Synthesis of a systolic array genetic algorithm

The paper presents a design for a hardware genetic algorithm which uses a pipeline of systolic arrays. These arrays have been designed using systolic synthesis techniques which involve expressing the algorithm as a set of uniform recurrence relations. The final design divorces the fitness function evaluation from the hardware and can process chromosomes of different lengths, giving the design a generic quality. The paper demonstrates the design methodology by progressively re-writing a simple genetic algorithm, expressed in C code, into a form from which systolic structures can be deduced. This paper extends previous work by introducing a simplification to a previous systolic design for the genetic algorithm. The simplification results in the removal of 2N 2 + 4N cells and reduces the time complexity by 3N + 1 cycles.

The systolic array genetic algorithm, an example of systolic arrays as a reconfigurable design methodology

We advocate the use of systolic design techniques to create custom hardware for Custom Computing Machines. We have developed a hardware genetic algorithm based on systolic arrays to illustrate the feasibility of the approach. The architecture is independent of the lengths of chromosomes used and can be scaled in size to accommodate different population sizes. An FPGA prototype design can process 16 million genes per second.

Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm

Capturing the pattern of structural change is a relevant task in applied demand analysis, as consumer preferences may vary significantly over time. Filtering and smoothing techniques have recently played an increasingly relevant role. A dynamic Almost Ideal Demand System with random walk parameters is estimated in order to detect modifications in consumer habits and preferences, as well as changes in the behavioural response to prices and income. Systemwise estimation, consistent with the underlying constraints from economic theory, is achieved through the EM algorithm. The proposed model is applied to UK aggregate consumption of alcohol and tobacco, using quarterly data from 1963 to 2003. Increased alcohol consumption is explained by a preference shift, addictive behaviour and a lower price elasticity. The dynamic and time-varying specification is consistent with the theoretical requirements imposed at each sample point. (c) 2005 Elsevier B.V. All rights reserved.

Floral volatiles controlling ant behaviour

P>1. Ants show complex interactions with plants, both facultative and mutualistic, ranging from grazers through seed predators and dispersers to herders of some herbivores and guards against others. But ants are rarely pollinators, and their visits to flowers may be detrimental to plant fitness. 2. Plants therefore have various strategies to control ant distributions, and restrict them to foliage rather than flowers. These 'filters' may involve physical barriers on or around flowers, or 'decoys and bribes' sited on the foliage (usually extrafloral nectaries - EFNs). Alternatively, volatile organic compounds (VOCs) are used as signals to control ant behaviour, attracting ants to leaves and/or deterring them from functional flowers. Some of the past evidence that flowers repel ants by VOCs has been equivocal and we describe the shortcomings of some experimental approaches, which involve behavioural tests in artificial conditions. 3. We review our previous study of myrmecophytic acacias, which used in situ experiments to show that volatiles derived from pollen can specifically and transiently deter ants during dehiscence, the effects being stronger in ant-guarded species and more effective on resident ants, both in African and Neotropical species. In these plants, repellence involves at least some volatiles that are known components of ant alarm pheromones, but are not repellent to beneficial bee visitors. 4. We also present new evidence of ant repellence by VOCs in temperate flowers, which is usually pollen-based and active on common European ants. We use these data to indicate that across a wide range of plants there is an apparent trade-off in ant-controlling filter strategies between the use of defensive floral volatiles and the alternatives of decoying EFNs or physical barriers.

The role of biotic and abiotic factors in evolution of ant dispersal in the milkwort family (Polygalaceae)

A phylogenetic approach was taken to investigate the evolutionary history of seed appendages in the plant family Polygalaceae (Fabales) and determine which factors might be associated with evolution of elaiosomes through comparisons to abiotic (climate) and biotic (ant species number and abundance) timelines. Molecular datasets from three plastid regions representing 160 species were used to reconstruct a phylogenetic tree of the order Fabales, focusing on Polygalaceae. Bayesian dating methods were used to estimate the age of the appearance of ant-dispersed elaiosomes in Polygalaceae, shown by likelihood optimizations to have a single origin in the family. Topology-based tests indicated a diversification rate shift associated with appearance of caruncular elaiosomes. We show that evolution of the caruncular elaiosome type currently associated with ant dispersal occurred 54.0-50.5 million year ago. This is long after an estimated increase in ant lineages in the Late Cretaceous based on molecular studies, but broadly concomitant with increasing global temperatures culminating in the Late Paleocene-Early Eocene thermal maxima. These results suggest that although most major ant clades were present when elaiosomes appeared, the environmental significance of elaiosomes may have been an important factor in success of elaiosome-bearing lineages. Ecological abundance of ants is perhaps more important than lineage numbers in determining significance of ant dispersal. Thus, our observation that elaiosomes predate increased ecological abundance of ants inferred from amber deposits could be indicative of an initial abiotic environmental function.

When are ant-attractant devices a worthwhile investment? Vicia faba extrafloral nectaries and Lasius niger ants

Most studies aiming to determine the beneficial effect of ants on plants simply consider the effects of the presence or exclusion of ants on plant yield. This approach is often inadequate, however, as ants interact with both non-tended herbivores and tended Homoptera. Moreover, the interaction with these groups of organisms is dependent on ant density, and these functional relationships are likely to be non-linear. A model is presented here that segregates plant herbivores into two categories depending on the sign of their numerical response to ants (myrmecophiles increase with ants, non-tended herbivores decline). The changes in these two components of herbivores with increasing ant density and the resulting implications for ant-plant mutualisms are considered. It emerges that a wide range of ant densities needs to be considered as the interaction sign (mutualism or parasitism) and strength is likely to change with ant density. The model is used to interpret the results of an experimental study that varied levels of Aphis fabae infestation and Lasius niger ant attendance on Vicia faba bean plants. Increasing ant density consistently reduced plant fitness and thus, in this location, the interaction between the ants and the plant can be considered parasitic. In the Vicia faba system, these costs of ants are unlikely to be offset by other beneficial agents (e.g., parasitoids), which also visit extrafloral nectaries.

Ant semiochemicals limit apterous aphid dispersal

Some organisms can manipulate the nervous systems of others or alter their physiology in order to obtain benefit. Ants are known to limit alate aphid dispersal by physically removing wings and also through chemical manipulation of the alate developmental pathway. This results in reduced dispersal and higher local densities of aphids, which benefit ants in terms of increased honeydew and prey availability. Here, we show that the walking movement of mutualistic apterous aphids is also reduced by ant semiochemicals. Aphids walk slower and their dispersal from an unsuitable patch is hampered by ants. If aphid walking dispersal has evolved as a means of natural enemy escape, then ant chemicals may act as a signal indicating protection; hence, reduced dispersal could be adaptive for aphids. If, however, dispersal is primarily a means to reduce competition or to maintain persistent metapopulations, then manipulation by ants could be detrimental. Such manipulation strategies, common in host-parasite and predator-prey interactions, may be more common in mutualism than expected.

Use of a novel Hill-climbing genetic algorithm in protein folding simulations

We have developed a novel Hill-climbing genetic algorithm (GA) for simulation of protein folding. The program (written in C) builds a set of Cartesian points to represent an unfolded polypeptide's backbone. The dihedral angles determining the chain's configuration are stored in an array of chromosome structures that is copied and then mutated. The fitness of the mutated chain's configuration is determined by its radius of gyration. A four-helix bundle was used to optimise simulation conditions, and the program was compared with other, larger, genetic algorithms on a variety of structures. The program ran 50% faster than other GA programs. Overall, tests on 100 non-redundant structures gave comparable results to other genetic algorithms, with the Hill-climbing program running from between 20 and 50% faster. Examples including crambin, cytochrome c, cytochrome B and hemerythrin gave good secondary structure fits with overall alpha carbon atom rms deviations of between 5 and 5.6 Angstrom with an optimised hydrophobic term in the fitness function. (C) 2003 Elsevier Ltd. All rights reserved.

Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

From ideals to reality: The women's smallholding colony at Lingfield, 1920-39

The immediate impetus for the colony at Lingfield in Surrey was the desire by the Women's Farm and Garden Association to enable women who had worked on the land during the First World War to be able to farm on their own account. However the motivation for the colony can also be traced back to late nineteenth-century ideals. The colony soon ran into problems which were exacerbated by the adverse agricultural conditions of the early 1920s. The association responded constructively but the colony was wound down from 1929. At one level the colony could be seen as a failure, yet this article argues that the 19 colony provided a rural community where single women lived in a mutually supportive environment.

Cannabinoids stimulate fibroblastic colony formation by bone marrow cells indirectly via CB2 receptors

Recently, the cannabinoid receptors CB1 and CB2 were shown to modulate bone formation and resorption in vivo, although little is known of the mechanisms underlying this. The effects of cannabinoids on mesenchymal stem cell (MSC) recruitment in whole bone marrow were investigated using either the fibroblastic colony-forming unit (CFU-f) assay or high-density cultures of whole bone marrow. Levels of the CB1 and CB2 receptors were assessed by flow cytometry. Treatment of CFU-f cultures with the endocannabinoid 2-arachidonylglycerol (2-AG) dose-dependently increased fibroblastic and differentiated colony formation along with colony size. The nonspecific agonists CP 55,940 and WIN 55,212 both increased colony numbers, as did the CB2 agonists BML190 and JWH015. The CB1-specific agonist ACEA had no effect, whereas the CB2 antagonist AM630 blocked the effect of the natural cannabinoid tetrahydrocannabivarin, confirming mediation via the CB2 receptor. Treatment of primary bone marrow cultures with 2-AG stimulated proliferation and collagen accumulation, whereas treatment of subcultures of MSC had no effect, suggesting that the target cell is not the MSC but an accessory cell present in bone marrow. Subcultures of MSCs were negative for CB1 and CB2 receptors as shown by flow cytometry, whereas whole bone marrow contained a small population of cells positive for both receptors. These data suggest that cannabinoids may stimulate the recruitment of MSCs from the bone marrow indirectly via an accessory cell and mediated via the CB2 receptor. This recruitment may be one mechanism responsible for the increased bone formation seen after cannabinoid treatment in vivo.

Antiproliferative activity of Titanocene Y against tumor colony-forming units

Bis-[(p-methoxybenzyl)cyclopentadienyl] titanium dichloride, better known as Titanocene Y, is a newly synthesized titanium-based anticancer drug. We studied the antitumor activity of Titanocene Y with concentrations of 2.1, 21 and 210 μmol/l against a range of freshly explanted human tumors, using an in-vitro soft agar cloning system. The sensitivity against Titanocene Y was highly remarkable in the case of renal cell, ovarian, nonsmall cell lung and colon cancer. In particular the surprisingly good response of nonsmall cell lung cancer and colon cancer against Titanocene Y at its lowest concentration of 2.1 μmol/l was well comparable or better with respect to cisplatin, given at a concentration of 1.0 μmol/l. Further clinical development of Titanocene Y appears to be warranted because of the broad cytotoxic activity shown and the specific activity of Titanocene Y against renal cell cancer.

Variable step LMS algorithm using the accumulated instantaneous error concept

The convergence speed of the standard Least Mean Square adaptive array may be degraded in mobile communication environments. Different conventional variable step size LMS algorithms were proposed to enhance the convergence speed while maintaining low steady state error. In this paper, a new variable step LMS algorithm, using the accumulated instantaneous error concept is proposed. In the proposed algorithm, the accumulated instantaneous error is used to update the step size parameter of standard LMS is varied. Simulation results show that the proposed algorithm is simpler and yields better performance than conventional variable step LMS.

Unsupervised genetic algorithm deployed for intrusion detection

This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machine-learning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method.