Identification of Gene Expression-Based Prognostic Markers in the Hematopoietic Stem Cells of Patients With Myelodysplastic Syndromes

The diagnosis of patients with myelodysplastic syndromes (MDS) is largely dependent on morphologic examination of bone marrow aspirates. Several criteria that form the basis of the classifications and scoring systems most commonly used in clinical practice are affected by operator-dependent variation. To identify standardized molecular markers that would allow prediction of prognosis, we have used gene expression profiling (GEP) data on CD34+ cells from patients with MDS to determine the relationship between gene expression levels and prognosis.

NFA decomposition and multiprocessing architecture for parallel regular expression processing

This work presents a novel algorithm for decomposing NFA automata into one-state-active modules for parallel execution on Multiprocessor Systems on Chip (MP-SoC). Furthermore, performance related studies based on a 16-PE system for Snort, Bro and Linux-L7 regular expressions are presented. ©2009 IEEE.

A-level Mathematics Module Choice and Subsequent Performance in First Year of an Engineering Degree

The A-level Mathematics qualification is based on a compulsory set of pure maths modules and a selection of applied maths modules. The flexibility in choice of applied modules has led to concerns that many students would proceed to study engineering at university with little background in mechanics. A survey of aerospace and mechanical engineering students in our university revealed that a combination of mechanics and statistics (the basic module in both) was by far the most popular choice of optional modules in A-level Mathematics, meaning that only about one-quarter of the class had studied mechanics beyond the basic module within school mathematics. Investigation of student performance in two core, first-year engineering courses, which build on a mechanics foundation, indicated that any benefits for students who studied the extra mechanics at school were small. These results give concern about the depth of understanding in mechanics gained during A-level Mathematics.

Interpreting A-level Mathematics grades – as easy as A, B, C?

Many concerns have been expressed that students’ basic mathematical skills have deteriorated during the 1990s and there has been disquiet that current A-level grading does not distinguish adequately between the more able students. This study reports the author’s experiences of teaching maths to large classes of first-year engineering students and aims to enhance understanding of levels of mathematical competence in more recent years. Over the last four years, the classes have consisted of a very large proportion of highly qualified students – about 91% of them had at least grade B in A-level Mathematics. With a small group of students having followed a non-traditional route to university (no A-level maths) and another group having benefitted through taking A-level Further Mathematics at school, the classes have contained a very wide range of mathematical backgrounds. Despite the introductory maths course at university involving mainly repetition of A-level material, students’ marks were spread over a very wide range – for example, A-level Mathematics grade B students have scored across the range 16 – 97%. Analytical integration is the topic which produced the largest variation in performance across the class but, in contrast, the A-level students generally performed well in differentiation. Initial analysis suggests some stability in recent years in the mathematical proficiency of students with a particular A-level Mathematics grade. Allowing choice of applied maths modules as part of the A-level maths qualification increases the variety of students’ mathematical backgrounds and their selection from mechanics, statistics or decision maths is not clear from the final qualification.

Wave loads on the foundation of a bottom-hinged modular flap structure

Large loads result in expensive foundations which are a substantial proportion of the capital cost of flap-type Wave Energy Converters (WECs). Devices such as Oyster 800, currently deployed at the European Marine Energy Centre (EMEC), comprise a single flap for the full width of the machine. Splitting a flap-type device into smaller vertical flap modules, to make a ‘modular-flap’, might reduce the total foundation loads, whilst still providing acceptable performance in terms of energy conversion.
This paper investigates the foundation loads of an undamped modular-flap device, comparing them to those for a rigid flap of an equivalent width. Physical modelling in a wave tank is used, with loads recorded using a six degree of freedom (DoF) load cell. Both fatigue and extreme loading analysis was conducted. The rotations of the flaps were also recorded, using a motion-tracking system.

Finite domination and Novikov rings: Laurent polynomial rings in two variables

Let C be a bounded cochain complex of finitely generatedfree modules over the Laurent polynomial ring L = R[x, x−1, y, y−1].The complex C is called R-finitely dominated if it is homotopy equivalentover R to a bounded complex of finitely generated projective Rmodules.Our main result characterises R-finitely dominated complexesin terms of Novikov cohomology: C is R-finitely dominated if andonly if eight complexes derived from C are acyclic; these complexes areC ⊗L R[[x, y]][(xy)−1] and C ⊗L R[x, x−1][[y]][y−1], and their variants obtainedby swapping x and y, and replacing either indeterminate by its inverse.

Low-Power Process-Variation Tolerant Arithmetic Units Using Input-Based Elastic Clocking

In this paper we propose a design methodology for low-power high-performance, process-variation tolerant architecture for arithmetic units. The novelty of our approach lies in the fact that possible delay failures due to process variations and/or voltage scaling are predicted in advance and addressed by employing an elastic clocking technique. The prediction mechanism exploits the dependence of delay of arithmetic units upon input data patterns and identifies specific inputs that activate the critical path. Under iso-yield conditions, the proposed design operates at a lower scaled down Vdd without any performance degradation, while it ensures a superlative yield under a design style employing nominal supply and transistor threshold voltage. Simulation results show power savings of upto 29%, energy per computation savings of upto 25.5% and yield enhancement of upto 11.1% compared to the conventional adders and multipliers implemented in the 70nm BPTM technology. We incorporated the proposed modules in the execution unit of a five stage DLX pipeline to measure performance using SPEC2000 benchmarks [9]. Maximum area and throughput penalty obtained were 10% and 3% respectively.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

High-Speed Fully Homomorphic Encryption Over the Integers

A fully homomorphic encryption (FHE) scheme is envisioned as a key cryptographic tool in building a secure and reliable cloud computing environment, as it allows arbitrary evaluation of a ciphertext without revealing the plaintext. However, existing FHE implementations remain impractical due to very high time and resource costs. To the authors’ knowledge, this paper presents the first hardware implementation of a full encryption primitive for FHE over the integers using FPGA technology. A large-integer multiplier architecture utilising Integer-FFT multiplication is proposed, and a large-integer Barrett modular reduction module is designed incorporating the proposed multiplier. The encryption primitive used in the integer-based FHE scheme is designed employing the proposed multiplier and modular reduction modules. The designs are verified using the Xilinx Virtex-7 FPGA platform. Experimental results show that a speed improvement factor of up to 44 is achievable for the hardware implementation of the FHE encryption scheme when compared to its corresponding software implementation. Moreover, performance analysis shows further speed improvements of the integer-based FHE encryption primitives may still be possible, for example through further optimisations or by targeting an ASIC platform.

Synthesizable high performance adaptive equaliser and viterbi decoder for the class-IV PRML channel

The design and VLSI implementation of two key components of the class-IV partial response maximum likelihood channel (PR-IV) the adaptive filter and the Viterbi decoder are described. These blocks are implemented using parameterised VHDL modules, from a library of common digital signal processing (DSP) and arithmetic functions. Design studies, based on 0.6 micron 3.3V standard cell processes, indicate that worst case sampling rates of 49 mega-samples per second are achievable for this system, with proportionally high sampling rates for full custom designs and smaller dimension processes. Significant increases in the sampling rate, from 49 MHz to approximately 180 MHz, can be achieved by operating four filter modules in parallel, and this implementation has 50% lower power consumption than a pipelined filter operating at the same speed.

An evaluation of a critical care course for undergraduate nursing students

AIM:
The aim of this paper was to evaluate a 2-day critical care course (CCC) delivered to a cohort of adult branch nursing students.

BACKGROUND:
In today's health care system there is an increase in the number of critically ill patients being cared for in a ward environment. As a result, nurses require the knowledge and skills to effectively manage this patient group. Skills such as prompt recognition of the sick patient, effective communication and performing basic management care skills are necessary.

METHODS:
The CCC was provided to final year adult branch nursing students (n = 182) within a university in the UK. On completion of the course, participants were invited to undertake a Likert scale questionnaire. The questionnaire also contained a free response section to elicit qualitative information. Quantitative data were analysed using SPSS version 17.0 and descriptive statistics produced. Qualitative responses were analysed thematically.

RESULTS:
There was a 73.7% (n = 135) response rate. Overall, there was a positive evaluation of the course. Students (89.6%; n = 121) reported a perceived increase in confidence when caring for critically ill patients following the course and 88.2% (n = 119) felt that their knowledge and skills had improved at the end of the 2-day course.

CONCLUSION:
This study supports the implementation of critical care training for undergraduate nursing students. There are implications for the development of specific modules, aiming to improve undergraduate nursing students' recognition, assessment and management of the critically ill patient.

Development of a continuing professional development website to raise awareness of preconception counselling and pre-pregnancy care for Women with Diabetes

Aims: Pre-pregnancy care reduces the risk of adverse pregnancy outcomes in women with diabetes, yet the majority of women receive suboptimal care due to poor preconception counselling rates and a lack of awareness about the importance of specialised pre-pregnancy care. The primary aim was to develop a continuing professional development (CPD) resource for healthcare professionals (HCPs) who work with women with diabetes to facilitate preconception counselling with this group.

Methods: The website was developed under the direction of a multidisciplinary team, adhering to NICE guidelines. The tone, key messages and format are informed by the “Women with Diabetes” preconception counselling website, www.womenwithdiabetes.net, an existing resource which is effective in helping women to be better prepared for pregnancy.Results: This e-learning resource will give HCPs the necessary knowledge and tools to prepare women with diabetes to plan for pregnancy. The website features women with diabetes sharing their views and experiences, alongside an evidence-based commentary and key messages from research papers and clinical guidelines. It comprises two modules: “Planning for Pregnancy”, focusing on contraception, risks and planning; and “Diabetes and Pregnancy”, focusing on support during pregnancy with an overview of each trimester of pregnancy.

Conclusion: This website will be a useful CPD resource for all HCPs working with women with diabetes, providing a certificate on completion. This resource will empower HCPs to engage in preconception counselling with women with diabetes by providing the HCP with a greater understanding of the specific needs of women with diabetes both preconception and during pregnancy.

Cultivating creative learners in higher education: Engaging students in anatomy through art

Introduction
Nursing and midwifery students often struggle to engage with bioscience modules because they lack confidence in their ability to study science (Fell et al., 2012). Consequently many have difficulty applying anatomical and physiological information, essential to providing safe and effective patient care (Rogers, 2014; Rogers and Sterling, 2012); therefore a need exists for nurse educators to explore different methods of delivery of these important topics to enhance current curricula (Johnston, 2010). Inspired by the reported success of creative methods to enhance the teaching and learning of anatomy in medical education (Noel, 2013; Finn and McLachlan, 2010), this pilot study engaged nursing students in anatomy through the art of felt. The project was underpinned by the principles of good practice in undergraduate education, staff-student engagement, cooperation among students, active learning, prompt feedback, time on task, high expectations and respect for diverse learning styles (Chickering and Gamson, 1987).

Method
Undergraduate student nurses from Queen’s University, Belfast, enrolled in the year one ‘Health and Wellbeing’ model were invited to participate in the project. Over a six week period the student volunteers worked in partnership with teaching staff to construct individual, unique, three dimensional felt models of the upper body. Students researched the agreed topic for each week in terms of anatomical structure, location, tissue composition and vascular access. Creativity was encouraged in relation to the colour and texture of materials used. The evaluation of the project was based on the four level model detailed by Kirkpatrick and Kirkpatrick (2006) and included both quantitative and qualitative analysis:• pre and post knowledge scores• self-rated confidence• student reflections on the application of learning to practice.

Results
At the end of the project students had created felt pieces reflective of their learning throughout the project and ‘memorable’ three dimensional mental maps of the human anatomy. Evaluation revealed not only acquisition of anatomical knowledge, but the wider benefits of actively engaging in creative learning with other students and faculty teaching staff.

The project has enabled nurse educators to assess the impact of innovative methods for delivery of these important topics.

Mind mapping: A tool to facilitate learning; why not give it a try?

Schools of nursing continuously strive to facilitate learning through student engagement and teaching strategies that encourage active learning. This paper reports on the successful use of mind mapping, an underutilised and underdeveloped strategy, to enhance teaching and learning in undergraduate nurse education (Spencer et al., 2013). Mind mapping or concept mapping has been defined in the literature as a visual representation of one’s thoughts and ideas (Abel and Freeze, 2006). It is characterised by colour, images and text in a graphical, nonlinear style. Mind maps promote the linking of concepts and capitalise on the brain’s natural aptitude for visual recognition to enhance learning and memory recall (Buzan, 2006). Traditional teaching strategies depend on linear processes, which in comparison lack engagement, associations and creativity (Spencer et al., 2013). Mind mapping was introduced to nursing students undertaking modules in ‘Dimensions of Care’ and ‘Care Delivery’ on year two of the nursing degree programme in Queen’s University Belfast. The aim of introducing mind mapping was to help students make the critical link between the pathophysiology of conditions studied and the provision of informed, safe and effective patient care, which had challenged previous student cohorts. Initially maps were instructor-made as described by Boley (2008), as a template for note taking during class and as a study aid. However, students rapidly embraced the strategy and started creating their own mind maps. Meaningful learning occurs when students engage with concepts and organise them independently in a way significant to them (Buzan, 2006). Students reported high levels of satisfaction to this teaching approach. This paper will present examples of the mind maps produced and explore how mind mapping can be further utilised within the undergraduate nursing curriculum.

Secure Virtualised Environment

Cloud computing is a technological advancementthat provide resources through internet on pay-as-you-go basis.Cloud computing uses virtualisation technology to enhance theefficiency and effectiveness of its advantages. Virtualisation isthe key to consolidate the computing resources to run multiple instances on each hardware, increasing the utilization rate of every resource, thus reduces the number of resources needed to buy, rack, power, cool, and manage. Cloud computing has very appealing features, however, lots of enterprises and users are still reluctant to move into cloud due to serious security concerns related to virtualisation layer. Thus, it is foremost important to secure the virtual environment.In this paper, we present an elastic framework to secure virtualised environment for trusted cloud computing called Server Virtualisation Security System (SVSS). SVSS provide security solutions located on hyper visor for Virtual Machines by deploying malicious activity detection techniques, network traffic analysis techniques, and system resource utilization analysis techniques.SVSS consists of four modules: Anti-Virus Control Module,Traffic Behavior Monitoring Module, Malicious Activity Detection Module and Virtualisation Security Management Module.A SVSS prototype has been deployed to validate its feasibility,efficiency and accuracy on Xen virtualised environment.