Identification of proteins binding coding and non-coding human RNAs using protein microarrays

Background: The regulation and function of mammalian RNAs has been increasingly appreciated to operate via RNA-protein interactions. With the recent discovery of thousands of novel human RNA molecules by high-throughput RNA sequencing, efficient methods to uncover RNA-protein interactions are urgently required. Existing methods to study proteins associated with a given RNA are laborious and require substantial amounts of cell-derived starting material. To overcome these limitations, we have developed a rapid and large-scale approach to characterize binding of in vitro transcribed labeled RNA to ~9,400 human recombinant proteins spotted on protein microarrays. Results: We have optimized methodology to probe human protein microarrays with full-length RNA molecules and have identified 137 RNA-protein interactions specific for 10 coding and non-coding RNAs. Those proteins showed strong enrichment for common human RNA binding domains such as RRM, RBD, as well as K homology and CCCH type zinc finger motifs. Previously unknown RNA-protein interactions were discovered using this technique, and these interactions were biochemically verified between TP53 mRNA and Staufen1 protein as well as between HRAS mRNA and CNBP protein. Functional characterization of the interaction between Staufen 1 protein and TP53 mRNA revealed a novel role for Staufen 1 in preserving TP53 RNA stability. Conclusions: Our approach demonstrates a scalable methodology, allowing rapid and efficient identification of novel human RNA-protein interactions using RNA hybridization to human protein microarrays. Biochemical validation of newly identified interactions between TP53-Stau1 and HRAS-CNBP using reciprocal pull-down experiments, both in vitro and in vivo, demonstrates the utility of this approach to study uncharacterized RNA-protein interactions.Stem Cell and Regenerative Biolog

Engineering Quantum Error Correction Codes Using Evolutionary Algorithms

Quantum error correction and the use of quantum error correction codes is likely to be essential for the realisation of practical quantum computing. Because the error models of quantum devices vary widely, quantum codes which are tailored for a particular error model may have much better performance. In this work, we present a novel evolutionary algorithm which searches for an optimal stabiliser code for a given error model, number of physical qubits and number of encoded qubits. We demonstrate an efficient representation of stabiliser codes as binary strings - this allows for random generation of valid stabiliser codes, as well as mutation and crossing of codes. Our algorithm finds stabiliser codes whose distance closely matches the best-known-distance codes of [1] for n ≤ 20 physical qubits. We perform a search for optimal distance CSS codes, and compare their distance to the best-known-codes. Finally, we show that the algorithm can be used to optimise stabiliser codes for biased error models, demonstrating a significant improvement in the undetectable error rate for [[12,1]]2 codes versus the best-known-distance code with the same parameters. As part of this work, we also introduce an evolutionary algorithm QDistEvol for finding the distance of quantum error correction codes

Fertilization of Established Trees: A Report of Field Studies

Prior to this study the two senior authors, as plant pathologists, had recommended fertilization as a preventive or corrective control measure for several tree diseases. It was recognized, however, that the procedures for fertilizing established trees had not been thoroughly subjected to scientific evaluation; more experimental data were needed. While cooperating with the junior author at the Morton Arboretum, Lisle, Illinois, on plant disease studies in 1962 the senior authors learned of an experimental area in the Arboretum which contained uniform, established trees in 100-tree blocks with tree spacing intervals optimum for fertilizer trials. In this area a cooperative study was initiated with the Morton Arboretum. Following the early successful attempts at measuring growth response to fertilizer applications in 1963 and 1964 at the Morton Arboretum, the senior authors expanded the study with trials at four additional sites in Illinois through 1968

Identification of proteins binding coding and non-coding human RNAs using protein microarrays

Abstract Background The regulation and function of mammalian RNAs has been increasingly appreciated to operate via RNA-protein interactions. With the recent discovery of thousands of novel human RNA molecules by high-throughput RNA sequencing, efficient methods to uncover RNA-protein interactions are urgently required. Existing methods to study proteins associated with a given RNA are laborious and require substantial amounts of cell-derived starting material. To overcome these limitations, we have developed a rapid and large-scale approach to characterize binding of in vitro transcribed labeled RNA to ~9,400 human recombinant proteins spotted on protein microarrays. Results We have optimized methodology to probe human protein microarrays with full-length RNA molecules and have identified 137 RNA-protein interactions specific for 10 coding and non-coding RNAs. Those proteins showed strong enrichment for common human RNA binding domains such as RRM, RBD, as well as K homology and CCCH type zinc finger motifs. Previously unknown RNA-protein interactions were discovered using this technique, and these interactions were biochemically verified between TP53 mRNA and Staufen1 protein as well as between HRAS mRNA and CNBP protein. Functional characterization of the interaction between Staufen 1 protein and TP53 mRNA revealed a novel role for Staufen 1 in preserving TP53 RNA stability. Conclusions Our approach demonstrates a scalable methodology, allowing rapid and efficient identification of novel human RNA-protein interactions using RNA hybridization to human protein microarrays. Biochemical validation of newly identified interactions between TP53-Stau1 and HRAS-CNBP using reciprocal pull-down experiments, both in vitro and in vivo, demonstrates the utility of this approach to study uncharacterized RNA-protein interactions. </jats:sec

Regenerative economics at the service of islands: Assessing the socio-economic metabolism of Samothraki in Greece

For many islands, the answer to the question “why a locally, self-sustaining, and regenerative economy is needed?” is clear. The struggle often lies in the “how”. Here, we argue that tools from regenerative economics, which follow an island economy-as-an-organism analogy, offer valuable and complementary insights to socio-metabolic research. Indicators from flow-based and information-based ecological network analysis can quantify proper- ties of an island’s socio-economic metabolism (SEM) which are related to cycling, resilience, and degree of mutualism, among others. To illustrate the applicability of these methods, we select Samothraki in Greece as a case study. Results show that over the years the island became very efficient in streamlining imported resources, experiencing physical growth as indicated by a substantial increase in its total material throughput. This growth was attributed to a high degree of order (i.e., network efficiency) endowed by the constraining (ordered) part of the linear structure of the island’s SEM. The disordered part of its SEM which is related to resilience, played a much smaller role which became progressively more important over the years, albeit to a limited degree. While the island exhibits an increasing trend in its robustness, its value over the years studied was well below what is typically observed for healthy natural ecosystems, and its current SEM has a very low ability to generate internal flow activity and cycling of resources per unit input. This limited robustness is due to the island’s dependency on imports but also due to its linear SEM which had a very small number of feedback loops in its network. A scenario analysis showed that a reticulated network structure would theoretically endow the island with increased resilience, and hence robustness, by allowing for more internal resource flow activity to be circulated as regen- erative re-investment. This article highlights that methods from regenerative economics can be used as diagnostic tools to assess and monitor the impact of strategies related to circular economy interventions on network properties, and to illuminate their effect on the regenerative potential of islands

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)

An agenda for integrated system-wide interdisciplinary agri-food research

© 2017 The Author(s)This paper outlines the development of an integrated interdisciplinary approach to agri-food research, designed to address the ‘grand challenge’ of global food security. Rather than meeting this challenge by working in separate domains or via single-disciplinary perspectives, we chart the development of a system-wide approach to the food supply chain. In this approach, social and environmental questions are simultaneously addressed. Firstly, we provide a holistic model of the agri-food system, which depicts the processes involved, the principal inputs and outputs, the actors and the external influences, emphasising the system’s interactions, feedbacks and complexities. Secondly, we show how this model necessitates a research programme that includes the study of land-use, crop production and protection, food processing, storage and distribution, retailing and consumption, nutrition and public health. Acknowledging the methodological and epistemological challenges involved in developing this approach, we propose two specific ways forward. Firstly, we propose a method for analysing and modelling agri-food systems in their totality, which enables the complexity to be reduced to essential components of the whole system to allow tractable quantitative analysis using LCA and related methods. This initial analysis allows for more detailed quantification of total system resource efficiency, environmental impact and waste. Secondly, we propose a method to analyse the ethical, legal and political tensions that characterise such systems via the use of deliberative fora. We conclude by proposing an agenda for agri-food research which combines these two approaches into a rational programme for identifying, testing and implementing the new agri-technologies and agri-food policies, advocating the critical application of nexus thinking to meet the global food security challenge

Reconstruction of primary vertices at the ATLAS experiment in Run 1 proton–proton collisions at the LHC

This paper presents the method and performance of primary vertex reconstruction in proton–proton collision data recorded by the ATLAS experiment during Run 1 of the LHC. The studies presented focus on data taken during 2012 at a centre-of-mass energy of √s=8 TeV. The performance has been measured as a function of the number of interactions per bunch crossing over a wide range, from one to seventy. The measurement of the position and size of the luminous region and its use as a constraint to improve the primary vertex resolution are discussed. A longitudinal vertex position resolution of about 30μm is achieved for events with high multiplicity of reconstructed tracks. The transverse position resolution is better than 20μm and is dominated by the precision on the size of the luminous region. An analytical model is proposed to describe the primary vertex reconstruction efficiency as a function of the number of interactions per bunch crossing and of the longitudinal size of the luminous region. Agreement between the data and the predictions of this model is better than 3% up to seventy interactions per bunch crossing

Deep Learning-Based Psoriasis Assessment: Harnessing Clinical Trial Imaging for Accurate Psoriasis Area Severity Index Prediction

Introduction: Image-based machine learning holds great promise for facilitating clinical care; however, the datasets often used for model training differ from the interventional clinical trial-based findings frequently used to inform treatment guidelines. Here, we draw on longitudinal imaging of psoriasis patients undergoing treatment in the Ultima 2 clinical trial (NCT02684357), including 2,700 body images with psoriasis area severity index (PASI) annotations by uniformly trained dermatologists. Methods: An image-processing workflow integrating clinical photos of multiple body regions into one model pipeline was developed, which we refer to as the “One-Step PASI” framework due to its simultaneous body detection, lesion detection, and lesion severity classification. Group-stratified cross-validation was performed with 145 deep convolutional neural network models combined in an ensemble learning architecture. Results: The highest-performing model demonstrated a mean absolute error of 3.3, Lin’s concordance correlation coefficient of 0.86, and Pearson correlation coefficient of 0.90 across a wide range of PASI scores comprising disease classifications of clear skin, mild, and moderate-to-severe disease. Within-person, time-series analysis of model performance demonstrated that PASI predictions closely tracked the trajectory of physician scores from severe to clear skin without systematically over- or underestimating PASI scores or percent changes from baseline. Conclusion: This study demonstrates the potential of image processing and deep learning to translate otherwise inaccessible clinical trial data into accurate, extensible machine learning models to assess therapeutic efficacy

The Distribution of Toxoplasma gondii Cysts in the Brain of a Mouse with Latent Toxoplasmosis: Implications for the Behavioral Manipulation Hypothesis

reportedly manipulates rodent behavior to enhance the likelihood of transmission to its definitive cat host. The proximate mechanisms underlying this adaptive manipulation remain largely unclear, though a growing body of evidence suggests that the parasite-entrained dysregulation of dopamine metabolism plays a central role. Paradoxically, the distribution of the parasite in the brain has received only scant attention. at six months of age and examined 18 weeks later. The cysts were distributed throughout the brain and selective tropism of the parasite toward a particular functional system was not observed. Importantly, the cysts were not preferentially associated with the dopaminergic system and absent from the hypothalamic defensive system. The striking interindividual differences in the total parasite load and cyst distribution indicate a probabilistic nature of brain infestation. Still, some brain regions were consistently more infected than others. These included the olfactory bulb, the entorhinal, somatosensory, motor and orbital, frontal association and visual cortices, and, importantly, the hippocampus and the amygdala. By contrast, a consistently low incidence of tissue cysts was recorded in the cerebellum, the pontine nuclei, the caudate putamen and virtually all compact masses of myelinated axons. Numerous perivascular and leptomeningeal infiltrations of inflammatory cells were observed, but they were not associated with intracellular cysts. distribution stems from uneven brain colonization during acute infection and explains numerous behavioral abnormalities observed in the chronically infected rodents. Thus, the parasite can effectively change behavioral phenotype of infected hosts despite the absence of well targeted tropism