Architecture of the chromatin remodeler RSC and insights into its nucleosome engagement.

Eukaryotic DNA is packaged into nucleosome arrays, which are repositioned by chromatin remodeling complexes to control DNA accessibility. The Saccharomyces cerevisiae RSC (Remodeling the Structure of Chromatin) complex, a member of the SWI/SNF chromatin remodeler family, plays critical roles in genome maintenance, transcription, and DNA repair. Here, we report cryo-electron microscopy (cryo-EM) and crosslinking mass spectrometry (CLMS) studies of yeast RSC complex and show that RSC is composed of a rigid tripartite core and two flexible lobes. The core structure is scaffolded by an asymmetric Rsc8 dimer and built with the evolutionarily conserved subunits Sfh1, Rsc6, Rsc9 and Sth1. The flexible ATPase lobe, composed of helicase subunit Sth1, Arp7, Arp9 and Rtt102, is anchored to this core by the N-terminus of Sth1. Our cryo-EM analysis of RSC bound to a nucleosome core particle shows that in addition to the expected nucleosome-Sth1 interactions, RSC engages histones and nucleosomal DNA through one arm of the core structure, composed of the Rsc8 SWIRM domains, Sfh1 and Npl6. Our findings provide structural insights into the conserved assembly process for all members of the SWI/SNF family of remodelers, and illustrate how RSC selects, engages, and remodels nucleosomes

Knowledge-aware Complementary Product Representation Learning

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness. We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases. The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers

On modular decompositions of system signatures

Considering a semicoherent system made up of $n$ components having i.i.d. continuous lifetimes, Samaniego defined its structural signature as the $n$-tuple whose $k$-th coordinate is the probability that the $k$-th component failure causes the system to fail. This $n$-tuple, which depends only on the structure of the system and not on the distribution of the component lifetimes, is a very useful tool in the theoretical analysis of coherent systems. It was shown in two independent recent papers how the structural signature of a system partitioned into two disjoint modules can be computed from the signatures of these modules. In this work we consider the general case of a system partitioned into an arbitrary number of disjoint modules organized in an arbitrary way and we provide a general formula for the signature of the system in terms of the signatures of the modules. The concept of signature was recently extended to the general case of semicoherent systems whose components may have dependent lifetimes. The same definition for the $n$-tuple gives rise to the probability signature, which may depend on both the structure of the system and the probability distribution of the component lifetimes. In this general setting, we show how under a natural condition on the distribution of the lifetimes, the probability signature of the system can be expressed in terms of the probability signatures of the modules. We finally discuss a few situations where this condition holds in the non-i.i.d. and nonexchangeable cases and provide some applications of the main results

Bi-parameter Potential theory and Carleson measures for the Dirichlet space on the bidisc

We characterize the Carleson measures for the Dirichlet space on the bidisc, hence also its multiplier space. Following Maz'ya and Stegenga, the characterization is given in terms of a capacitary condition. We develop the foundations of a bi-parameter potential theory on the bidisc and prove a Strong Capacitary Inequality. In order to do so, we have to overcome the obstacle that the Maximum Principle fails in the bi-parameter theory.Comment: 44 pages, 5 figures, title changed, minor editin

On distance sets, box-counting and Ahlfors-regular sets

We obtain box-counting estimates for the pinned distance sets of (dense subsets of) planar discrete Ahlfors-regular sets of exponent $s>1$. As a corollary, we improve upon a recent result of Orponen, by showing that if $A$ is Ahlfors-regular of dimension $s>1$, then almost all pinned distance sets of $A$ have lower box-counting dimension $1$. We also show that if $A,B\subset\mathbb{R}^2$ have Hausdorff dimension $>1$ and $A$ is Ahlfors-regular, then the set of distances between $A$ and $B$ has modified lower box-counting dimension $1$, which taking $B=A$ improves Orponen's result in a different direction, by lowering packing dimension to modified lower box-counting dimension. The proofs involve ergodic-theoretic ideas, relying on the theory of CP-processes and projections.Comment: 22 pages, no figures. v2: added Corollary 1.5 on box dimension of pinned distance sets. v3: numerous fixes and clarifications based on referee report

Symptoms of major depression: Their stability, familiality, and prediction by genetic, temperamental, and childhood environmental risk factors

Background: Psychiatry has long sought to develop biological diagnostic subtypes based on symptomatic differences. This effort assumes that symptoms reflect, with good fidelity, underlying etiological processes. We address this question for major depression (MD). Methods: We examine, in twins from a population-based registry, similarity in symptom endorsement in individuals meeting criteria for last-year MD at separate interview waves and in concordant twin pairs. Among individuals with MD, we explore the impact of genetic-temperamental and child adversity risk factors on individual reported symptoms. Aggregated criteria do not separate insomnia from hypersomnia, weight gain from loss, etc. while disaggregated criteria do. Results: In twins with MD at two different waves, the mean tetrachoric correlations (+/- SEM) for aggregated and disaggregated DSM-IV A criteria were, respectively, + 0.31 +/- 0.06 and + 0.34 +/- 0.03. In monozygotic (MZ) and dizygotic (DZ) twin pairs concordant for last-year MD, the mean tetrachoric correlations for aggregated and disaggregated criteria were, respectively, + 0.33 +/- 0.07 and + 0.43 +/- 0.04, and + 0.05 +/- 0.08 and + 0.07 +/- 0.04. In individuals meeting MD criteria, neuroticism predicted the most MD symptoms (10), followed by childhood sexual abuse (8), low parental warmth (6), and genetic risk (4). Conclusions: The correlations for individual depressive symptoms over multiple episodes and within MZ twins concordant for MD are modest suggesting the important role of transient influences. The multidetermination of individual symptoms was further evidenced by their prediction by personality and exposure to early life adversities. The multiple factors influencing symptomatic presentation inMDmay contribute to our difficulties in isolating clinical depressive subtypes with distinct pathophysiologies