GANN: Genetic algorithm neural networks for the detection of conserved combinations of features in DNA

Background: The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence-and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence. Results: GANN ( available at http://bioinformatics.org.au/gann) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions. Conclusion: GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences.

Genetic algorithm optimization of adaptive multi-scale GLCM features

We introduce a new second-order method of texture analysis called Adaptive Multi-Scale Grey Level Co-occurrence Matrix (AMSGLCM), based on the well-known Grey Level Co-occurrence Matrix (GLCM) method. The method deviates significantly from GLCM in that features are extracted, not via a fixed 2D weighting function of co-occurrence matrix elements, but by a variable summation of matrix elements in 3D localized neighborhoods. We subsequently present a new methodology for extracting optimized, highly discriminant features from these localized areas using adaptive Gaussian weighting functions. Genetic Algorithm (GA) optimization is used to produce a set of features whose classification worth is evaluated by discriminatory power and feature correlation considerations. We critically appraised the performance of our method and GLCM in pairwise classification of images from visually similar texture classes, captured from Markov Random Field (MRF) synthesized, natural, and biological origins. In these cross-validated classification trials, our method demonstrated significant benefits over GLCM, including increased feature discriminatory power, automatic feature adaptability, and significantly improved classification performance.

Capacity limits for the detection of changing visual features

Capacity limits in visual attention have traditionally been studied using static arrays of elements from which an observer must detect a target defined by a certain visual feature or combination of features. In the current study we use this visual search paradigm, with accuracy as the dependent variable, to examine attentional capacity limits for different visual features undergoing change over time. In Experiment 1, detectability of a single changing target was measured under conditions where the type of change (size, speed, colour), the magnitude of change, the set size and homogeneity of the unchanging distractors were all systematically varied. Psychometric function slopes were calculated for different experimental conditions and ‘change thresholds’extracted from these slopes were used in Experiment 2, in which multiple supra-threshold changes were made, simultaneously, either to a single or to two or three different stimulus elements. These experiments give an objective psychometric paradigm for measuring changes in visual features over time. Results favour object-based accounts of visual attention, and show consistent differences in the allocation of attentional capacity to different perceptual dimensions.

Effects of data characteristics on the results of reserve selection algorithms

We tested the effects of four data characteristics on the results of reserve selection algorithms. The data characteristics were nestedness of features (land types in this case), rarity of features, size variation of sites (potential reserves) and size of data sets (numbers of sites and features). We manipulated data sets to produce three levels, with replication, of each of these data characteristics while holding the other three characteristics constant. We then used an optimizing algorithm and three heuristic algorithms to select sites to solve several reservation problems. We measured efficiency as the number or total area of selected sites, indicating the relative cost of a reserve system. Higher nestedness increased the efficiency of all algorithms (reduced the total cost of new reserves). Higher rarity reduced the efficiency of all algorithms (increased the total cost of new reserves). More variation in site size increased the efficiency of all algorithms expressed in terms of total area of selected sites. We measured the suboptimality of heuristic algorithms as the percentage increase of their results over optimal (minimum possible) results. Suboptimality is a measure of the reliability of heuristics as indicative costing analyses. Higher rarity reduced the suboptimality of heuristics (increased their reliability) and there is some evidence that more size variation did the same for the total area of selected sites. We discuss the implications of these results for the use of reserve selection algorithms as indicative and real-world planning tools.

Adsorption of ethylene on graphitized thermal carbon black and in slit pores: A computer simulation study

In this paper, we studied vapor-liquid equilibria (VLE) and adsorption of ethylene on graphitized thermal carbon black and in slit pores whose walls are composed of graphene layers. Simple models of a one-center Lennard-Jones (LJ) potential and a two-center united atom (UA)-LJ potential are investigated to study the impact of the choice of potential models in the description of VLE and adsorption behavior. Here, we used a Monte Carlo simulation method with grand canonical Monte Carlo (GCMC) and Gibbs ensemble Monte Carlo ensembles. The one-center potential model cannot describe adequately the VLE over the practical range of temperature from the triple point to the critical point. On the other hand, the two-center potential model (Wick et al. J. Phys. Chem. B 2000, 104, 8008-8016) performs well in the description of VLE (saturated vapor and liquid densities and vapor pressure) over the wide range of temperature. This UA-LJ model is then used in the study of adsorption of ethylene on graphitized thermal carbon black and in slit pores. Agreement between the GCMC simulation results and the experimental data on graphitized thermal carbon black for moderate temperatures is excellent, demonstrating that the potential of the GCMC method and the proper choice of potential model are essential to investigate adsorption. For slit pores of various sizes, we have found that the behavior of ethylene exhibits a number of features that are not manifested in the study of spherical LJ particles. In particular, the singlet density distribution versus distance across the pore and the angle between the molecular axis and the z direction provide rich information about the way molecules arrange themselves when the pore width is varied. Such an arrangement has been found to be very sensitive to the pore width.

Genetic basis of human testicular germ cell cancer: insights from the fruitfly and mouse

The prevalence of tumours of the germ line is increasing in the male population. This complex disease has a complex aetiology. We examine the contribution of genetic mutations to the development of germ line tumours in this review. In particular, we concentrate on fly and mouse experimental systems in order to demonstrate that mutations in some conserved genes cause pathologies typical of certain human germ cell tumours, whereas other mutations elicit phenotypes that are unique to the experimental model. Despite these experimental systems being imperfect, we show that they are useful models of human testicular germ cell tumourigenesis.

The biocompatibility of carbon nanotubes

Carbon nanotubes (CNT) are well-ordered, high aspect ratio allotropes of carbon. The two main variants, single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) both possess a high tensile strength, are ultra-light weight, and have excellent chemical and thermal stability. They also possess semi- and metallic-conductive properties. This startling array of features has led to many proposed applications in the biomedical field, including biosensors, drug and vaccine delivery and the preparation of unique biomaterials such as reinforced and/or conductive polymer nanocomposites. Despite an explosion of research into potential devices and applications, it is only recently that information on toxicity and biocompatibility has become available. This review presents a summary of the performance of existing carbon biomaterials and gives an outline of the emerging field of nanotoxicology, before reviewing the available and often conflicting investigations into the cytotoxicity and biocompatibility of CNT. Finally, future areas of investigation and possible solutions to current problems are proposed. (c) 2005 Elsevier Ltd. All rights reserved.

The (new ?) basics of a teacher professional learning community

This paper argues for the need to further theorise the concept of teacher professional learning communities and provides empirical evidence to support this case. The paper presents findings from an ongoing research project, which investigates the nature of teacher professional learning communities. The study reveals that actual communities do not conform to a normailized articulation of features, as outlined in much of the literature on this topic. Consequently, it represents an attempt to engage in the difficult but necessary task of simultaneously fashioning theory from practice, whilst interpreting theory, in practice. The study proposes that current functionalist understandings of teacher professional learning communities are based upon a literature base which is insufficiently nuanced to capture the complexity inherent within these bodies. A broader base of a more critical sociological literature is also drawn upon to better understand actual, "lived" teacher communities, which are somewhat difficult to describe. In part, such communities exhibit features of functionalist conceptions but they are also organic entities which may be quite unpredictable in their outcomes and cannot be reduced to specific features; they each have their own specific "logic of practice" (Bourdieu, 1990) which influences their activities, in their particular field. The argument proposed here is that in one particular community, this complexity may be represented by the many purposes which the community served, arguably often unbeknown to its members, which fashioned the actual community. This paper tries to add to the existing theoretical base of literature, at the same time as providing evidence to support this theorisation.

Spatial bias and temporal lag in tactile space perception

Constructing a veridical spatial map by touch poses at least two problems for a perceptual system. First, as the hand is moved through space, the locations of features may be displaced if there is an uncorrected lag between the moment the hand encounters a feature and the time that feature is encoded on a spatial map. Second, due to the sequential nature of the process, some form of memory, which itself may be subject to spatial distortions, is required for integration of spatial samples. We investigated these issues using a task involving active haptic exploration with a stylus swept back and forth in the horizontal plane at the wrist. Remembered locations of tactile targets were shifted towards the medial axis of the forearm, suggesting a central tendency in haptic spatial memory, while evidence for a displacement of perceived locations in the direction of sweep motion was consistent with processing delays.

Designing effective written health education materials: Considerations for health professionals

Purpose: Written health education materials can only be effective if they can be read, understood, and remembered by patients. The purpose of this article was to review the literature about features that should be incorporated into written health education materials to maximize their effectiveness, identify where there is consensus and debate about which features should be incorporated, and develop recommendations that health professionals can use when reviewing their existing materials and designing new materials. Method: Literature review of published research and education articles. Results: There is a large number of features that need to be considered when designing written health education materials so that they are suitable for the target audience and effective. Although there is consensus about the majority of features that should be included, further research is needed to explore the contribution of certain features, such as illustrations, to the effectiveness of written materials and the effect of well-designed written materials on patient outcomes. Conclusions: Health professionals need to provide their patients with written health education materials that are patient-orientated and designed according to the best practice principles in written health education material design.

Versatile peptide dendrimers for nucleic acid delivery

Dendrimers are nonviral vectors that have attracted interest on account of a number of features. They are structurally versatile because their size, shape, and surface charge can be selectively altered. Here we examine the functions of a new family of composite dendrimers that were synthesized with lipidic amino acid cores. These dendrimers are bifunctional because they are characterized by positively charged (lysine) modules for interaction with nucleic acids and neutral lipidic moieties for membrane lipid-bilayer transit. We assessed their structure-function correlations by a combination of molecular and biophysical techniques. Our assessment revealed an unexpected pleitropy of functions subserved by these vectors that included plasmid and oligonucleotide delivery. We also generated a firefly luciferase cell line in which we could modulate luciferase activity by RNA interference. We found that these vectors could also mediate RNA suppression of luciferase expression by delivering double-stranded luciferase transcripts generated in vitro. The structural uniqueness of these lipidic peptide dendrimers coupled with their ease and specificity of assembly and the versatility in their choice of cargo, puts them in a new category of macromolecule carriers. These vectors, therefore, have potential applications as epigenetic modifiers of gene function. (C) 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

Automatic texture segmentation for texture-based image retrieval

Texture-segmentation is the crucial initial step for texture-based image retrieval. Texture is the main difficulty faced to a segmentation method. Many image segmentation algorithms either can’t handle texture properly or can’t obtain texture features directly during segmentation which can be used for retrieval purpose. This paper describes an automatic texture segmentation algorithm based on a set of features derived from wavelet domain, which are effective in texture description for retrieval purpose. Simulation results show that the proposed algorithm can efficiently capture the textured regions in arbitrary images, with the features of each region extracted as well. The features of each textured region can be directly used to index image database with applications as texture-based image retrieval.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Towards a validation of multiple features in the assessment of emotions

This study extends previous attempts to assess emotion with single adjective descriptors, by examining semantic as well as cognitive, motivational, and intensity features of emotions. The focus was on seven negative emotions common to several emotion typologies: anger, fear, sadness, shame, pity, jealousy, and contempt. For each of these emotions, seven items were generated corresponding to cognitive appraisal about the self, cognitive appraisal about the environment, action tendency, action fantasy, synonym, antonym, and intensity range of the emotion, respectively. A pilot study established that 48 of the 49 items were linked predominantly to the specific emotions as predicted. The main data set comprising 700 subjects' ratings of relatedness between items and emotions was subjected to a series of factor analyses, which revealed that 44 of the 49 items loaded on the emotion constructs as predicted. A final factor analysis of these items uncovered seven factors accounting for 39% of the variance. These emergent factors corresponded to the hypothesized emotion constructs, with the exception of anger and fear, which were somewhat confounded. These findings lay the groundwork for the construction of an instrument to assess emotions multicomponentially.