Secondary omental torsion in children: a report of two cases and review of the literature.

The Authors describe a case of omental torsion in pediatric ag

Discovering candidates for gene network expansion by variable subsetting and ranking aggregation

We present a method that produces a list of genes that are candidates for Network Expansion by Subsetting and Ranking Aggregation (NESRA) and its application to gene regulatory networks. Our group has recently developed gene@home [3], a BOINC project [1] that permits to search for candidate genes for the expansion of a gene regulatory network using gene expression data. The project adopts intensive variable-subsetting strategies enabled by the computational power provided by the volunteers who join the project by means of the BOINC client, and exploits the PC algorithm for discovering putative causal relationships within each subset of variables. The PC algorithm, whose name derives from the initials of its authors [7] and PC* [2] are algorithms that discover causal relationships among variables. In particular, PC is based on the systematic testing for conditional independence of variables given subsets of other variables, comprehensively presented and evaluated by Kalish and colleagues [4] who proposed it also for gene network reconstruction [5]. NESRA is an algorithm which runs as a postprocessor of the gene@home project that has: 1) a procedure that systematically subsets the variables, runs the PC and ranks the genes; the subsetting is iterated several times and a ranked list of candidates is produced by counting the number of times a relationship is found; 2) several ranking steps are executed with different values of the dimension of the subsets and with different number of iterations producing several ranked lists; 3) the ranked lists are aggregated by using a state-of-the-art ranking aggregator. Here we show that a single ranking step is enough to outperform PC and PC*, but with some dependency on the parameters. Moreover, we show that the output ranking aggregation method is better that the average performance of the single ranking steps. Evaluations are done by means of the gene@home project on Arabidopsis thaliana including a comparison against ARACNE [6] (Table 1). Method k=5 k=10 k=20 k=55 NESRA 0.90 0.80 0.60 0.42 ARACNE 0.2 0.3 0.35 0.45 Table 1: A. thaliana, Expansion of the Flower Organ Specification Gene Regulatory Network. NESRA and ARACNE (default parameters) precision for different values k of the length of the gene lis

Super-Earths, M Dwarfs, and Photosynthetic Organisms: Habitability in the Lab

In a few years, space telescopes will investigate our Galaxy to detect evidence of life, mainly by observing rocky planets. In the last decade, the observation of exoplanet atmospheres and the theoretical works on biosignature gasses have experienced a considerable acceleration. The most attractive feature of the realm of exoplanets is that 40% of M dwarfs host super-Earths with a minimum mass between 1 and 30 Earth masses, orbital periods shorter than 50 days, and radii between those of the Earth and Neptune (1–3.8 R⊕). Moreover, the recent finding of cyanobacteria able to use far-red (FR) light for oxygenic photosynthesis due to the synthesis of chlorophylls d and f, extending in vivo light absorption up to 750 nm, suggests the possibility of exotic photosynthesis in planets around M dwarfs. Using innovative laboratory instrumentation, we exposed different cyanobacteria to an M dwarf star simulated irradiation, comparing their responses to those under solar and FR simulated lights. As expected, in FR light, only the cyanobacteria able to synthesize chlorophyll d and f could grow. Surprisingly, all strains, both able or unable to use FR light, grew and photosynthesized under the M dwarf generated spectrum in a similar way to the solar light and much more efficiently than under the FR one. Our findings highlight the importance of simulating both the visible and FR light components of an M dwarf spectrum to correctly evaluate the photosynthetic performances of oxygenic organisms exposed under such an exotic light condition.ISSN:2075-172

NES2RA: network expansion by stratified variable subsetting and ranking aggregation

Gene network expansion is a task of the foremost importance in computational biology. Gene network expansion aims at finding new genes to expand a given known gene network. To this end, we developed gene@home, a BOINC-based project that finds candidate genes that expand known local gene networks using NESRA. In this paper, we present NES2RA, a novel approach that extends and improves NESRA by modeling, using a probability vector, the confidence of the presence of the genes belonging to the local gene network. NES2RA adopts intensive variable-subsetting strategies, enabled by the computational power provided by gene@home volunteers. In particular, we use the skeleton procedure of the PC-algorithm to discover candidate causal relationships within each subset of variables. Finally, we use state-of-the-art aggregators to combine the results into a single ranked candidate genes list. The resulting ranking guides the discovery of unknown relations between genes and a priori known local gene networks. Our experimental results show that NES2RA outperforms the PC-algorithm and its order-independent PC-stable version, ARACNE, and our previous approach, NESRA. In this paper we extensively discuss the computational aspects of the NES2RA approach and we also present and validate expansions performed on the model plant Arabidopsis thaliana and the model bacteria Escherichia coli