Phylogenetic relationships within the moss family Bryaceae based on chloroplast DNA evidence

The Bryaceae are a large cosmopolitan family of mosses containing genera of considerable taxonomic difficulty. Phylogenetic relationships within the family were inferred using data from chloroplast DNA sequences (rps4 and trnL-trnF region). Parsimony and maximum likelihood optimality criteria, and Bayesian phylogenetic inference procedures were employed to reconstruct relationships. The genera Bryum and Brachymenium are not monophyletic groups. A clade comprising Plagiobryum, Acidodontium, Mielichhoferia macrocarpa, Bryum sects. Bryum, Apalodictyon, Limbata, Leucodontium, Caespiticia, Capillaria (in part: sect. Capillaria), and Brachymenium sect. Dicranobryum, is well supported in all analyses and represents a major lineage within the family. Section Dicranobryum of Brachymenium is more closely related to section Bryum than to the other sections of Brachymenium, as are Mielichhoferia macrocarpa and M. himalayana. Species of Acidodontium form a clade with Anomobryum julaceum. The grouping of species with a rosulate gametophytic growth form suggests the presence of a 'rosulate' clade similar in circumscription to the genus Rosulabryum. Mielichhoferia macrocarpa and M. himalayana are transferred to Bryum as B. porsildii and B. caucasicum, respectively.

The future of floristics in the Mediterranean region

In this paper, I address the "wider issues Both That Affect Floristic studies today and how They Are Likely to Develop in the Future, And The Problems That special concern in the Mediterranean region particularism. A survey of published floristic studies is given for the Mediterranean and the Middle East, and the desirability of applying electronic web-based preparation and publication of floristic and taxonomic projects is considered, with special reference to the Euro + Med PlantBase project. A Survey of Published Studies Floristic IS Given for the Mediterranean and the Middle East, and the Desirability of Applying web-based electronic preparation and publication of Floristic and Taxonomic Project is regarded, with Special Reference To The Euro + Med project PlantBase. A new paradigm for taxonomy, and a plan of action for Mediterranean floristic studies are proposed. A New Paradigm for taxonomy, and a plan of action for Mediterranean Floristic studies are Proposed.

Development of a PCR-based assay for rapid and reliable identification of pathogenic Fusaria

Identification of Fusarium species has always been difficult due to confusing phenotypic classification systems. We have developed a fluorescent-based polymerase chain reaction assay that allows for rapid and reliable identification of five toxigenic and pathogenic Fusarium species. The species includes Fusarium avenaceum, F. culmorum, F. equiseti, F. oxysporum and F. sambucinum. The method is based on the PCR amplification of species-specific DNA fragments using fluorescent oligonucleotide primers, which were designed based on sequence divergence within the internal transcribed spacer region of nuclear ribosomal DNA. Besides providing an accurate, reliable, and quick diagnosis of these Fusaria, another advantage with this method is that it reduces the potential for exposure to carcinogenic chemicals as it substitutes the use of fluorescent dyes in place of ethidium, bromide. Apart from its multidisciplinary importance and usefulness, it also obviates the need for gel electrophoresis. (C) 2002 Published by Elsevier Science B.V. on behalf of the Federation of European Microbiological Societies.

Low-molecular-weight gelators: Elucidating the principles of gelation based on gelator solubility and a cooperative self-assembly model

This paper highlights the key role played by solubility in influencing gelation and demonstrates that many facets of the gelation process depend on this vital parameter. In particular, we relate thermal stability (T-gel) and minimum gelation concentration (MGC) values of small-molecule gelation in terms of the solubility and cooperative self-assembly of gelator building blocks. By employing a van't Hoff analysis of solubility data, determined from simple NMR measurements, we are able to generate T-calc values that reflect the calculated temperature for complete solubilization of the networked gelator. The concentration dependence of T-calc allows the previously difficult to rationalize "plateau-region" thermal stability values to be elucidated in terms of gelator molecular design. This is demonstrated for a family of four gelators with lysine units attached to each end of an aliphatic diamine, with different peripheral groups (Z or Bee) in different locations on the periphery of the molecule. By tuning the peripheral protecting groups of the gelators, the solubility of the system is modified, which in turn controls the saturation point of the system and hence controls the concentration at which network formation takes place. We report that the critical concentration (C-crit) of gelator incorporated into the solid-phase sample-spanning network within the gel is invariant of gelator structural design. However, because some systems have higher solubilities, they are less effective gelators and require the application of higher total concentrations to achieve gelation, hence shedding light on the role of the MGC parameter in gelation. Furthermore, gelator structural design also modulates the level of cooperative self-assembly through solubility effects, as determined by applying a cooperative binding model to NMR data. Finally, the effect of gelator chemical design on the spatial organization of the networked gelator was probed by small-angle neutron and X-ray scattering (SANS/SAXS) on the native gel, and a tentative self-assembly model was proposed.

Verification and FPGA circuits of a block-2 fast path-based predictor

This paper formally derives a new path-based neural branch prediction algorithm (FPP) into blocks of size two for a lower hardware solution while maintaining similar input-output characteristic to the algorithm. The blocked solution, here referred to as B2P algorithm, is obtained using graph theory and retiming methods. Verification approaches were exercised to show that prediction performances obtained from the FPP and B2P algorithms differ within one mis-prediction per thousand instructions using a known framework for branch prediction evaluation. For a chosen FPGA device, circuits generated from the B2P algorithm showed average area savings of over 25% against circuits for the FPP algorithm with similar time performances thus making the proposed blocked predictor superior from a practical viewpoint.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

A new blind equalization algorithm based on convex combination

The convex combination is a mathematic approach to keep the advantages of its component algorithms for better performance. In this paper, we employ convex combination in the blind equalization to achieve better blind equalization. By combining the blind constant modulus algorithm (CMA) and decision directed algorithm, the combinative blind equalization (CBE) algorithm can retain the advantages from both. Furthermore, the convergence speed of the CBE algorithm is faster than both of its component equalizers. Simulation results are also given to verify the proposed algorithm.

A camera based digit location and recognition system for garment tracking

Garment information tracking is required for clean room garment management. In this paper, we present a camera-based robust system with implementation of Optical Character Reconition (OCR) techniques to fulfill garment label recognition. In the system, a camera is used for image capturing; an adaptive thresholding algorithm is employed to generate binary images; Connected Component Labelling (CCL) is then adopted for object detection in the binary image as a part of finding the ROI (Region of Interest); Artificial Neural Networks (ANNs) with the BP (Back Propagation) learning algorithm are used for digit recognition; and finally the system is verified by a system database. The system has been tested. The results show that it is capable of coping with variance of lighting, digit twisting, background complexity, and font orientations. The system performance with association to the digit recognition rate has met the design requirement. It has achieved real-time and error-free garment information tracking during the testing.

Examining random number generators used in stochastic iteration algorithms

This paper investigates random number generators in stochastic iteration algorithms that require infinite uniform sequences. We take a simple model of the general transport equation and solve it with the application of a linear congruential generator, the Mersenne twister, the mother-of-all generators, and a true random number generator based on quantum effects. With this simple model we show that for reasonably contractive operators the theoretically not infinite-uniform sequences perform also well. Finally, we demonstrate the power of stochastic iteration for the solution of the light transport problem.

Parallel Monte Carlo algorithms for information retrieval

In any data mining applications, automated text and text and image retrieval of information is needed. This becomes essential with the growth of the Internet and digital libraries. Our approach is based on the latent semantic indexing (LSI) and the corresponding term-by-document matrix suggested by Berry and his co-authors. Instead of using deterministic methods to find the required number of first "k" singular triplets, we propose a stochastic approach. First, we use Monte Carlo method to sample and to build much smaller size term-by-document matrix (e.g. we build k x k matrix) from where we then find the first "k" triplets using standard deterministic methods. Second, we investigate how we can reduce the problem to finding the "k"-largest eigenvalues using parallel Monte Carlo methods. We apply these methods to the initial matrix and also to the reduced one. The algorithms are running on a cluster of workstations under MPI and results of the experiments arising in textual retrieval of Web documents as well as comparison of the stochastic methods proposed are presented. (C) 2003 IMACS. Published by Elsevier Science B.V. All rights reserved.

OFDM joint data detection and phase noise cancellation based on minimum mean square prediction error

This paper proposes a new iterative algorithm for orthogonal frequency division multiplexing (OFDM) joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the relatively less studied problem of "overfitting" such that the iterative approach may converge to a trivial solution. Specifically, we apply a hard-decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the PHN, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical Simulations are also given to verify the proposed algorithm. (C) 2008 Elsevier B.V. All rights reserved.

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.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.