzothiadiazole end-capped small molecule 3,6-bis(5-(benzo[c][1,2,5] thiadiazol-4-yl)thiophen-2-yl)-2,5-bis(2-butyloctyl)pyrrolo[3,4-c]pyrrole-1, 4(2H,5H)-dione (BO-DPP-BTZ) using a fused aromatic moiety DPP (at the centre) is designed and synthesized. BO-DPP-BTZ is a donor-acceptor-donor (D-A-D) structure which possesses a band gap of 1.6 eV and exhibits a strong solid state ordering inferred from ∼120 nm red shift of the absorption maxima from solution to thin film. Field-effect transistors utilizing a spin coated thin film of BO-DPP-BTZ as an active layer exhibited a hole mobility of 0.06 cm 2 V-1 s-1. Solution-processed bulk heterojunction organic photovoltaics employing a blend of BO-DPP-BTZ and [70]PCBM demonstrated a power conversion efficiency of 0.9%.

Effects of artificial shade on attack by the mahogany shoot borer, Hypsipyla robusta (Moore)

Swietenia macrophylla King (Meliaceae: Swietenioideae) provides one of the premier timbers of the world. The mahogany shoot borer Hypsipyla robusta Moore (Lepidoptera: Pyralidae) is an economically important pest of S. macrophylla throughout Asia, Africa and the Pacific. No viable method of controlling this pest is known. Previous observations have suggested that the presence of overhead shade may reduce attack by H. robusta, but this has not been investigated experimentally. This research was therefore designed to assess the influence of light availability on shoot-borer attack on S. macrophylla, by establishing seedlings under three different artificial shade regimes, then using these seedlings to test oviposition preference of adult moths, neonate larval survival and growth and development of shoot borer larvae. Oviposition preference of shoot borer moths was tested on leaves from seedlings grown under artificial shade for 63 weeks. A significant difference in choice was recorded between treatments, with 27.4 ± 1.5 eggs laid under high shade and 87.1 ± 1.8 under low shade. Neonate larval survival on early flushing leaflets of S. macrophylla did not differ significantly between shade treatments. Larval growth rate, estimated by measuring daily frass width, was significantly higher for those larvae fed on seedlings from the high and medium shade treatments (0.1 mm/day), than the low shade treatment (0.06 mm/day). In laboratory-reared larvae, the total mass of frass produced was significantly higher in the high shade treatment (0.4 g) than under the low shade treatment (0.2 g). Longer tunnel lengths were bored by larvae in plants grown under high shade (12.0 ± 2.4 cm) than under low shade (7.07 ± 1.9 cm). However, pupal mass under low shade was 48% higher than that under the high shade treatment, suggesting that plants grown under high shade were of lower nutritional quality for shoot borer larvae. These results indicate that shading of mahogany seedlings may reduce the incidence of shoot borer attack, by influencing both oviposition and larval development. The establishment of mahogany under suitable shade regimes may therefore provide a basis for controlling shoot borer attack using silvicultural approaches.

Book review : "Artificial Hells: participatory art and the politics of spectatorship by Claire Bishop"

In the years since Nicolas Bourriaud’s Relational Aesthetics (1998) was published, a plethora of books (Shannon Jackson’s Social Works: Performing Art, Supporting Publics [2011], Nato Thompson’s Living as Form: Socially Engaged Art from 1991–2011 [2011], Grant Kester’s Conversation Pieces: Community and Communication in Modern Art [2004], Pablo Helguera’s Education for Socially Engaged Art: A Material and Techniques Handbook [2011]), conferences and articles have surfaced creating a rich and textured discourse that has responded to, critiqued and reconfigured the proposed social utopias of Bourriaud’s aesthetics. As a touchstone for this emerging discourse, Relational Aesthetics outlines in a contemporary context the plethora of social and process-based art forms that took as their medium the ‘social’. It is, however, Clare Bishop’s book Artificial Hells: Participatory Art and the Politics of Spectatorship (Verso), that offers a deeper art historical and theoretically considered rendering of this growing and complicated form of art, and forms a central body of work in this broad constellation of writings about participatory art, or social practice art/socially engaged art (SEA), as it is now commonly known...

The adhesion molecule L1 regulates transendothelial migration and trafficking of dendritic cells

The adhesion molecule L1, which is extensively characterized in the nervous system, is also expressed in dendritic cells (DCs), but its function there has remained elusive. To address this issue, we ablated L1 expression in DCs of conditional knockout mice. L1-deficient DCs were impaired in adhesion to and transmigration through monolayers of either lymphatic or blood vessel endothelial cells, implicating L1 in transendothelial migration of DCs. In agreement with these findings, L1 was expressed in cutaneous DCs that migrated to draining lymph nodes, and its ablation reduced DC trafficking in vivo. Within the skin, L1 was found in Langerhans cells but not in dermal DCs, and L1 deficiency impaired Langerhans cell migration. Under inflammatory conditions, L1 also became expressed in vascular endothelium and enhanced transmigration of DCs, likely through L1 homophilic interactions. Our results implicate L1 in the regulation of DC trafficking and shed light on novel mechanisms underlying transendothelial migration of DCs. These observations might offer novel therapeutic perspectives for the treatment of certain immunological disorders.

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

Identification of New Zealand bats in flight from analysis of echolocation calls by artificial neural networks.

Time-expanded and heterodyned echolocation calls of the New Zealand long-tailed Chalinolobus tuberculatus and lesser short-tailed bat Mystacina tuberculata were recorded and digitally analysed. Temporal and spectral parameters were measured from time-expanded calls and power spectra generated for both time-expanded and heterodyned calls. Artificial neural networks were trained to classify the calls of both species using temporal and spectral parameters and power spectra as input data. Networks were then tested using data not previously seen. Calls could be unambiguously identified using parameters and power spectra from time-expanded calls. A neural network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 40 kHz (the frequency with the most energy of the fundamental of C. tuberculatus call), could identify 99% and 84% of calls of C. tuberculatus and M. tuberculata, respectively. A second network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 27 kHz (the frequency with the most energy of the fundamental of M. tuberculata call), could identify 34% and 100% of calls of C. tuberculatus and M. tuberculata, respectively. This study represents the first use of neural networks for the identification of bats from their echolocation calls. It is also the first study to use power spectra of time-expanded and heterodyned calls for identification of chiropteran species. The ability of neural networks to identify bats from their echolocation calls is discussed, as is the ecology of both species in relation to the design of their echolocation calls.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Autonomous helicopter hover using an artificial neural network

Details the developments to date of an unmanned air vehicle (UAV) based on a standard size 60 model helicopter. The design goal is to have the helicopter achieve stable hover with the aid of an INS and stereo vision. The focus of the paper is on the development of an artificial neural network (ANN) that makes use of only the INS data to generate hover commands, which are used to directly manipulate the flight servos. Current results show that networks incorporating some form of recurrency (state history) offer little advantage over those without. At this stage, the ANN has partially maintained periods of hover even with misaligned sensors.

Thermal behavior of kaolinite–urea intercalation complex and molecular dynamics simulation for urea molecule orientation

The thermal behavior of kaolinite–urea intercalation complex was investigated by thermogravimetry–differential scanning calorimetry (TG–DSC), X-ray diffraction (XRD), and fourier transform infrared spectroscopy (FTIR). In addition, the interaction mode of urea molecules intercalated into the kaolinite gallery was studied by means of molecular dynamics simulation. Three main mass losses were observed at 136 °C, in the range of 210–270 °C, and at 500 °C in the TG–DSC curves, which were, respectively, attributed to (1) melting of the surface-adsorbed urea, (2) removal of the intercalated urea, and (3) dehydroxylation of the deintercalated kaolinite. The three DSC endothermic peaks at 218, 250, and 261 °C were related to the successive removals of intercalated urea with three different distribution structures. Based on the angle between the dipole moment vector of urea and the basal surface of kaolinite, the three urea models could be described as follows: (1) Type A, the dipole moment vector is nearly parallel to the basal surface of kaolinite; (2) Type B, the dipole moment vector points to the silica tetrahedron with the angle between it and the basal surface of kaolinite ranging from 20°to 40°; and (3) Type C, the dipole moment vector is nearly perpendicular to the basal surface of kaolinite. The three distribution structures of urea molecules were validated by the results of the molecular dynamics simulation. Furthermore, the thermal behavior of the kaolinite–urea intercalation complex investigated by TG–DSC was also supported by FTIR and XRD analyses.