Automated pre-processing strategies for species occurrence data used in biodiversity modelling

To construct Biodiversity richness maps from Environmental Niche Models (ENMs) of thousands of species is time consuming. A separate species occurrence data pre-processing phase enables the experimenter to control test AUC score variance due to species dataset size. Besides, removing duplicate occurrences and points with missing environmental data, we discuss the need for coordinate precision, wide dispersion, temporal and synonymity filters. After species data filtering, the final task of a pre-processing phase should be the automatic generation of species occurrence datasets which can then be directly ’plugged-in’ to the ENM. A software application capable of carrying out all these tasks will be a valuable time-saver particularly for large scale biodiversity studies.

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.

Benchmarking consensus model quality assessment for protein fold recognition

Background: Selecting the highest quality 3D model of a protein structure from a number of alternatives remains an important challenge in the field of structural bioinformatics. Many Model Quality Assessment Programs (MQAPs) have been developed which adopt various strategies in order to tackle this problem, ranging from the so called "true" MQAPs capable of producing a single energy score based on a single model, to methods which rely on structural comparisons of multiple models or additional information from meta-servers. However, it is clear that no current method can separate the highest accuracy models from the lowest consistently. In this paper, a number of the top performing MQAP methods are benchmarked in the context of the potential value that they add to protein fold recognition. Two novel methods are also described: ModSSEA, which based on the alignment of predicted secondary structure elements and ModFOLD which combines several true MQAP methods using an artificial neural network. Results: The ModSSEA method is found to be an effective model quality assessment program for ranking multiple models from many servers, however further accuracy can be gained by using the consensus approach of ModFOLD. The ModFOLD method is shown to significantly outperform the true MQAPs tested and is competitive with methods which make use of clustering or additional information from multiple servers. Several of the true MQAPs are also shown to add value to most individual fold recognition servers by improving model selection, when applied as a post filter in order to re-rank models. Conclusion: MQAPs should be benchmarked appropriately for the practical context in which they are intended to be used. Clustering based methods are the top performing MQAPs where many models are available from many servers; however, they often do not add value to individual fold recognition servers when limited models are available. Conversely, the true MQAP methods tested can often be used as effective post filters for re-ranking few models from individual fold recognition servers and further improvements can be achieved using a consensus of these methods.

Ultrafast excited state dynamics controlling photochemical isomerization of N-methyl-4-[trans-2-(4-pyridyl)ethenyl]pyridinium coordinated to a {Re-1(CO)(3)(2,2 '-bipyridine)} chromophore

Two Multifunctional photoactive complexes [Re(Cl)(CO)(3)-(MeDpe(+))(2)](2+) and [Re(MeDpe(+))(CO)(3)(bpy)](2+) (MeDpe(+) = N-methyl-4-[trans-2-(4-pyridyl)ethenyl]pyridinium, bpy = 2,2'-bipyridine) were synthesized. characterized. and their redox and photonic properties were investigated by cyclic voltammetry: ultraviolet-visible-infrared (UV/Vis/IR) spectroelectrochemistry, stationary UV/Vis and resonance Raman spectroscopy; photolysis; picosecond time-resolved absorption spectroscopy in the visible and infrared regions: and time-resolved resonance Raman spectroscopy. The first reduction step of either complex Occurs at about -1.1 V versus Fc/Fc(+) and is localized at MeDpe(+). Reduction alone does not induce a trans -> cis isomerization of MeDpe(+). [Re(Cl)(CO)(3)(MeDPe(+))(2)](2+) is photostable, while [Re(MeDpe(+))(CO)(3)(bpy)](2+) and free MeDpe(+) isomerize under near-UV irradiation. The lowest excited state of [Re(Cl)(CO)(3)(MeDPe(+))(2)](2+) has been identified as the Re(Cl)(CO)(3) -> MeDpe(+) (MLCT)-M-3 (MLCT = metal-to-ligand charge transfer), decaying directly to the ground state with lifetimes of approximate to 42 (73%) and approximate to 430ps (27%). Optical excitation of [Re(MeDpe(+))(CO)(3)(bpy)](2+) leads to population of Re(CO)(3) -> MeDpe(+) and Re(CO)(3) -> bpy (MLCT)-M-3 states, from which a MeDpe(+) localized intraligand 3 pi pi* excited state ((IL)-I-3) is populated with lifetimes of approximate to 0.6 and approximate to 10 ps, respectively. The 3IL state undergoes a approximate to 21 ps internal rotation, which eventually produces the cis isomer on a much longer timescale. The different excited-state behavior of the two complexes and the absence of thermodynamically favorable interligand electron transfer in excited [Re(MeDpe(+))(CO)(3)(bpy)](2+) reflect the fine energetic balance between excited states of different orbital origin, which can be tuned by subtle Structural variations. The complex [Re(MeDpe+)(CO)(3)(bpy)](2+) emerges as a prototypical, multifunctional species with complementary redox and photonic behavior.

Catalytic oxidation of alcohols using molecular oxygen mediated by poly(ethylene glycol)-supported nitroxyl radicals

The selective catalytic oxidation of alcohols over a mixture of copper(l) chloride and a number of linear 'linker-less' or 'branched' poly(ethylene glycol)-supported nitroxyl radicals of the 2,2,6,6-tetramethyl-piperidine-1-oxyl (TEMPO) family as a catalyst system has been investigated in the presence of molecular oxygen in a batch reactor. It is found that the activity profile of the polymer-supported nitroxyl radicals is in good agreement with that of low-molecular weight nitroxyl catalysts, for example, allylic and benzylic alcohols are oxidised faster than aliphatic alcohols. The oxidations can be tuned to be highly selective such that aldehydes are the only oxidation products observed in the oxidation of primary alcohols and the oxidations of secondary alcohols yield the corresponding ketones. A strong structural effect of the polymeric nitroxyl species on catalytic activity that is dependent upon their spatial orientation of the nitroxyl radicals is particularly noted. The new soluble macromolecular catalysts can be recovered readily from the reaction mixture by solvent precipitation and filtration. In addition, the recycled catalysts demonstrate a similar selectivity with only a small decrease in activity compared to the fresh catalyst even after five repetitive cycles. (c) 2005 Elsevier B.V. All rights reserved.

Synthesis of multicarboxylic acid appended imidazolium ionic liquids and their application in palladium-catalyzed selective oxidation of styrene

A series of multicarboxylic acid appended imidazolium ionic liquids ( McaILs) with chloride [ Cl](-) or bromide [ Br](-) as anions have been synthesized and characterized. Deprotonation of these ionic acids gives the corresponding zwitterions. Re-protonation of the zwitterions with strong Bronsted acids gives a series of new ionic acid-adducts, many of which remained as room-temperature ionic liquids. A new catalytic system, McaIL/PdCl2 for the selective catalytic oxidation of styrene to acetophenone with hydrogen peroxide as an oxidant has been attempted. In the presence of McaILs, it is found that the quantity of palladium chloride PdCl2 used can be greatly reduced while the activity ( TOF) and selectivity towards acetophenone are enhanced sharply. It is also shown that the catalytic properties of this system could be finely tuned through the molecular design of the McaILs. The best TOF value obtained so far is 146 h(-1) with 100% conversion of styrene at 93% selectivity to acetophenone. In addition, the catalytic activity has been maintained for at least ten catalytic cycles.

Consequences of N,C,N '- and C,N,N '-coordination modes on electronic and photophysical properties of cyclometalated aryl ruthenium(II) complexes

The effects of isoelectronic replacement of a neutral nitrogen donor atom by an anionic carbon atom in terpyridine ruthenium(II) complexes on the electronic and photophysical properties of the resulting N,C,N'- and C,N,N'-cyclometalated aryl ruthenium(II) complexes were investigated. To this end, a series of complexes was prepared either with ligands containing exclusively nitrogen donor atoms, that is, [Ru(R-1-tpy)(R-2-tpy)](2+) (R-1, R-2 = H, CO2Et), or bearing either one N,C,N'- or C,N,N'-cyclometalated ligand and one tpy ligand, that is, [Ru(R-1-(NCN)-C-Lambda-N-Lambda)(R-2-tpy)](+) and [Ru(R-1-(CNN)-N-Lambda-N-Lambda)(R-2-tpy)](+), respectively. Single-crystal X-ray structure determinations showed that cyclometalation does not significantly alter the overall geometry of the complexes but does change the bond lengths around the ruthenium(II) center, especially the nitrogen-to-ruthenium bond length trans to the carbanion. Substitution of either of the ligands with electron-withdrawing ester functionalities fine-tuned the electronic properties and resulted in the presence of an IR probe. Using trends obtained from redox potentials, emission energies, IR spectroelectrochemical responses, and the character of the lowest unoccupied molecular orbitals from DFT studies, it is shown that the first reduction process and luminescence are associated with the ester-substituted C,N,N'-cyclometalated ligand in [Ru(EtO2C-(CNN)-N-Lambda-N-Lambda)(tpy)](+). Cyclometalation in an N,C,N'-bonding motif changed the energetic order of the ruthenium d(zx), d(yz), and d(xy) orbitals. The red-shifted absorption in the N,C,N'-cyclometalated complexes is assigned to MLCT transitions to the tpy ligand. The red shift observed upon introduction of the ester moiety is associated with an increase in intensity of low-energy transitions, rather than a red shift of the main transition. Cyclometalation in the C,N,N'-binding motif also red-shifts the absorption, but the corresponding transition is associated with both ligand types. Luminescence of the cyclometalated complexes is relatively independent of the mode of cyclometalation, obeying the energy gap law within each individual series.

Integrated optics for nulling interferometry in the thermal infrared

Modal filtering is based on the capability of single-mode waveguides to transmit only one complex amplitude function to eliminate virtually any perturbation of the interfering wavefronts, thus making very high rejection ratios possible in a nulling interferometer. In the present paper we focus on the progress of Integrated Optics in the thermal infrared [6-20 mu m] range, one of the two candidate technologies for the fabrication of Modal Filters, together with fiber optics. In conclusion of the European Space Agency's (ESA) "Integrated Optics for Darwin" activity, etched layers of clialcogenide material deposited on chalcogenide glass substrates was selected among four candidates as the technology with the best potential to simultaneously meet the filtering efficiency, absolute and spectral transmission, and beam coupling requirements. ESA's new "Integrated Optics" activity started at mid-2007 with the purpose of improving the technology until compliant prototypes can be manufactured and validated, expectedly by the end of 2009. The present paper aims at introducing the project and the components requirements and functions. The selected materials and preliminary designs, as well as the experimental validation logic and test benches are presented. More details are provided on the progress of the main technology: vacuum deposition in the co-evaporation mode and subsequent etching of chalcogenide layers. In addition., preliminary investigations of an alternative technology based on burying a chalcogenide optical fiber core into a chalcogenide substrate are presented. Specific developments of anti-reflective solutions designed for the mitigation of Fresnel losses at the input and output surface of the components are also introduced.

Prediction of Parkinson's disease tremor onset using artificial neural networks

In this paper we present the initial results using an artificial neural network to predict the onset of Parkinson's Disease tremors in a human subject. Data for the network was obtained from implanted deep brain electrodes. A tuned artificial neural network was shown to be able to identify the pattern of the onset tremor from these real time recordings.

Investigation of cadmium alternatives in thin-film coatings - art. no. 62860C

The health risks associated with the inhalation or ingestion of cadmium are well documented([1,2]). During the past 18 years, EU legislation has steadily been introduced to restrict its use, leaving a requirement for the development of replacement materials. This paper looks at possible alternatives to various cadmium II-VI dielectric compounds used in the deposition of optical thin-films for various opto-electronic devices. Application areas of particular interest are for infrared multilayer interference filter fabrication and solar cell industries, where cadmium-based coatings currently find widespread use. The results of single and multilayer designs comprising CdTe, CdS, CdSe and PbTe deposited onto group IV and II-VI materials as interference filters for the mid-IR region are presented. Thin films of SnN, SnO2, SnS and SnSe are fabricated by plasma assisted CVD, reactive RF sputtering and thermal evaporation. Examination of these films using FTIR spectroscopy, SEM, EDX analysis and optical characterisation methods provide details of material dispersion, absorption, composition, refractive index, energy band gap and layer thicknesses. The optimisation of deposition parameters in order to synthesise coatings with similar optical and semiconductor properties as those containing cadmium has been investigated. Results of environmental, durability and stability trials are also presented.

Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

Identification of Parkinson’s disease tremor onset using artificial neural networks

In this paper we consider the possibility of using an artificial neural network to accurately identify the onset of Parkinson’s Disease tremors in human subjects. Data for the network is obtained by means of deep brain implantation in the human brain. Results presented have been obtained from a practical study (i.e. real not simulated data) but should be regarded as initial trials to be discussed further. It can be seen that a tuned artificial neural network can act as an extremely effective predictor in these circumstances.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Identification of nonlinear systems using generalized kernel models

Nonlinear system identification is considered using a generalized kernel regression model. Unlike the standard kernel model, which employs a fixed common variance for all the kernel regressors, each kernel regressor in the generalized kernel model has an individually tuned diagonal covariance matrix that is determined by maximizing the correlation between the training data and the regressor using a repeated guided random search based on boosting optimization. An efficient construction algorithm based on orthogonal forward regression with leave-one-out (LOO) test statistic and local regularization (LR) is then used to select a parsimonious generalized kernel regression model from the resulting full regression matrix. The proposed modeling algorithm is fully automatic and the user is not required to specify any criterion to terminate the construction procedure. Experimental results involving two real data sets demonstrate the effectiveness of the proposed nonlinear system identification approach.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.