The dilemma of binary or weighted data in interaction networks

Despite the increasing number of studies dealing with interaction networks in the last few years, there is still a lack of knowledge about how their structural organization are affected by changes in binary or weighted data. To fill this gap, we collected ants foraging on plants with extrafloral nectaries in 10 sites within the Brazilian Amazon to evaluate if the generality, vulnerability, nestedness, and modularity observed in these ant-plant networks could be affected by changes in data categories. Specifically, we used three matrices built by different data categories: (i) binary data (i.e., presence or absence of an interaction between a plant and an ant species); (ii) frequency data (i.e., number of times in which a plant species interacted with an ant species); and (iii) abundance data (i.e., number of workers of an ant species recorded foraging on a plant species). In general, when analyzing different matrix categories, we observed changes in the structural organization of the studied ant-plant interaction networks. Surprisingly, however, at the species level, both categories of weighted data (i.e., frequency and abundance data) seem to be equally appropriate for describing the role of ant species. Our results highlight the need to expand the discussion about data categories in ecological interaction studies to understand how different data categories may lead to different ecological interpretations.     

Applications of fractal geometry to biology

The applications of concepts derived from fractal geometry tobiological problems are described. Three major applicationsare identified: modelling of structures; investigation of theoreticalproblems; and the measurment of complexity. The review concentrateson methods and algorithms, including potential problems, whichcan be used with biological problems. These algorithms are drawnfrom a wide range of literature, including some non–biologicalsources.     

Nonlinear vibrational microscopy applied to lipid biology

Optical microscopy is an indispensable tool that is driving progress in cell biology. It still is the only practical means of obtaining spatial and temporal resolution within living cells and tissues. Most prominently, fluorescence microscopy based on dye-labeling or protein fusions with fluorescent tags is a highly sensitive and specific method of visualizing biomolecules within sub-cellular structures. It is however severely limited by labeling artifacts, photo-bleaching and cytotoxicity of the labels. Coherent Raman Scattering (CRS) has emerged in the last decade as a new multiphoton microscopy technique suited for imaging unlabeled living cells in real time with high three-dimensional spatial resolution and chemical specificity. This technique has proven to be particularly successful in imaging unstained lipids from artificial membrane model systems, to living cells and tissues to whole organisms. In this article, we will review the experimental implementations of CRS microscopy and their application to imaging lipids. We will cover the theoretical background of linear and non-linear vibrational micro-spectroscopy necessary for the understanding of CRS microscopy. The different experimental implementations of CRS will be compared in terms of sensitivity limits and excitation and detection methods. Finally, we will provide an overview of the applications of CRS microscopy to lipid biology.     

Applications of synthetic oligodeoxyribonucleotides to problems in molecular biology

The applications of synthetic oligodeoxyribonucleotides to problems in molecular biology described in this article are those where the oligodeoxyribonucleotide is a probe for a specific region of a nucleic acid. This includes the isolation of the iso-1-cytochrome c gene of yeast; the sequence determination of RNAs and DNAs including regions of double-stranded DNA; the introduction of defined site-specific point mutations into bacteriophage OX174 and in the in vitro selection of mutant DNA from a mixture with wild-type DNA.     

Genomic and proteomic techniques applied to reproductive biology

A report on the Frontiers in Reproduction Symposium 2001 'Reproductive genetics, genomics and proteomics: advances in genetic, molecular and bioinformatics techniques', Cambridge, USA, 30 June to 1 July, 2001.     

Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish

Optical projection tomography (OPT) is a 3D mesoscopic imaging modality that can utilize absorption or fluorescence contrast. 3D images can be rapidly reconstructed from tomographic data sets sampled with sufficient numbers of projection angles using the Radon transform, as is typically implemented with optically cleared samples of the mm‐to‐cm scale. For in vivo imaging, considerations of phototoxicity and the need to maintain animals under anesthesia typically preclude the acquisition of OPT data at a sufficient number of angles to avoid artifacts in the reconstructed images. For sparse samples, this can be addressed with iterative algorithms to reconstruct 3D images from undersampled OPT data, but the data processing times present a significant challenge for studies imaging multiple animals. We show here that convolutional neural networks (CNN) can be used in place of iterative algorithms to remove artifacts—reducing processing time for an undersampled in vivo zebrafish dataset from 77 to 15 minutes. We also show that using CNN produces reconstructions of equivalent quality to compressed sensing with 40% fewer projections. We further show that diverse training data classes, for example, ex vivo mouse tissue data, can be used for CNN‐based reconstructions of OPT data of other species including live zebrafish.      

Applications of microfluidics in chemical biology

This review discusses the application of microfluidics in chemical biology. It aims to introduce the reader to microfluidics, describe characteristics of microfluidic systems that are useful in studying chemical biology, and summarize recent progress at the interface of these two fields. The review concludes with an assessment of future directions and opportunities of microfluidics in chemical biology.     

Applications of neural networks to colour recognition

Colour specification and matching are important tasks used in a number of different industries. However, current methods can employ very complicated, elaborate pieces of laboratory equipment which are not portable, such as spectrophotometers with combinations of calibrated light sources and a large number of narrow band filters. Simpler, portable devices are typically not sophisticated enough to give a high degree of quality control due to the less sophisticated light sources and the necessarily smaller number of filters employed. Lastly, techniques such as the `match by eye' method can lead to deceptive metameric matches. This paper outlines the beginning of the development of a colour measurement system which is portable, easy to use, and more accurate than other portable devices by virtue of an artificial neural network used to analyze the data. With specialized training of the neural network, it could even be used in applications such as teeth and skin colour measurements. Received: 6 November 1996 / Accepted in revised form: 13 July 1998     

Inferring meaningful pathways in weighted metabolic networks

An approach is presented for computing meaningful pathways in the network of small molecule metabolism comprising the chemical reactions characterized in all organisms. The metabolic network is described as a weighted graph in which all the compounds are included, but each compound is assigned a weight equal to the number of reactions in which it participates. Path finding is performed in this graph by searching for one or more paths with lowest weight. Performance is evaluated systematically by computing paths between the first and last reactions in annotated metabolic pathways, and comparing the intermediate reactions in the computed pathways to those in the annotated ones. For the sake of comparison, paths are computed also in the un-weighted raw (all compounds and reactions) and filtered (highly connected pool metabolites removed) metabolic graphs, respectively. The correspondence between the computed and annotated pathways is very poor (<30%) in the raw graph; increasing to approximately 65% in the filtered graph; reaching approximately 85% in the weighted graph. Considering the best-matching path among the five lightest paths increases the correspondence to 92%, on average. We then show that the average distance between pairs of metabolites is significantly larger in the weighted graph than in the raw unfiltered graph, suggesting that the small-world properties previously reported for metabolic networks probably result from irrelevant shortcuts through pool metabolites. In addition, we provide evidence that the length of the shortest path in the weighted graph represents a valid measure of the "metabolic distance" between enzymes. We suggest that the success of our simplistic approach is rooted in the high degree of specificity of the reactions in metabolic pathways, presumably reflecting thermodynamic constraints operating in these pathways. We expect our approach to find useful applications in inferring metabolic pathways in newly sequenced genomes.     

An extension of the weighted dissimilarity test to association study in families

Abstact Association studies for complex diseases based on pedigree haplotype or genotype data have received increasing attention in the last few years. The similarity tests are appealing for these studies because they take into account of the DNA structure, but they have blind areas on which significant association can not be detected. Recently, we developed a dissimilarity method for this problem based on independent haplotype data, which eliminates the blind areas of the existing methods. As DNA collected on families are common in practice, and the data are either of the form of genotype or haplotype. Here we extend our method for association study to data on families. It can be used to evaluate different designs in terms of power. Simulation studies confirmed that the extended method improves the type I error rate and power. Applying this method to the Genetic Analysis Workshop 14 alcoholism data, we find that markers rs716581, rs1017418, rs1332184 and rs1943418 on chromosomes 1, 2, 9 and 18 yield strong signal (with P value 0.001 or lower) for association with alcoholism. Our work can serve as a guide in the design of association studies in families.