Efficiency of Xist-mediated silencing on autosomes is linked to chromosomal domain organisation

BACKGROUND: X chromosome inactivation, the mechanism used by mammals to equalise dosage of X-linked genes in XX females relative to XY males, is triggered by chromosome-wide localisation of a cis-acting non-coding RNA, Xist. The mechanism of Xist RNA spreading and Xist-dependent silencing is poorly understood. A large body of evidence indicates that silencing is more efficient on the X chromosome than on autosomes, leading to the idea that the X chromosome has acquired sequences that facilitate propagation of silencing. LINE-1 (L1) repeats are relatively enriched on the X chromosome and have been proposed as candidates for these sequences. To determine the requirements for efficient silencing we have analysed the relationship of chromosome features, including L1 repeats, and the extent of silencing in cell lines carrying inducible Xist transgenes located on one of three different autosomes. RESULTS: Our results show that the organisation of the chromosome into large gene-rich and L1-rich domains is a key determinant of silencing efficiency. Specifically genes located in large gene-rich domains with low L1 density are relatively resistant to Xist-mediated silencing whereas genes located in gene-poor domains with high L1 density are silenced more efficiently. These effects are observed shortly after induction of Xist RNA expression, suggesting that chromosomal domain organisation influences establishment rather than long-term maintenance of silencing. The X chromosome and some autosomes have only small gene-rich L1-depleted domains and we suggest that this could confer the capacity for relatively efficient chromosome-wide silencing. CONCLUSIONS: This study provides insight into the requirements for efficient Xist mediated silencing and specifically identifies organisation of the chromosome into gene-rich L1-depleted and gene-poor L1-dense domains as a major influence on the ability of Xist-mediated silencing to be propagated in a continuous manner in cis

Model-based Trajectory Stitching for Improved Offline Reinforcement Learning

In many real-world applications, collecting large and high-quality datasets may be too costly or impractical. Offline reinforcement learning (RL) aims to infer an optimal decision-making policy from a fixed set of data. Getting the most information from historical data is then vital for good performance once the policy is deployed. We propose a model-based data augmentation strategy, Trajectory Stitching (TS), to improve the quality of sub-optimal historical trajectories. TS introduces unseen actions joining previously disconnected states: using a probabilistic notion of state reachability, it effectively `stitches' together parts of the historical demonstrations to generate new, higher quality ones. A stitching event consists of a transition between a pair of observed states through a synthetic and highly probable action. New actions are introduced only when they are expected to be beneficial, according to an estimated state-value function. We show that using this data augmentation strategy jointly with behavioural cloning (BC) leads to improvements over the behaviour-cloned policy from the original dataset. Improving over the BC policy could then be used as a launchpad for online RL through planning and demonstration-guided RL.Comment: Offline RL Workshop at Neural Information Processing Systems, 202

Evolving rules for document classification

We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision and recall when evaluated against a set of training documents. We describe a set of functions and terminals and provide results from a classification task using the Reuters 21578 dataset. We also suggest that because the induced rules are meaningful to a human analyst they may have a number of other uses beyond classification and provide a basis for text mining applications

Inclusive Healthcare Facilities Access and Accommodations Resource Toolkit

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