Surface Morphology and Structural Modification Induced by Femtosecond Pulses in Hydrogenated Amorphous Silicon Films.

This work investigates the modification, resulting from fs-laser irradiation (150 fs, 775 nm and 1 kHz), on the structure and surface morphology of hydrogenated amorphous silicon (a-Si:H) thin films. The sample morphology was studied by performing a statistical analyzes of atomic force microscopy images, using a specially developed software that identifies and characterizes the domains (spikes) produced by the laser irradiation. For a fluence of 3.1 MJ/m2, we observed formation of spikes with smaller average height distribution, centered at around 15 nm, while for fluencies higher than 3.7 MJ/m2 aggregation of the produced spikes dominates the sample morphology. On the other hand, Raman spectroscopy revealed that a higher crystalline fraction (73%) is obtained for higher fluences (> 3.1 MJ/m2), which is accompanied by a decrease in the size of the produced crystals. Therefore, such results indicate that there is a trade-off between the spike distribution, crystallization fraction and size of the nanocrystals attained by laser irradiation, which has to be taken into account when using such approach for the development of devices.

Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system.

Many putative genetic factors that confer risk to neurodevelopmental disorders such as autism spectrum disorders (ASDs) and X-linked intellectual disability (XLID), and to neuropsychiatric disorders including attention deficit hyperactivity disorder (ADHD) and schizophrenia (SZ) have been identified in individuals from diverse human populations. Although there is significant aetiological heterogeneity within and between these conditions, recent data show that genetic factors contribute to their comorbidity. Many studies have identified candidate gene associations for these mental health disorders, albeit this is often done in a piecemeal fashion with little regard to the inherent molecular complexity. Here, we sought to abstract relationships from our knowledge of systems level biology to help understand the unique and common genetic drivers of these conditions. We undertook a global and systematic approach to build and integrate available data in gene networks associated with ASDs, XLID, ADHD and SZ. Complex network concepts and computational methods were used to investigate whether candidate genes associated with these conditions were related through mechanisms of gene regulation, functional protein-protein interactions, transcription factor (TF) and microRNA (miRNA) binding sites. Although our analyses show that genetic variations associated with the four disorders can occur in the same molecular pathways and functional domains, including synaptic transmission, there are patterns of variation that define significant differences between disorders. Of particular interest is DNA variations located in intergenic regions that comprise regulatory sites for TFs or miRNA. Our approach provides a hypothetical framework, which will help discovery and analysis of candidate genes associated with neurodevelopmental and neuropsychiatric disorders.

Prominent effect of soil network heterogeneity on microbial invasion.

Using a network representation for real soil samples and mathematical models for microbial spread, we show that the structural heterogeneity of the soil habitat may have a very significant influence on the size of microbial invasions of the soil pore space. In particular, neglecting the soil structural heterogeneity may lead to a substantial underestimation of microbial invasion. Such effects are explained in terms of a crucial interplay between heterogeneity in microbial spread and heterogeneity in the topology of soil networks. The main influence of network topology on invasion is linked to the existence of long channels in soil networks that may act as bridges for transmission of microorganisms between distant parts of soil.

Epidemics in networks of spatially correlated three-dimensional root-branching structures.

Using digitized images of the three-dimensional, branching structures for root systems of bean seedlings, together with analytical and numerical methods that map a common susceptible-infected-recovered ('SIR') epidemiological model onto the bond percolation problem, we show how the spatially correlated branching structures of plant roots affect transmission efficiencies, and hence the invasion criterion, for a soil-borne pathogen as it spreads through ensembles of morphologically complex hosts. We conclude that the inherent heterogeneities in transmissibilities arising from correlations in the degrees of overlap between neighbouring plants render a population of root systems less susceptible to epidemic invasion than a corresponding homogeneous system. Several components of morphological complexity are analysed that contribute to disorder and heterogeneities in the transmissibility of infection. Anisotropy in root shape is shown to increase resilience to epidemic invasion, while increasing the degree of branching enhances the spread of epidemics in the population of roots. Some extension of the methods for other epidemiological systems are discussed.

Knowledge acquisition by networks of interacting agents in the presence of observation errors.

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Rényi or Barabási-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.

Automatic contour extraction from 2D neuron images.

This work describes a novel methodology for automatic contour extraction from 2D images of 3D neurons (e.g. camera lucida images and other types of 2D microscopy). Most contour-based shape analysis methods cannot be used to characterize such cells because of overlaps between neuronal processes. The proposed framework is specifically aimed at the problem of contour following even in presence of multiple overlaps. First, the input image is preprocessed in order to obtain an 8-connected skeleton with one-pixel-wide branches, as well as a set of critical regions (i.e., bifurcations and crossings). Next, for each subtree, the tracking stage iteratively labels all valid pixel of branches, up to a critical region, where it determines the suitable direction to proceed. Finally, the labeled skeleton segments are followed in order to yield the parametric contour of the neuronal shape under analysis. The reported system was successfully tested with respect to several images and the results from a set of three neuron images are presented here, each pertaining to a different class, i.e. alpha, delta and epsilon ganglion cells, containing a total of 34 crossings. The algorithms successfully got across all these overlaps. The method has also been found to exhibit robustness even for images with close parallel segments. The proposed method is robust and may be implemented in an efficient manner. The introduction of this approach should pave the way for more systematic application of contour-based shape analysis methods in neuronal morphology.

A comparative analysis of highly conserved sex-determining genes between Apis mellifera and Drosophila melanogaster

A comparison of the most conserved sex-determining genes between the fruit fly, Drosophila melanogaster, and the honey bee, Apis mellifera, was performed with bioinformatics tools developed for computational molecular biology. An initial set of protein sequences already described in the fruit fly as participants of the sex-determining cascade was retrieved from the Gene Ontology database (http://www.geneontology.org/) and aligned against a database of protein sequences predicted from the honey bee genome. The doublesex (dsx) gene is considered one of the most conserved sex-determining genes among metazoans, and a male-specific partial cDNA of putative A. mellifera dsx gene (Amdsx) was identified experimentally. The theoretical predictions were developed in the context of sequence similarity. Experimental evidence indicates that dsx is present in embryos and larvae, and that it encodes a transcription factor widely conserved in metazoans, containing a DM DNA-binding domain implicated in the regulation of the expression of genes involved in sexual phenotype formation.

A computational approach to characterization of bovine sperm chromatin alterations.

We describe here a computational morphology-based approach to the investigation of possible causes of chromatin alterations in sperm. A comprehensive set of state-of-the-art and geometric measures are computationally extracted from toluidine blue stained images and analyzed to infer the possible processes leading to normal and abnormal chromatin formation while seeking a possible taxonomy of chromatin alterations and their influence on sperm head morphology. Using this methodology, we have identified higher chromatin fragility at some specific points of the sperm head. Despite the lack of correlation between morphologies of sperm head and chromatin structure, four main morphological types of chromatin alterations in bull spermatozoa have been identified and their possible causes discussed.

Mathematical characterization of three-dimensional gene expression patterns.

MOTIVATION: The importance of a systematic methodology for the mathematical characterization of three-dimensional gene expression patterns in embryonic development. METHODS: By combining lacunarity and multiscale fractal dimension analyses with computer-based methods of three-dimensional reconstruction, it becomes possible to extract new information from in situ hybridization studies. Lacunarity and fractality are appropriate measures for the cloud-like gene activation signals in embryonic tissues. The newly introduced multiscale method provides a natural extension of the fractal dimension concept, being capable of characterizing the fractality of geometrical patterns in terms of spatial scale. This tool can be systematically applied to three-dimensional patterns of gene expression. RESULTS: Applications are illustrated using the three-dimensional expression patterns of the myogenic marker gene Myf5 in a series of differentiating somites of a mouse embryo.

Automatic characterization and classification of ganglion cells from the salamander retina.

The classification of retinal ganglion cells according to their morphological features is addressed by using a comprehensive set of shape measures and several clustering strategies. The morphological features considered include many common measures (such as dendritic radii and the number of dendritic segments) and three new quantifiable measures: 1) the area of influence of the dendritic tree as calculated in an operator-independent manner by using Minkowski sausages; 2) the complexity of tortuousity along each dendritic segment as represented by the 3D bending energy; and 3) the coverage factor as calculated by using the Bouligand-Minkowski fractal dimension, which is more accurate than the commonly used box-counting algorithm. We evaluated four clustering approaches including the k-means and Ward's hierarchical clustering methods. By using these highly quantifiable methods to group the cells into classes, the present work has extended and reassessed the analysis of 68 ganglion cells from the tiger salamander previously classified by Toris et al. ([1995] J. Comp. Neurol. 352:535-559). Though substantiating the number of classes (5) previously proposed by Toris et al., the results obtained here indicate a number of discrepancies among the members of each class, especially regarding the border between two classes, originally called the medium simple and the medium complex cells. Such an effect has motivated the proposal of new names for the medium simple and medium complex classes, now called small highly complex and medium cells, respectively. Also included in the present article are comprehensive statistics of each class, correlations among all the adopted shape measures, and examples of the cells from each class. The resultant classes that emerged were compared using their electrotonic characteristics and physiological profiles.

Neural cell classification by wavelets and multiscale curvature.

A new approach to automatic classification of retinal ganglion cells using multiscale techniques including the continuous wavelet transform, curvature, and standard pattern recognition techniques is described. Each neural cell is represented by its outer contour, and the wavelet transform is calculated from the complex signal defined by the aforementioned contour, leading to the so-called W-representation (Antoine et al. 1996). The normalized multiscale wavelet energy (NMWE) is used to define a set of shape measures associated with the number of details of the shape for a broad range of spatial scales. Next, the more discriminating NMWE coefficients are chosen through a feature ordering technique and fed to statistical classifiers. In addition, the normalized multiscale bending energy (NMBE) is discussed as a means of neural shape description for classification purposes based on the multiscale curvature, i.e. the curvegram, of the neural contour. It is shown that both shape descriptors are suitable for shape classification, presenting similar classification performance. In fact, NMBE has a slightly better recognition rate than NMWE in our experiments. On the other hand, NMWE is less computationally expensive than NMBE, presenting also the potentially useful property of allowing the use of more suitable different analyzing wavelets, depending on the problem under consideration. Therefore, both measures are related and provide a good framework for the design of neural cell description and classification. The methods described in this work have been successfully applied to the classification of two classes of cat retinal ganglion cells, namely alpha and beta (henceforth referred as alpha-cells and beta-cells, respectively), and three statistical classifiers were considered: minimum-distance, k-nearest neighbours and maximum likelihood. The mean recognition rates are near 90%, which is superior to the other shape measures considered. It is argued here that the proposed technique can be adopted as a new general methodology for multiscale shape analysis and recognition, being applicable also to other problems in biological shape characterization in neuroscience and general biomedical image analysis. In the context of analysis of shape complexity, the multiscale energies are coherent with subjective judgements by humans.