18FDG uptake during induction chemoradiation for oesophageal cancer fails to predict histomorphological tumour response

To determine whether [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) could predict the pathological response in oesophageal cancer after only the first week of neoadjuvant chemoradiation. Thirty-two patients with localised oesophageal cancer had a pretreatment PET scan and a repeat after the first week of chemoradiation. The change in mean maximum standardised uptake value (SUV) and volume of metabolically active tissue (MTV) was compared with the tumour regression grade (TRG) in the final histology. Those who achieved a TRG of 1 and 2 were deemed responders and 3-5 nonresponders. In the responders (28%), the SUV fell from 12.6 (±6.3) to 8.1 (±2.9) after 1 week of chemoradiation (P = 0.070). In nonresponders (72%), the results were 9.7 (±5.4) and 7.1 (±3.8), respectively (P = 0.003). The MTV in responders fell from 36.6 (±22.7) to 22.3 (±10.4) cm3 (P = 0.180), while in nonresponders, this fell from 35.9 (±36.7) to 31.9 (±52.7) cm3 (P = 0.405). There were no significant differences between responders and nonresponders. The hypothesis that early repeat FDG-PET scanning may predict histomorphologic response was not proven. This may reflect an inflammatory effect of radiation that obscures tumour-specific metabolic changes at this time. This assessment may have limited application in predicting response to multimodal regimens for oesophageal cancer. © 2006 Cancer Research UK.

Potential role of bcl-2 as a suppressor of tumour angiogenesis in non- small-cell lung cancer

It has been reported that genes regulating apoptosis may play a role in tumoral angiogenesis. This study examined the relationship between tumour vascularization, a measure of tumour angiogenesis, and bcl-2 and p53 expression in operable non-small-cell lung cancer (NSCLC). The relationship between bcl-2, p53 and tumour vascularization and epidermal-growth-factor- receptor(EGFR) and c-erbB-2 expression was also studied. Tissue sections from resected tumour specimens of 107 NSCLC patients were evaluated immunohistochemically for vascular grade and bcl-2, p53, EGFR and c-erbB-2 expression. bcl-2 expression was found in 20/107 (19%) cases and was associated with squamous-cell histology (p = 0.03). A strong inverse relationship was found between bcl-2 expression and vascular grade (p = 0.005). All c-erbB-2-positive cases were negative for bcl-2 expression (p = 0.01). Overall no association was found between c-erbB-2 expression and vascular grade. However, in bcl-2-negative cases positive c-erbB-2 expression correlated with low angiogenesis (p = 0.05). No relationship was found between p53 and EGFR expression and bcl-2, c-erbB-2 or vascular grade. The improved prognosis reported in bcl-2-positive NSCLC may be related to low tumour vascularization. The results suggest that the anti-apoptotic gene bcl- 2 plays a role in regulating tumour angiogenesis. Since normal lung epithelium expresses bcl-2, a sequence of tumour progression involving loss of bcl-2, then activation of c-erbB-2 or increase in tumour vascularization is proposed.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the a mission should be aborted due to mechanical or other failure. On-board cameras provide information that can be used in the determination of potential landing sites, which are continually updated and ranked to prevent injury and minimize damage. Pulse Coupled Neural Networks have been used for the detection of features in images that assist in the classification of vegetation and can be used to minimize damage to the aerial vehicle. However, a significant drawback in the use of PCNNs is that they are computationally expensive and have been more suited to off-line applications on conventional computing architectures. As heterogeneous computing architectures are becoming more common, an OpenCL implementation of a PCNN feature generator is presented and its performance is compared across OpenCL kernels designed for CPU, GPU and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images obtained during unmanned aerial vehicle trials to determine the plausibility for real-time feature detection.

Tumour necrosis is an independent prognostic marker in non-small cell lung cancer : correlation with biological variables

Background Tumour necrosis (TN) is recognized to be a consequence of chronic cellular hypoxia. TN and hypoxia correlate with poor prognosis in solid tumours. Methods In a retrospective study the prognostic implications of the extent of TN was evaluated in non-small cell lung cancer (NSCLC) and correlated with clinicopathological variables and expression of epidermal growth factor receptor, Bcl-2, p53 and matrix metalloproteinase-9 (MMP-9). Tissue specimens from 178 surgically resected cases of stage I-IIIA NSCLC with curative intent were studied. The specimens were routinely processed, formalin-fixed and paraffin-embedded. TN was graded as extensive or either limited or absent by two independent observers; disagreements were resolved using a double-headed microscope. The degree of reproducibility was estimated by re-interpreting 40 randomly selected cases after a 4 month interval. Results Reproducibility was attained in 36/40 cases, Kappa score=0.8 P<0.001. TN correlated with T-stage (P=0.001), platelet count (P=0.004) and p53 expression (P=0.031). Near significant associations of TN with N-stage (P=0.063) and MMP-9 expression (P=0.058) were seen. No association was found with angiogenesis (P=0.98). On univariate (P=0.0016) and multivariate analysis (P=0.023) TN was prognostic. Conclusion These results indicate that extensive TN reflects an aggressive tumour phenotype in NSCLC and may improve the predictive power of the TMN staging system. The lack of association between TN and angiogenesis may be important although these variables were not evaluated on serial sections. © 2002 Elsevier Science Ireland Ltd. All rights reserved.

Existence of travelling wave solutions for a model of tumour invasion

The existence of travelling wave solutions to a haptotaxis dominated model is analysed. A version of this model has been derived in Perumpanani et al. (1999) to describe tumour invasion, where diffusion is neglected as it is assumed to play only a small role in the cell migration. By instead allowing diffusion to be small, we reformulate the model as a singular perturbation problem, which can then be analysed using geometric singular perturbation theory. We prove the existence of three types of physically realistic travelling wave solutions in the case of small diffusion. These solutions reduce to the no diffusion solutions in the singular limit as diffusion as is taken to zero. A fourth travelling wave solution is also shown to exist, but that is physically unrealistic as it has a component with negative cell population. The numerical stability, in particular the wavespeed of the travelling wave solutions is also discussed.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. 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 and learning algorithm were 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 coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Crucial roles for olfactory ensheathing cells and olfactory mucosal cells in the repair of damaged neural tracts

Olfactory ensheathing cells, the glial cells of the olfactory nervous system, exhibit unique growth-promoting and migratory properties that make them interesting candidates for cell therapies targeting neuronal injuries such as spinal cord injury. Transplantation of olfactory cells is feasible and safe in humans; however, functional outcomes are highly variable with some studies showing dramatic improvements and some no improvements at all. We propose that the reason for this is that the identity and purity of the cells is different in each individual study. We have shown that olfactory ensheathing cells are not a uniform cell population and that individual subpopulations of OECs are present in different regions of the olfactory nervous system, with strikingly different behaviors. Furthermore, the presence of fibroblasts and other cell types in the transplant can dramatically alter the behavior of the transplanted glial cells. Thus, a thorough characterization of the differences between olfactory ensheathing cell subpopulations and how the behavior of these cells is affected by the presence of other cell types is highly warranted.

Pulse-coupled neural network performance for real-time identification of vegetation during forced landing

Safety concerns in the operation of autonomous aerial systems require safe-landing protocols be followed during situations where the mission should be aborted due to mechanical or other failure. This article presents a pulse-coupled neural network (PCNN) to assist in the vegetation classification in a vision-based landing site detection system for an unmanned aircraft. We propose a heterogeneous computing architecture and an OpenCL implementation of a PCNN feature generator. Its performance is compared across OpenCL kernels designed for CPU, GPU, and FPGA platforms. This comparison examines the compute times required for network convergence under a variety of images to determine the plausibility for real-time feature detection.

Mesenchymal stem cells, neural lineage potential, heparan sulfate proteoglycans and the matrix

Along with the tri-lineage of bone, cartilage and fat, human mesenchymal stem cells (hMSCs) retain neural lineage potential. Multiple factors have been described that influence lineage fate of hMSCs including the extracellular microenvironment or niche. The niche includes the extracellular matrix (ECM) providing structural composition, as well as other associated proteins and growth factors, which collectively influence hMSC stemness and lineage specification. As such, lineage specific differentiation of MSCs is mediated through interactions including cell–cell and cell–matrix, as well as through specific signalling pathways triggering downstream events. Proteoglycans (PGs) are ubiquitous within this microenvironment and can be localised to the cell surface or embedded within the ECM. In addition, the heparan sulfate (HS) and chondroitin sulfate (CS) families of PGs interact directly with a number of growth factors, signalling pathways and ECM components including FGFs, Wnts and fibronectin. With evidence supporting a role for HSPGs and CSPGs in the specification of hMSCs down the osteogenic, chondrogenic and adipogenic lineages, along with the localisation of PGs in development and regeneration, it is conceivable that these important proteins may also play a role in the differentiation of hMSCs toward the neuronal lineage. Here we summarise the current literature and highlight the potential for HSPG directed neural lineage fate specification in hMSCs, which may provide a new model for brain damage repair.

Colonizing while migrating : how do individual enteric neural crest cells behave?

Background Directed cell migration is essential for normal development. In most of the migratory cell populations that have been analysed in detail to date, all of the cells migrate as a collective from one location to another. However, there are also migratory cell populations that must populate the areas through which they migrate, and thus some cells get left behind while others advance. Very little is known about how individual cells behave to achieve concomitant directional migration and population of the migratory route. We examined the behavior of enteric neural crest-derived cells (ENCCs), which must both advance caudally to reach the anal end and populate each gut region. Results The behaviour of individual ENCCs was examined using live imaging and mice in which ENCCs express a photoconvertible protein. We show that individual ENCCs exhibit very variable directionalities and speed; as the migratory wavefront of ENCCs advances caudally, each gut region is populated primarily by some ENCCs migrating non-directionally. After populating each region, ENCCs remain migratory for at least 24 hours. Endothelin receptor type B (EDNRB) signaling is known to be essential for the normal advance of the ENCC population. We now show that perturbation of EDNRB principally affects individual ENCC speed rather than directionality. The trajectories of solitary ENCCs, which occur transiently at the wavefront, were consistent with an unbiased random walk and so cell-cell contact is essential for directional migration. ENCCs migrate in close association with neurites. We showed that although ENCCs often use neurites as substrates, ENCCs lead the way, neurites are not required for chain formation and neurite growth is more directional than the migration of ENCCs as a whole. Conclusions Each gut region is initially populated by sub-populations of ENCCs migrating non-directionally, rather than stopping. This might provide a mechanism for ensuring a uniform density of ENCCs along the growing gut.

THE quest for alpha : can artificial neural networks help?

The application of artificial neural networks (ANN) in finance is relatively new area of research. We employed ANNs that used both fundamental and technical inputs to predict future prices of widely held Australian stocks and used these predicted prices for stock portfolio selection over a 10-year period (2001-2011). We found that the ANNs generally do well in predicting the direction of stock price movements. The stock portfolios selected by the ANNs with median accuracy are able to generate positive alpha over the 10-year period. More importantly, we found that a portfolio based on randomly selected network configuration had zero chance of resulting in a significantly negative alpha but a 27% chance of yielding a significantly positive alpha. This is in stark contrast to the findings of the research on mutual fund performance where active fund managers with negative alphas outnumber those with positive alphas.

Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China

The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

Rho GTPases mediate the mechanosensitive lineage commitment of neural stem cells

Adult neural stem cells (NSCs) play important roles in learning and memory and are negatively impacted by neurological disease. It is known that biochemical and genetic factors regulate self-renewal and differentiation, and it has recently been suggested that mechanical and solid-state cues, such as extracellular matrix (ECM) stiffness, can also regulate the functions of NSCs and other stem cell types. However, relatively little is known of the molecular mechanisms through which stem cells transduce mechanical inputs into fate decisions, the extent to which mechanical inputs instruct fate decisions versus select for or against lineage-committed blast populations, or the in vivo relevance of mechanotransductive signaling molecules in native stem cell niches. Here we demonstrate that ECM-derived mechanical signals act through Rho GTPases to activate the cellular contractility machinery in a key early window during differentiation to regulate NSC lineage commitment. Furthermore, culturing NSCs on increasingly stiff ECMs enhances RhoA and Cdc42 activation, increases NSC stiffness, and suppresses neurogenesis. Likewise, inhibiting RhoA and Cdc42 or downstream regulators of cellular contractility rescues NSCs from stiff matrix- and Rho GTPase-induced neurosuppression. Importantly, Rho GTPase expression and ECM stiffness do not alter proliferation or apoptosis rates indicating that an instructive rather than selective mechanism modulates lineage distributions. Finally, in the adult brain, RhoA activation in hippocampal progenitors suppresses neurogenesis, analogous to its effect in vitro. These results establish Rho GTPase-based mechanotransduction and cellular stiffness as biophysical regulators of NSC fate in vitro and RhoA as an important regulatory protein in the hippocampal stem cell niche.

Out of their minds: legal theory in neural networks

This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.