SummarySmall RNAs (sRNAs) associated with the RNA chaperon protein Hfq are key posttranscriptional regulators of gene expression in bacteria. Deciphering the sRNA-target interactome is an essential step toward understanding the roles of sRNAs in the cellular networks. We developed a broadly applicable methodology termed RIL-seq (RNA interaction by ligation and sequencing), which integrates experimental and computational tools for in vivo transcriptome-wide identification of interactions involving Hfq-associated sRNAs. By applying this methodology to Escherichia coli we discovered an extensive network of interactions involving RNA pairs showing sequence complementarity. We expand the ensemble of targets for known sRNAs, uncover additional Hfq-bound sRNAs encoded in various genomic regions along with their trans encoded targets, and provide insights into binding and possible cycling of RNAs on Hfq. Comparison of the sRNA interactome under various conditions has revealed changes in the sRNA repertoire as well as substantial re-wiring of the network between conditions.

Figure 1: Speech-to-gesture translation example. In this paper, we study the connection between conversational gesture and speech. Here, we show the result of our model that predicts gesture from audio. From the bottom upward: the input audio, arm and hand pose predicted by our model, and video frames synthesized from pose predictions using [10]. AbstractHuman speech is often accompanied by hand and arm gestures. Given audio speech input, we generate plausible gestures to go along with the sound. Specifically, we perform cross-modal translation from "in-the-wild" monologue speech of a single speaker to their hand and arm motion. We train on unlabeled videos for which we only have noisy pseudo ground truth from an automatic pose detection system. Our proposed model significantly outperforms baseline methods in a quantitative comparison. To support research toward obtaining a computational understanding of the relationship between gesture and speech, we release a large video dataset of person-specific gestures.

DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. Here, we show that methylation patterns within populations of cells from individual somatic tissues are heterogeneous and polymorphic. Using in vitro evolution of immortalized fibroblasts for over 300 generations, we track the dynamics of polymorphic methylation at regions developing significant differential methylation on average. The data indicate that changes in population-averaged methylation occur through a stochastic process that generates a stream of local and uncorrelated methylation aberrations. Despite the stochastic nature of the process, nearly deterministic epigenetic remodeling emerges on average at loci that lose or gain resistance to methylation accumulation. Changes in the susceptibility to methylation accumulation are correlated with changes in histone modification and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical to understanding methylation dynamics in normal and cancer cells.

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We introduce and analyze an exactly soluble one-dimensional Ising model with long range interactions that exhibits a mixed-order transition, namely a phase transition in which the order parameter is discontinuous as in first order transitions while the correlation length diverges as in second order transitions. Such transitions are known to appear in a diverse classes of models that are seemingly unrelated. The model we present serves as a link between two classes of models that exhibit a mixed-order transition in one dimension, namely, spin models with a coupling constant that decays as the inverse distance squared and models of depinning transitions, thus making a step towards a unifying framework.

The dynamics of a loop in DNA molecules at the denaturation transition is studied by scaling arguments and numerical simulations. The autocorrelation function of the state of complementary bases (either closed or open) is calculated. The long-time decay of the autocorrelation function is expressed in terms of the loop exponent c both for homopolymers and heteropolymers. This suggests an experimental method for measuring the exponent c using florescence correlation spectroscopy.PACS numbers: 82.37.-j, 87.14.GgThe thermodynamic properties of DNA near the thermal denaturation transition have been extensively studied during the last few decades [1, 2]. At low temperatures a small fraction of the base pairs are unbound, forming loops of fluctuating lengths. These loops increase in size as the temperature is raised, until the denaturation transition is reached and the two strands separate. Experiments using uv absorption and specific heat measurements have yielded valuable information on equilibrium properties of DNA [2]. Recently, single molecules techniques, most notably Fluorescence Correlation Spectroscopy (FCS) have been used to study dynamical properties such as the temporal behavior of loops [3].The main theoretical approach for studying DNA denaturation has been introduced by Poland and Scheraga (PS) [4] and was used to analyze the case of homopolymers. It was found that the dependence of the entropy of a loop on its length plays a dominant role in determining the thermodynamic behavior near the transition. On general grounds one can argue that the entropy of a loop of length n takes the form S = k B log(Ω(n)), where Ω(n) ∼ s n /n c is the number of loop configurations. Here s is a model-dependent constant and c is a universal exponent whose numerical value has been debated over the years and was found to depend on the degree in which excluded volume interactions are taken into account [4,5,6]. When excluded volume interactions both within a loop and between the loop and the rest of the chain are taken into account one finds c ≃ 2.12 [1,6]. This result, which predicts a first order denaturation transition, has been verified numerically [7]. While numerical studies of the model with excluded volume interaction yield a clear first order transition [8], a direct experimental measurement of c is rather difficult and has not been carried out so far. Theoretical studies of the case of a heteropolymers suggest that disorder makes the transition of order higher than two [9,10].In this paper we analyze the loop dynamics at the denaturation transition. The analysis suggests a method for measuring the exponent c. We focus on predictions for FCS studies [3]. In these experiments one monitors the state of a base pair (whether it is open or closed) as a function of time. The measured quantity is the base pair autocorrelation function C i (t) = u i (0)u i (t) where u i (t) = 1, 0 is a variable which indicates if base pair i is open (1) or closed (0) at time t. By analyzing the loop dynamics using a scaling approach and by di...

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