ReadAlong: reading articles and comments together

We propose a new paradigm for displaying comments: showing comments alongside parts of the article they correspond to. We evaluate the effectiveness of various approaches for this task and show that a combination of bag of words and topic models performs the best.

Role of the Ribosomal P-Site Elements of m(2)G966, m(5)C967, and the S9 C-Terminal Tail in Maintenance of the Reading Frame during Translational Elongation in Escherichia coli

The ribosomal P-site hosts the peptidyl-tRNAs during translation elongation. Which P-site elements support these tRNA species to maintain codon-anticodon interactions has remained unclear. We investigated the effects of P-site features of methylations of G966, C967, and the conserved C-terminal tail sequence of Ser, Lys, and Arg (SKR) of the S9 ribosomal protein in maintenance of the translational reading frame of an mRNA. We generated Escherichia coli strains deleted for the SKR sequence in S9 ribosomal protein, RsmB (which methylates C967), and RsmD (which methylates G966) and used them to translate LacZ from its +1 and -1 out-of-frame constructs. We show that the S9 SKR tail prevents both the +1 and -1 frameshifts and plays a general role in holding the P-site tRNA/peptidyl-tRNA in place. In contrast, the G966 and C967 methylations did not make a direct contribution to the maintenance of the translational frame of an mRNA. However, deletion of rsmB in the S9 Delta 3 background caused significantly increased -1 frameshifting at 37 degrees C. Interestingly, the effects of the deficiency of C967 methylation were annulled when the E. coli strain was grown at 30 degrees C, supporting its context-dependent role.

Simultaneously Uncovering the Patterns of Brain Regions Involved in Different Story Reading Subprocesses

Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders.

Energy Harvesting WSNs for Accurately Estimating the Maximum Sensor Reading: Trade-Offs and Optimal Design

Computing the maximum of sensor readings arises in several environmental, health, and industrial monitoring applications of wireless sensor networks (WSNs). We characterize the several novel design trade-offs that arise when green energy harvesting (EH) WSNs, which promise perpetual lifetimes, are deployed for this purpose. The nodes harvest renewable energy from the environment for communicating their readings to a fusion node, which then periodically estimates the maximum. For a randomized transmission schedule in which a pre-specified number of randomly selected nodes transmit in a sensor data collection round, we analyze the mean absolute error (MAE), which is defined as the mean of the absolute difference between the maximum and that estimated by the fusion node in each round. We optimize the transmit power and the number of scheduled nodes to minimize the MAE, both when the nodes have channel state information (CSI) and when they do not. Our results highlight how the optimal system operation depends on the EH rate, availability and cost of acquiring CSI, quantization, and size of the scheduled subset. Our analysis applies to a general class of sensor reading and EH random processes.