Pseudogene-gene functional networks are prognostic of patient survival in breast cancer

Background: Given the vast range of molecular mechanisms giving rise to breast cancer, it is unlikely universal cures exist. However, by providing a more precise prognosis for breast cancer patients through integrative models, treatments can become more individualized, resulting in more successful outcomes. Specifically, we combine gene expression, pseudogene expression, miRNA expression, clinical factors, and pseudogene-gene functional networks to generate these models for breast cancer prognostics. Establishing a LASSO-generated molecular gene signature revealed that the increased expression of genes STXBP5, GALP and LOC387646 indicate a poor prognosis for a breast cancer patient. We also found that increased CTSLP8 and RPS10P20 and decreased HLA-K pseudogene expression indicate poor prognosis for a patient. Perhaps most importantly we identified a pseudogene-gene interaction, GPS2-GPS2P1 (improved prognosis) that is prognostic where neither the gene nor pseudogene alone is prognostic of survival. Besides, miR-3923 was predicted to target GPS2 using miRanda, PicTar, and TargetScan, which imply modules of gene-pseudogene-miRNAs that are potentially functionally related to patient survival. Results: In our LASSO-based model, we take into account features including pseudogenes, genes and candidate pseudogene-gene interactions. Key biomarkers were identified from the features. The identification of key biomarkers in combination with significant clinical factors (such as stage and radiation therapy status) should be considered as well, enabling a specific prognostic prediction and future treatment plan for an individual patient. Here we used our PseudoFuN web application to identify the candidate pseudogene-gene interactions as candidate features in our integrative models. We further identified potential miRNAs targeting those features in our models using PseudoFuN as well. From this study, we present an interpretable survival model based on LASSO and decision trees, we also provide a novel feature set which includes pseudogene-gene interaction terms that have been ignored by previous prognostic models. We find that some interaction terms for pseudogenes and genes are significantly prognostic of survival. These interactions are cross-over interactions, where the impact of the gene expression on survival changes with pseudogene expression and vice versa. These may imply more complicated regulation mechanisms than previously understood. Conclusions: We recommend these novel feature sets be considered when training other types of prognostic models as well, which may provide more comprehensive insights into personalized treatment decisions

A novel mechanism of RNase L inhibition: Theiler\u27s virus L* protein prevents 2-5A from binding to RNase L

<div><p>The OAS/RNase L pathway is one of the best-characterized effector pathways of the IFN antiviral response. It inhibits the replication of many viruses and ultimately promotes apoptosis of infected cells, contributing to the control of virus spread. However, viruses have evolved a range of escape strategies that act against different steps in the pathway. Here we unraveled a novel escape strategy involving Theiler’s murine encephalomyelitis virus (TMEV) L* protein. Previously we found that L* was the first viral protein binding directly RNase L. Our current data show that L* binds the ankyrin repeats R1 and R2 of RNase L and inhibits 2’-5’ oligoadenylates (2-5A) binding to RNase L. Thereby, L* prevents dimerization and oligomerization of RNase L in response to 2-5A. Using chimeric mouse hepatitis virus (MHV) expressing TMEV L*, we showed that L* efficiently inhibits RNase L <i>in vivo</i>. Interestingly, those data show that L* can functionally substitute for the MHV-encoded phosphodiesterase ns2, which acts upstream of L* in the OAS/RNase L pathway, by degrading 2-5A.</p></div

Predictable duty cycle modulation through coupled pairing of syringes with microfluidic oscillators

The ability to elicit distinct duty cycles from the same self-regulating microfluidic oscillator device would greatly enhance the versatility of this micro-machine as a tool, capable of recapitulating in vitro the diverse oscillatory processes that occur within natural systems. We report a novel approach to realize this using the coordinated modulation of input volumetric flow rate ratio and fluidic capacitance ratio. The demonstration uses a straightforward experimental system where fluid inflow to the oscillator is provided by two syringes (of symmetric or asymmetric cross-sectional area) mounted upon a single syringe pump applying pressure across both syringes at a constant linear velocity. This produces distinct volumetric outflow rates from each syringe that are proportional to the ratio between their cross-sectional areas. The difference in syringe cross-sectional area also leads to differences in fluidic capacitance; this underappreciated capacitive difference allows us to present a simplified expression to determine the microfluidic oscillators duty cycle as a function of cross-sectional area. Examination of multiple total volumetric inflows under asymmetric inflow rates yielded predictable and robust duty cycles ranging from 50% to 90%. A method for estimating the outflow duration for each inflow under applied flow rate ratios is provided to better facilitate the utilization of this system in experimental protocols requiring specific stimulation and rest intervalsopen1

Against Reduction

Provocative, hopeful essays imagine a future that is not reduced to algorithms. What is human flourishing in an age of machine intelligence, when many claim that the world's most complex problems can be reduced to narrow technical questions? Does more computing make us more intelligent, or simply more computationally powerful? We need not always resist reduction; our ability to simplify helps us interpret complicated situations. The trick is to know when and how to do so. Against Reduction offers a collection of provocative and illuminating essays that consider different ways of recognizing and addressing the reduction in our approach to artificial intelligence, and ultimately to ourselves. Inspired by a widely read manifesto by Joi Ito that called for embracing the diversity and irreducibility of the world, these essays offer persuasive and compelling variations on resisting reduction. Among other things, the writers draw on Indigenous epistemology to argue for an extended “circle of relationships” that includes the nonhuman and robotic; cast “Snow White” as a tale of AI featuring a smart mirror; point out the cisnormativity of security protocol algorithms; map the interconnecting networks of so-called noncommunicable disease; and consider the limits of moral mathematics. Taken together, they show that we should push back against some of the reduction around us and do whatever is in our power to work toward broader solutions

Structuring Stress for Active Materials Control

Active materials are capable of converting free energy into mechanical work to produce autonomous motion, and exhibit striking collective dynamics that biology relies on for essential functions. Controlling those dynamics and transport in synthetic systems has been particularly challenging. Here, we introduce the concept of spatially structured activity as a means to control and manipulate transport in active nematic liquid crystals consisting of actin filaments and light-sensitive myosin motors. Simulations and experiments are used to demonstrate that topological defects can be generated at will, and then constrained to move along specified trajectories, by inducing local stresses in an otherwise passive material. These results provide a foundation for design of autonomous and reconfigurable microfluidic systems where transport is controlled by modulating activity with light

MEAD - A Platform for Multidocument Multilingual Text Summarization

This paper describes the functionality of MEAD, a comprehensive, public domain, open source, multidocument multilingual summarization environment that has been thus far downloaded by more than 500 organizations. MEAD has been used in a variety of summarization applications ranging from summarization for mobile devices to Web page summarization within a search engine and to novelty detection