Sox1-deficient mice suffer from epilepsy associated with abnormal ventral forebrain development and olfactory cortex hyperexcitability

Mutations in several classes of embryonically-expressed transcription factor genes are associated with behavioral disorders and epilepsies. However, there is little known about how such genetic and neurodevelopmental defects lead to brain dysfunction. Here we present the characterization of an epilepsy syndrome caused by the absence of the transcription factor SOX1 in mice. In vivo electroencephalographic recordings from SOX1 mutants established a correlation between behavioral changes and cortical output that was consistent with a seizure origin in the limbic forebrain. In vitro intracellular recordings from three major forebrain regions, neocortex, hippocampus and olfactory (piriform) cortex (OC) showed that only the OC exhibits abnormal enhanced synaptic excitability and spontaneous epileptiform discharges. Furthermore, the hyperexcitability of the OC neurons was present in mutants prior to the onset of seizures but was completely absent from both the hippocampus and neocortex of the same animals. The local inhibitory GABAergic neurotransmission remained normal in the OC of SOX1-deficient brains, but there was a severe developmental deficit of OC postsynaptic target neurons, mainly GABAergic projection neurons within the olfactory tubercle and the nucleus accumbens shell. Our data show that SOX1 is essential for ventral telencephalic development and suggest that the neurodevelopmental defect disrupts local neuronal circuits leading to epilepsy in the SOX1-deficient mice

The fate and toxicity of the flavonoids naringenin and formononetin in soil

The flavonoid class of plant secondary metabolites play a multifunctional role in below-ground plant-microbe interactions with their best known function as signals in the nitrogen fixing legume-rhizobia symbiosis. Flavonoids enter rhizosphere soil as a result of root exudation and senescence but little is known about their subsequent fate or impacts on microbial activity. Therefore, the present study examined the sorptive behaviour, biodegradation and impact on dehydrogenase activity (as determined by iodonitrotetrazolium chloride reduction) of the flavonoids naringenin and formononetin in soil. Organic carbon normalised partition coefficients, log K-oc, of 3.12 (formononetin) and 3.19 (naringenin) were estimated from sorption isotherms and, after comparison with literature log K-oc values for compounds whose soil behaviour is better characterised, the test flavonoids were deemed to be moderately sorbed. Naringenin (spiked at 50 mu g g(-1)) was biodegraded without a detectable lag phase with concentrations reduced to 0.13 +/- 0.01 mu g g(-1) at the end of the 96 h time course. Biodegradation of formononetin proceeded after a lag phase of similar to 24 with concentrations reduced to 4.5 +/- 1% of the sterile control after 72 h. Most probable number (MPN) analysis revealed that prior to the addition of flavonoids, the soil contained 5.4 x 10(6) MPNg(-1) (naringenin) and 7.9 x 10(5) MPNg(-1) (formononetin) catabolic microbes. Formononetin concentration had no significant (p > 0.05) effect on soil dehydrogenase activity, whereas naringenin concentration had an overall but non-systematic impact (p = 0.045). These results are discussed with reference to likely total and bioavailable concentrations of flavonoids experienced by microbes in the rhizosphere. (c) 2007 Elsevier Ltd. All rights reserved.

Difluorinated analogues of shikimic acid

Investigations into the quinate to shikimate transformation have been carried out, the results of which have been exploited in the synthesis of a novel difluoromethylene homologue of shikimic acid from (-)-quinic acid. Martin's sulfurane {Ph2S[OC(CF3)(2)Ph](2)} was the reagent of choice for the key dehydration step of this synthesis. The results of investigations into the synthesis of the important natural product analogue, 6,6-difluoroshikimic acid are also reported. (C) 2003 Elsevier Science Ltd. All rights reserved.

Simulating regional scale secondary organic aerosol formation during the TORCH 2003 campaign in the southern UK

A photochemical trajectory model has been used to simulate the chemical evolution of air masses arriving at the TORCH field campaign site in the southern UK during late July and August 2003, a period which included a widespread and prolonged photochemical pollution episode. The model incorporates speciated emissions of 124 nonmethane anthropogenic VOC and three representative biogenic VOC, coupled with a comprehensive description of the chemistry of their degradation. A representation of the gas/aerosol absorptive partitioning of ca. 2000 oxygenated organic species generated in the Master Chemical Mechanism (MCM v3.1) has been implemented, allowing simulation of the contribution to organic aerosol (OA) made by semi- and non-volatile products of VOC oxidation; emissions of primary organic aerosol (POA) and elemental carbon (EC) are also represented. Simulations of total OA mass concentrations in nine case study events (optimised by comparison with observed hourly-mean mass loadings derived from aerosol mass spectrometry measurements) imply that the OA can be ascribed to three general sources: (i) POA emissions; (ii) a '' ubiquitous '' background concentration of 0.7 mu g m(-3); and (iii) gas-to-aerosol transfer of lower volatility products of VOC oxidation generated by the regional scale processing of emitted VOC, but with all partitioning coefficients increased by a species-independent factor of 500. The requirement to scale the partitioning coefficients, and the implied background concentration, are both indicative of the occurrence of chemical processes within the aerosol which allow the oxidised organic species to react by association and/or accretion reactions which generate even lower volatility products, leading to a persistent, non-volatile secondary organic aerosol (SOA). The contribution of secondary organic material to the simulated OA results in significant elevations in the simulated ratio of organic carbon (OC) to EC, compared with the ratio of 1.1 assigned to the emitted components. For the selected case study events, [OC]/[EC] is calculated to lie in the range 2.7-9.8, values which are comparable with the high end of the range reported in the literature.

Investigation of support vector machine for the detection of architectural distortion in mammographic images

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.

Face verification using Gabor wavelets and AdaBoost

This paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

Synthesis, characterisation, Mössbauer spectra, and structures of some trinuclear iron-tin clusters

Reactions of [Fe3(CO)12] with diaryltin species SnR2(R1= 2,4,6-triisopropylphenyl, R2= 2,6-diethylphenyl, R3= pentamethylphenyl) and with Sn[CH(PPh2)2]2 have been investigated. The tin reagents SnR2(R = R1 or R2) reacted under mild conditions to give in moderate yields the trinuclear species [Fe2(CO)8(µ-SnR12)]1 or [Fe2(CO)8(µ-SnR22)]2, as orange-red crystalline solids, which decompose in air on prolonged exposure. The compound [Fe2(CO)8(µ-SnR42)]3(R4= 2,4,6-triphenylphenyl) can be similarly obtained. Prolonged treatment of the carbonyl with the novel tin reagent SnR32, by contrast, afforded the known compound spiro-[(OC)8Fe2SnFe2(CO)8]4 for which data are briefly reported. Reactions with tin or lead reagents M[CH(PPh2)2]2(M = Sn or Pb) afforded [Fe2(CO)6(µ-CO)(µ-dppm)][dppm = 1,2-bis(diphenylphosphino)methane] rapidly and almost quantitatively. Full crystal and molecular structural data are reported for [Fe2(CO)8(µ-SnR12)] and [Fe2(CO)8(µ-SnR22)]. Mössbauer data are also presented for compounds 1–3, and interpreted in terms of the structural data for these and other systems.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Prediction of novel and analogous folds using fragment assembly and fold recognition

A number of new and newly improved methods for predicting protein structure developed by the Jones–University College London group were used to make predictions for the CASP6 experiment. Structures were predicted with a combination of fold recognition methods (mGenTHREADER, nFOLD, and THREADER) and a substantially enhanced version of FRAGFOLD, our fragment assembly method. Attempts at automatic domain parsing were made using DomPred and DomSSEA, which are based on a secondary structure parsing algorithm and additionally for DomPred, a simple local sequence alignment scoring function. Disorder prediction was carried out using a new SVM-based version of DISOPRED. Attempts were also made at domain docking and “microdomain” folding in order to build complete chain models for some targets.

Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Nonlinear set-membership estimation: a support vector machine approach

In this paper a support vector machine (SVM) approach for characterizing the feasible parameter set (FPS) in non-linear set-membership estimation problems is presented. It iteratively solves a regression problem from which an approximation of the boundary of the FPS can be determined. To guarantee convergence to the boundary the procedure includes a no-derivative line search and for an appropriate coverage of points on the FPS boundary it is suggested to start with a sequential box pavement procedure. The SVM approach is illustrated on a simple sine and exponential model with two parameters and an agro-forestry simulation model.

Influence of fermentation conditions on the surface properties and adhesion of Lactobacillus rhamnosus GG

Background: The surface properties of probiotic bacteria influence to a large extent their interactions within the gut ecosystem. There is limited amount of information on the effect of the production process on the surface properties of probiotic lactobacilli in relation to the mechanisms of their adhesion to the gastrointestinal mucosa. The aim of this work was to investigate the effect of the fermentation pH and temperature on the surface properties and adhesion ability to Caco-2 cells of the probiotic strain Lactobacillus rhamnosus GG. Results: The cells were grown at pH 5, 5.5, 6 (temperature 37 °C) and at pH 6.5 (temperature 25 °C, 30 °C and 37 °C), and their surfaces analysed by X-ray photoelectron spectrometry (XPS), Fourier transform infrared spectroscopy (FT-IR) and gel-based proteomics. The results indicated that for all the fermentation conditions, with the exception of pH 5, a higher nitrogen to carbon ratio and a lower phosphate content was observed at the surface of the bacteria, which resulted in a lower surface hydrophobicity and reduced adhesion levels to Caco-2 cells as compared to the control fermentation (pH 6.5, 37 oC). A number of adhesive proteins, which have been suggested in previous published works to take part in the adhesion of bacteria to the human gastrointestinal tract, were identified by proteomic analysis, with no significant differences between samples however. Conclusions: The temperature and the pH of the fermentation influenced the surface composition, hydrophobicity and the levels of adhesion of L. rhamnosus GG to Caco-2 cells. It was deduced from the data that a protein rich surface reduced the adhesion ability of the cells.