Quantitative principles of cis-translational control by general mRNA sequence features in eukaryotes.

BackgroundGeneral translational cis-elements are present in the mRNAs of all genes and affect the recruitment, assembly, and progress of preinitiation complexes and the ribosome under many physiological states. These elements include mRNA folding, upstream open reading frames, specific nucleotides flanking the initiating AUG codon, protein coding sequence length, and codon usage. The quantitative contributions of these sequence features and how and why they coordinate to control translation rates are not well understood.ResultsHere, we show that these sequence features specify 42-81% of the variance in translation rates in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Mus musculus, and Homo sapiens. We establish that control by RNA secondary structure is chiefly mediated by highly folded 25-60 nucleotide segments within mRNA 5' regions, that changes in tri-nucleotide frequencies between highly and poorly translated 5' regions are correlated between all species, and that control by distinct biochemical processes is extensively correlated as is regulation by a single process acting in different parts of the same mRNA.ConclusionsOur work shows that general features control a much larger fraction of the variance in translation rates than previously realized. We provide a more detailed and accurate understanding of the aspects of RNA structure that directs translation in diverse eukaryotes. In addition, we note that the strongly correlated regulation between and within cis-control features will cause more even densities of translational complexes along each mRNA and therefore more efficient use of the translation machinery by the cell

KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles

We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness). The effectiveness of our approach is demonstrated on two collections of content-representative titles in the domains of Computer Science and Physics.Comment: 9 page

Post-Stack Seismic Characterization of Pore Structure Variations for Predicting Permeability Heterogeneity in Deeply-Buried Carbonate Reservoirs, Puguang Gas Field, China

Alteration of depositional environment and diagenesis of carbonate rocks create various pore structures that cause strong heterogeneity in permeability. In this research, the petrophysical and elastic characteristics of diverse carbonate reservoir pore types in deeply-buried Puguang Gas Field, China are analyzed by integrating core, well log and seismic data. Core and well log measurements were first investigated using a frame flexibility factor (γ) derived from a rock physics model of poroelasticity to classify different pore types in Feixianguan Formation of Puguang Gas Field and build the relationship between porosity and permeability for different pore type groups. The frame flexibility factor (γ) has a good correlation with pore shape instead of porosity and can be used as the pore structure indicator to classify moldic (γ 15) in the studied reservoir. When 4.5 < γ < 5.5, the reservoir rocks have mixed pore types, including both moldic and intercrystalline pores. Two distinct permeability trends were observed within two main pore types. At a similar porosity value, permeability is high in well-connected intercrystalline pores and low in isolated moldic pores. The effect of pore structure variations on acoustic velocity and impedance was then quantified using the pore structure indicator (γ). A more linear correlation of acoustic impedance (AI) and the product of porosity and γ was established. Results show that moldic pores have higher AI, whereas intercrystalline pores have lower AI at a given porosity. These relationships were used to interpret seismic AI inversion results and estimate the spatial variation of permeability using the post-stack seismic data. Moldic pores generated in platform margin ooid shoals and restricted platform after exposure and selectively dissolution as well as refluxion have lower permeability appearing as high AI; whereas dolostone with intercrystalline pores deposited in platform margin experienced reflux and burial dolomitization has relatively higher permeability, manifested in low AI values. The result shows great influence of varied carbonate pore structures on permeability heterogeneity and can be useful for further reservoir properties prediction

An Emergency Disposal Decision-making Method with Human--Machine Collaboration

Rapid developments in artificial intelligence technology have led to unmanned systems replacing human beings in many fields requiring high-precision predictions and decisions. In modern operational environments, all job plans are affected by emergency events such as equipment failures and resource shortages, making a quick resolution critical. The use of unmanned systems to assist decision-making can improve resolution efficiency, but their decision-making is not interpretable and may make the wrong decisions. Current unmanned systems require human supervision and control. Based on this, we propose a collaborative human--machine method for resolving unplanned events using two phases: task filtering and task scheduling. In the task filtering phase, we propose a human--machine collaborative decision-making algorithm for dynamic tasks. The GACRNN model is used to predict the state of the job nodes, locate the key nodes, and generate a machine-predicted resolution task list. A human decision-maker supervises the list in real time and modifies and confirms the machine-predicted list through the human--machine interface. In the task scheduling phase, we propose a scheduling algorithm that integrates human experience constraints. The steps to resolve an event are inserted into the normal job sequence to schedule the resolution. We propose several human--machine collaboration methods in each phase to generate steps to resolve an unplanned event while minimizing the impact on the original job plan.Comment: 15 pages, 16 figure

Intergenerational transmission of education in China: New evidence from the Chinese Cultural Revolution

This paper estimates the effect of parental education on children’s education by using instruments generated by the Chinese Cultural Revolution, and further explores the mechanisms of this causal relationship. Several important findings stand out from our empirical analyses. We find a larger intergenerational persistence in education for higher level in urban areas but for a lower level of education in rural areas. The main results from instrumental variable estimation show that the nurture effect is larger and more significant for fathers than for mothers. A deeper investigation of the mechanism behind this nurture effect informs us that a father’s education passes on to his children’s education partly through the income channel. Another notable finding is that even after controlling for fathers’ income, parental education still has a significantly positive effect on children’s education through the nurture effect. This indicates that beyond the income channel, there may exist other channels such as better home environment, which deserve to be explored in future research.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147763/1/rode12558_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147763/2/rode12558.pd

meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice

This paper introduces the R package meta4diag for implementing Bayesian bivariate meta-analyses of diagnostic test studies. Our package meta4diag is a purpose-built front end of the R package INLA. While INLA offers full Bayesian inference for the large set of latent Gaussian models using integrated nested Laplace approximations, meta4diag extracts the features needed for bivariate meta-analysis and presents them in an intuitive way. It allows the user a straightforward model specification and offers user-specific prior distributions. Further, the newly proposed penalized complexity prior framework is supported, which builds on prior intuitions about the behaviors of the variance and correlation parameters. Accurate posterior marginal distributions for sensitivity and specificity as well as all hyperparameters, and covariates are directly obtained without Markov chain Monte Carlo sampling. Further, univariate estimates of interest, such as odds ratios, as well as the summary receiver operating characteristic (SROC) curve and other common graphics are directly available for interpretation. An interactive graphical user interface provides the user with the full functionality of the package without requiring any R programming. The package is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=meta4diag/ and its usage will be illustrated using three real data examples