Emergence of skew distributions in controlled growth processes

Starting from a master equation, we derive the evolution equation for the size distribution of elements in an evolving system, where each element can grow, divide into two, and produce new elements. We then probe general solutions of the evolution quation, to obtain such skew distributions as power-law, log-normal, and Weibull distributions, depending on the growth or division and production. Specifically, repeated production of elements of uniform size leads to power-law distributions, whereas production of elements with the size distributed according to the current distribution as well as no production of new elements results in log-normal distributions. Finally, division into two, or binary fission, bears Weibull distributions. Numerical simulations are also carried out, confirming the validity of the obtained solutions.Comment: 9 pages, 3 figure

Arginine–glycine–aspartic acid functional branched semi-interpenetrating hydrogels

For the first time a series of functional hydrogels based on semi-interpenetrating networks with both branched and crosslinked polymer components have been prepared and we show the successful use of these materials as substrates for cell culture. The materials consist of highly branched poly(N-isopropyl acrylamide)s with peptide functionalised end groups in a continuous phase of crosslinked poly(vinyl pyrrolidone). Functionalisation of the end groups of the branched polymer component with the GRGDS peptide produces a hydrogel that supports cell adhesion and proliferation. The materials provide a new synthetic functional biomaterial that has many of the features of extracellular matrix, and as such can be used to support tissue regeneration and cell culture. This class of high water content hydrogel material has important advantages over other functional hydrogels in its synthesis and does not require postprocessing modifications nor are functional-monomers, which change the polymerisation process, required. Thus, the systems are amenable to large scale and bespoke manufacturing using conventional moulding or additive manufacturing techniques. Processing using additive manufacturing is exemplified by producing tubes using microstereolithography

Ventilatory Chaos Is Impaired in Carotid Atherosclerosis

Ventilatory chaos is strongly linked to the activity of central pattern generators, alone or influenced by respiratory or cardiovascular afferents. We hypothesized that carotid atherosclerosis should alter ventilatory chaos through baroreflex and autonomic nervous system dysfunctions. Chaotic dynamics of inspiratory flow was prospectively evaluated in 75 subjects undergoing carotid ultrasonography: 27 with severe carotid stenosis (>70%), 23 with moderate stenosis (<70%), and 25 controls. Chaos was characterized by the noise titration method, the correlation dimension and the largest Lyapunov exponent. Baroreflex sensitivity was estimated in the frequency domain. In the control group, 92% of the time series exhibit nonlinear deterministic chaos with positive noise limit, whereas only 68% had a positive noise limit value in the stenoses groups. Ventilatory chaos was impaired in the groups with carotid stenoses, with significant parallel decrease in the noise limit value, correlation dimension and largest Lyapunov exponent, as compared to controls. In multiple regression models, the percentage of carotid stenosis was the best in predicting the correlation dimension (p<0.001, adjusted R2: 0.35) and largest Lyapunov exponent (p<0.001, adjusted R2: 0.6). Baroreflex sensitivity also predicted the correlation dimension values (p = 0.05), and the LLE (p = 0.08). Plaque removal after carotid surgery reversed the loss of ventilatory complexity. To conclude, ventilatory chaos is impaired in carotid atherosclerosis. These findings depend on the severity of the stenosis, its localization, plaque surface and morphology features, and is independently associated with baroreflex sensitivity reduction. These findings should help to understand the determinants of ventilatory complexity and breathing control in pathological conditions

Statistical and visual differentiation of subcellular imaging

<p>Abstract</p> <p>Background</p> <p>Automated microscopy technologies have led to a rapid growth in imaging data on a scale comparable to that of the genomic revolution. High throughput screens are now being performed to determine the localisation of all of proteins in a proteome. Closer to the bench, large image sets of proteins in treated and untreated cells are being captured on a daily basis to determine function and interactions. Hence there is a need for new methodologies and protocols to test for difference in subcellular imaging both to remove bias and enable throughput. Here we introduce a novel method of statistical testing, and supporting software, to give a rigorous test for difference in imaging. We also outline the key questions and steps in establishing an analysis pipeline.</p> <p>Results</p> <p>The methodology is tested on a high throughput set of images of 10 subcellular localisations, and it is shown that the localisations may be distinguished to a statistically significant degree with as few as 12 images of each. Further, subtle changes in a protein's distribution between nocodazole treated and control experiments are shown to be detectable. The effect of outlier images is also examined and it is shown that while the significance of the test may be reduced by outliers this may be compensated for by utilising more images. Finally, the test is compared to previous work and shown to be more sensitive in detecting difference. The methodology has been implemented within the iCluster system for visualising and clustering bio-image sets.</p> <p>Conclusion</p> <p>The aim here is to establish a methodology and protocol for testing for difference in subcellular imaging, and to provide tools to do so. While iCluster is applicable to moderate (<1000) size image sets, the statistical test is simple to implement and will readily be adapted to high throughput pipelines to provide more sensitive discrimination of difference.</p

A highly efficient multi-core algorithm for clustering extremely large datasets

<p>Abstract</p> <p>Background</p> <p>In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer.</p> <p>Results</p> <p>We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization.</p> <p>Conclusions</p> <p>Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer.</p

Mining protein loops using a structural alphabet and statistical exceptionality

<p>Abstract</p> <p>Background</p> <p>Protein loops encompass 50% of protein residues in available three-dimensional structures. These regions are often involved in protein functions, e.g. binding site, catalytic pocket... However, the description of protein loops with conventional tools is an uneasy task. Regular secondary structures, helices and strands, have been widely studied whereas loops, because they are highly variable in terms of sequence and structure, are difficult to analyze. Due to data sparsity, long loops have rarely been systematically studied.</p> <p>Results</p> <p>We developed a simple and accurate method that allows the description and analysis of the structures of short and long loops using structural motifs without restriction on loop length. This method is based on the structural alphabet HMM-SA. HMM-SA allows the simplification of a three-dimensional protein structure into a one-dimensional string of states, where each state is a four-residue prototype fragment, called structural letter. The difficult task of the structural grouping of huge data sets is thus easily accomplished by handling structural letter strings as in conventional protein sequence analysis. We systematically extracted all seven-residue fragments in a bank of 93000 protein loops and grouped them according to the structural-letter sequence, named structural word. This approach permits a systematic analysis of loops of all sizes since we consider the structural motifs of seven residues rather than complete loops. We focused the analysis on highly recurrent words of loops (observed more than 30 times). Our study reveals that 73% of loop-lengths are covered by only 3310 highly recurrent structural words out of 28274 observed words). These structural words have low structural variability (mean RMSd of 0.85 Å). As expected, half of these motifs display a flanking-region preference but interestingly, two thirds are shared by short (less than 12 residues) and long loops. Moreover, half of recurrent motifs exhibit a significant level of amino-acid conservation with at least four significant positions and 87% of long loops contain at least one such word. We complement our analysis with the detection of statistically over-represented patterns of structural letters as in conventional DNA sequence analysis. About 30% (930) of structural words are over-represented, and cover about 40% of loop lengths. Interestingly, these words exhibit lower structural variability and higher sequential specificity, suggesting structural or functional constraints.</p> <p>Conclusions</p> <p>We developed a method to systematically decompose and study protein loops using recurrent structural motifs. This method is based on the structural alphabet HMM-SA and not on structural alignment and geometrical parameters. We extracted meaningful structural motifs that are found in both short and long loops. To our knowledge, it is the first time that pattern mining helps to increase the signal-to-noise ratio in protein loops. This finding helps to better describe protein loops and might permit to decrease the complexity of long-loop analysis. Detailed results are available at <url>http://www.mti.univ-paris-diderot.fr/publication/supplementary/2009/ACCLoop/</url>.</p

Covering Chemical Diversity of Genetically-Modified Tomatoes Using Metabolomics for Objective Substantial Equivalence Assessment

As metabolomics can provide a biochemical snapshot of an organism's phenotype it is a promising approach for charting the unintended effects of genetic modification. A critical obstacle for this application is the inherently limited metabolomic coverage of any single analytical platform. We propose using multiple analytical platforms for the direct acquisition of an interpretable data set of estimable chemical diversity. As an example, we report an application of our multi-platform approach that assesses the substantial equivalence of tomatoes over-expressing the taste-modifying protein miraculin. In combination, the chosen platforms detected compounds that represent 86% of the estimated chemical diversity of the metabolites listed in the LycoCyc database. Following a proof-of-safety approach, we show that % had an acceptable range of variation while simultaneously indicating a reproducible transformation-related metabolic signature. We conclude that multi-platform metabolomics is an approach that is both sensitive and robust and that it constitutes a good starting point for characterizing genetically modified organisms

Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning

Reinforcement learning (RL) provides an influential characterization of the brain's mechanisms for learning to make advantageous choices. An important problem, though, is how complex tasks can be represented in a way that enables efficient learning. We consider this problem through the lens of spatial navigation, examining how two of the brain's location representations—hippocampal place cells and entorhinal grid cells—are adapted to serve as basis functions for approximating value over space for RL. Although much previous work has focused on these systems' roles in combining upstream sensory cues to track location, revisiting these representations with a focus on how they support this downstream decision function offers complementary insights into their characteristics. Rather than localization, the key problem in learning is generalization between past and present situations, which may not match perfectly. Accordingly, although neural populations collectively offer a precise representation of position, our simulations of navigational tasks verify the suggestion that RL gains efficiency from the more diffuse tuning of individual neurons, which allows learning about rewards to generalize over longer distances given fewer training experiences. However, work on generalization in RL suggests the underlying representation should respect the environment's layout. In particular, although it is often assumed that neurons track location in Euclidean coordinates (that a place cell's activity declines “as the crow flies” away from its peak), the relevant metric for value is geodesic: the distance along a path, around any obstacles. We formalize this intuition and present simulations showing how Euclidean, but not geodesic, representations can interfere with RL by generalizing inappropriately across barriers. Our proposal that place and grid responses should be modulated by geodesic distances suggests novel predictions about how obstacles should affect spatial firing fields, which provides a new viewpoint on data concerning both spatial codes

Shanghaied into the future: the Asianization of the future Metropolis in post-Blade Runner cinema

The clichéd 1930–1950 Western cinematic images of Shanghai as a fascinating den of iniquity, and, in contrast, as a beacon of modernity, were merged in Fritz Lang’s Metropolis. As a result, a new standard emerged in science ction lms for the representation of future urban conglomerates: the Asianized metropolis. e standard set by this lm, of a dark dystopian city, populated by creatures of all races and genetic codes, will be adopted in most of the representations of future cities in non-Asian cinema. is article traces the representation of Shanghai in Western cinema from its earliest days (1932– Shanghai Express) through Blade Runner (1982) to the present (2013– Her). Shanghai, already in the early 1930s, sported extremely daring examples of modern architecture and, at the same time, in non-Asian cinema, was represented as a city of sin and depravity. is dualistic representation became the standard image of the future Asianized city, where its debauchery was o en complemented by modernity; therefore, it is all the more seedy. Moreover, it is Asianized, the “Yellow Peril” incarnated in a new, much more subtle, much more dangerous way. As such, it is deserving of destruction, like Sodom and Gomorrah