A rule-based kinetic model of RNA polymerase II C-terminal domain phosphorylation

The complexity ofmany RNA processing pathways is such that a conventional systemsmodelling approach is inadequate to represent all themolecular species involved. We demonstrate that rule-based modelling permits a detailed model of a complex RNA signalling pathway to be defined. Phosphorylation of the RNApolymerase II (RNAPII)C-terminal domain (CTD; a flexible tail-like extension of the largest subunit) couples pre-messenger RNA capping, splicing and 30 end maturation to transcriptional elongation and termination, and plays a central role in integrating these processes. The phosphorylation states of the serine residues of many heptapeptide repeats of the CTD alter along the coding region of genes as a function of distance from the promoter. From a mechanistic perspective, both the changes in phosphorylation and the location atwhich they take place on the genes are a function of the time spent byRNAPII in elongation as this interval provides the opportunity for the kinases and phosphatases to interactwith theCTD.On this basis,we synthesize the available data to create a kinetic model of the action of the known kinases and phosphatases to resolve the phosphorylation pathways and their kinetics.</p

Eosinophile Leucocyte: its occurrence and significance with special reference to asthma

I. There are four main groups of conditions in which eosinophilia is of frequent occurrence:-(a) Convalescence from infectious fevers, and in the course of scarlet fever.(h) Skin diseases.(c) Asthma.(d) Parasitic infestations.II. The eosinophilia is of low grade in conva­lescence and in most skin diseases,except dermatitis herpetiformis and pemphigus.III. A low grade eosinophilia is present in 50 per cent of asthmatic patients.IV. High grade eosinophilia is specially associated with infestations of Bilharzia, Pilaria, Trichina and Ankylostoma; a low grade eosinophilia may occur with any parasitic infestation

The Large Lecture Course Redesign Project: Pedagogical Goals And Assessment Results

An analysis and assessment of the Course Redesign Project, which used technology to improve student learning and course satisfaction in large lecture courses at the University of Massachusetts Amherst. Six disciplinary-diverse departments participated in the project. Technology was selected for the purpose of introducing active learning into lecture halls and providing frequent feedback to students on their individual learning progress. The assessment methodology compares traditionally taught sections with redesigned sections, holding constant (where possible) such potential confounding factors as student academic ability, professor, textbook, day and time of class and the number, type and difficulty of exams and other graded assignments. The assessment of the project produced strong and significant statistical results that indicate that students across the broad spectrum of redesigned courses learned more and achieved higher grades than students in traditional sections. This occurred despite the fact that students in traditional sections had either the same or higher high school-grade point averages and SAT scores compared to students in the redesigned sections. The project included 12 traditional course sections with a total enrollment of 2,456 and 13 redesigned courses sections with a total enrollment of 3,101. The project was supported by a grant from the Davis Educational Foundation

Characteristics of successful interventions to reduce turnover and increase retention of early career nurses: a systematic review

Background nurse shortages have been identified as central to workforce issues in healthcare systems globally and although interventions to increase the nursing workforce have been implemented, nurses leaving their roles, particularly in the first year after qualification, present a significant barrier to building the nurse workforce. Objective to evaluate the characteristics of successful interventions to promote retention and reduce turnover of early career nurses. Design this is a systematic review Data sources Online databases including Academic Search Complete, Medline, Health Policy reference Centre, EMBASE, Psychinfo, CINAHL and the Cochran Library were searched to identify relevant publications in English published between 2001 and April 2018. Studies included evaluated an intervention to increase retention or reduce turnover and used turnover or retention figures as a measure. Review methods The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Studies were quality-assessed using the Joanna Briggs Institute Critical Appraisal tools for Quasi Experimental and Randomised Controlled Trials. Retention/turnover data were used to guide the comparison between studies and appropriate measures of central tendency and dispersion were calculated and presented, based on the normality of the data. Results A total of 11, 656 papers were identified, of which 53 were eligible studies. A wide variety of interventions and components within those interventions were identified to improve nurse retention. Promising interventions appear to be either internship/residency programmes or orientation/transition to practice programmes, lasting between 27-52 weeks, with a teaching and preceptor and mentor component. Conclusions Methodological issues impacted on the extent to which conclusions could be drawn, even though a large number of studies were identified. Future research should focus on standardising the reporting of interventions and outcome measures used to evaluate these interventions and carrying out further research with rigorous methodology. Clinical practice areas are recommended to assess their current interventions against the identified criteria to guide development of their effectiveness. Evaluations of cost-effectiveness are considered an important next step to maximise return on investment

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

High Resolution Millimeter-Wave Mapping of Linearly Polarized Dust Emission: Magnetic Field Structure in Orion

We present 1.3 and 3.3 mm polarization maps of Orion-KL obtained with the BIMA array at approximately 4 arcsec resolution. Thermal emission from magnetically aligned dust grains produces the polarization. Along the Orion ``ridge'' the polarization position angle varies smoothly from about 10 degrees to 40 degrees, in agreement with previous lower resolution maps. In a small region south of the Orion ``hot core,'' however, the position angle changes by 90 degrees. This abrupt change in polarization direction is not necessarily the signpost of a twisted magnetic field. Rather, in this localized region processes other than the usual Davis-Greenstein mechanism might align the dust grains with their long axes parallel with the field, orthogonal to their normal orientation.Comment: AAS preprint:14 pages, 2 figures (3mm.eps and 1mm.eps); requires aaspp4.sty To be published in Astrophysical Journal Letter

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods