Genome and transcriptome of the regeneration-competent flatworm, Macrostomum lignano.

The free-living flatworm, Macrostomum lignano has an impressive regenerative capacity. Following injury, it can regenerate almost an entirely new organism because of the presence of an abundant somatic stem cell population, the neoblasts. This set of unique properties makes many flatworms attractive organisms for studying the evolution of pathways involved in tissue self-renewal, cell-fate specification, and regeneration. The use of these organisms as models, however, is hampered by the lack of a well-assembled and annotated genome sequences, fundamental to modern genetic and molecular studies. Here we report the genomic sequence of M. lignano and an accompanying characterization of its transcriptome. The genome structure of M. lignano is remarkably complex, with ∼75% of its sequence being comprised of simple repeats and transposon sequences. This has made high-quality assembly from Illumina reads alone impossible (N50=222 bp). We therefore generated 130× coverage by long sequencing reads from the Pacific Biosciences platform to create a substantially improved assembly with an N50 of 64 Kbp. We complemented the reference genome with an assembled and annotated transcriptome, and used both of these datasets in combination to probe gene-expression patterns during regeneration, examining pathways important to stem cell function.This work is supported by National Institutes of Health Grants R37 GM062534 (to G.J.H.) and R01-HG006677 (to M.S.); National Science Foundation Grant DBI-1350041 (to M.S.); and a Swiss National Science Foundation Grant 31003A-143732 (to L.S.). This work was performed with assistance from Cold Spring Harbor Laboratory Shared Resources, which are funded, in part, by Cancer Center Support Grant 5P30CA045508.This is the final version of the article. It first appeared from PNAS via http://dx.doi.org/10.1073/pnas.151671811

Search for Nucleon Decays induced by GUT Magnetic Monopoles with the MACRO Experiment

The interaction of a Grand Unification Magnetic Monopole with a nucleon can lead to a barion-number violating process in which the nucleon decays into a lepton and one or more mesons (catalysis of nucleon decay). In this paper we report an experimental study of the effects of a catalysis process in the MACRO detector. Using a dedicated analysis we obtain new magnetic monopole (MM) flux upper limits at the level of $\sim 3\cdot 10^{-16} cm^{-2} s^{-1} sr^{-1}$ for $1.1\cdot 10^{-4} \le |\beta| \le 5\cdot 10^{-3}$, based on the search for catalysis events in the MACRO data. We also analyze the dependence of the MM flux limit on the catalysis cross section.Comment: 12 pages, Latex, 10 figures and 2 Table

Test beam results of a stereo preshower integrated in the liquid argon accordion calorimeter

This paper describes the construction of an integrated preshower within the RD3 liquid argon accordion calorimeter. It has a stereo view which enables the measurement of two transverse coordinates. The prototype was tested at CERN with electrons, photons and muons to validate its capability to work at LHC ( Energy resolution, impact point resolution, angular resolution, $\pi^o$/$\gamma$ rejection )

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery