The k - meson nucleon interaction & the hadron – nucleus interaction

In the first part two inelastic two-body channels of the KN system are examined in energy-dependent partial wave analyses in which each non-resonant partial amplitude is expanded in an orthogonal series of polynomials over a normalised energy-dependent variable. The resonances known to be present in these channels are investigated as well as any possible new resonant states. It is found that the existing resonances are generally adequate to describe the available date for the two channels and that for K¯p → ^π in particular, the present fits form a statistically good solution with little new structure other than in the background phases. In the second part two particular examples of hadron-nucleuselastic scattering are studied using Glaubers multiple-scattering series. The first of these is the scattering of negative pi-mesonsfrom Helium-4, which is studied in detail at medium and high energy. A complete spin and isospin dependent set of πN amplitudes are used together with a number of forms for the nuclear densities. It is found that the use of more elaborate forms docs not provide any significantly better description than the more simple forms available and it is concluded that more realistic nuclear densities may be needed to describe the wide-angle data adequately. The second case studied is the scattering of medium-energy protonsfrom Carbon-12 and a modified form of Glauber series is used with the nucleus described as a state formed from three alpha-particles. Different forms of distribution for these are examined but are generally found to give little improvement over the simple harmonic oscillator densities. An improved a-particle density is proposed which may combine the best properties of the different forms used while retaining simplicity of calculation

An investigation of modelling and design for software service applications

Software services offer the opportunity to use a component-based approach for the design of applications. However, this needs a deeper understanding of how to develop service-based applications in a systematic manner, and of the set of properties that need to be included in the ‘design model’. We have used a realistic application to explore systematically how service-based designs can be created and described. We first identified the key properties of an SOA (service oriented architecture) and then undertook a single-case case study to explore its use in the development of a design for a large-scale application in energy engineering, modelling this with existing notations wherever possible. We evaluated the resulting design model using two walkthroughs with both domain and application experts. We were able to successfully develop a design model around the ten properties identified, and to describe it by adapting existing design notations. A component-based approach to designing such systems does appear to be feasible. However, it needs the assistance of a more integrated set of notations for describing the resulting design model

Evaluating a Cloud Service using Scheduling Security Model (SSM)

The development in technology makes cloud com-puting widely used in different sectors such as academic and business or for a private purposes. Also, it can provide a convenient services via the Internet allowing stakeholders get all the benefits that the cloud can facilitate. With all the benefits of cloud computing still there are some risks such as security. This brings into consideration the need to improve the Quality of Service (QoS). A Scheduling Security Model (SSM) for Cloud Computing has been developed to address these issues. This paper will discuss the evaluation of the SSM model on some examples with different scenarios to investigate the cost and the effect on the service requested by customers

The contribution that empirical studies performed in industry make to the findings of systematic reviews: A tertiary study

Context: Systematic reviews can provide useful knowledge for software engineering practice, by aggregating and synthesising empirical studies related to a specific topic. Objective: We sought to assess how far the findings of systematic reviews addressing practice-oriented topics have been derived from empirical studies that were performed in industry or that used industry data. Method: We drew upon and augmented the data obtained from a tertiary study that performed a systematic review of systematic reviews published in the period up to the end of 2015, seeking to identify those with findings that are relevant for teaching and practice. For the supplementary analysis reported here, we then examined the profiles of the primary studies as reported in each systematic review. Results: We identified 48 systematic reviews as candidates for further analysis. The many differences that arise between systematic reviews, together with the incompleteness of reporting for these, mean that our counts should be treated as indicative rather than definitive. However, even when allowing for problems of classification, the findings from the majority of these systematic reviews were predominantly derived from using primary studies conducted in industry. There was also an emphasis upon the use of case studies, and a number of the systematic reviews also made some use of weaker ‘experience’ or even ‘opinion’ papers. Conclusions: Primary studies from industry play an important role as inputs to systematic reviews. Using more rigorous industry-based primary studies can give greater authority to the findings of the systematic reviews, and should help with the creation of a corpus of sound empirical data to support evidence-informed decisions

Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm

Background: Electronic health record (EHR) systems generate large datasets that can significantly enrich the development of medical predictive models. Several attempts have been made to investigate the effect of glycated hemoglobin (HbA1c) elevation on the prediction of diabetes onset. However, there is still a need for validation of these models using EHR data collected from different populations. Objective: The aim of this study is to perform a replication study to validate, evaluate, and identify the strengths and weaknesses of replicating a predictive model that employed multiple logistic regression with EHR data to forecast the levels of HbA1c. The original study used data from a population in the United States and this differentiated replication used a population in Saudi Arabia. Methods: A total of 3 models were developed and compared with the model created in the original study. The models were trained and tested using a larger dataset from Saudi Arabia with 36,378 records. The 10-fold cross-validation approach was used for measuring the performance of the models. Results: Applying the method employed in the original study achieved an accuracy of 74% to 75% when using the dataset collected from Saudi Arabia, compared with 77% obtained from using the population from the United States. The results also show a different ranking of importance for the predictors between the original study and the replication. The order of importance for the predictors with our population, from the most to the least importance, is age, random blood sugar, estimated glomerular filtration rate, total cholesterol, non–high-density lipoprotein, and body mass index. Conclusions: This replication study shows that direct use of the models (calculators) created using multiple logistic regression to predict the level of HbA1c may not be appropriate for all populations. This study reveals that the weighting of the predictors needs to be calibrated to the population used. However, the study does confirm that replicating the original study using a different population can help with predicting the levels of HbA1c by using the predictors that are routinely collected and stored in hospital EHR systems

Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms with Electronic Health Records

Background: Predicting the risk of glycated hemoglobin (HbA1c) elevation can help identify patients with the potential for developing serious chronic health problems such as diabetes. Early preventive interventions based upon advanced predictive models using electronic health records (EHR) data for identifying such patients can ultimately help provide better health outcomes. Objective: Our study investigates the performance of predictive models to forecast HbA1c elevation levels by employing several machine learning models. We also investigate utilizing the patient's EHR longitudinal data in the performance of the predictive models. Explainable methods have been employed to interpret the decisions made by the blackbox models. Methods: This study employed Multiple Logistic Regression, Random Forest, Support Vector Machine and Logistic Regression models, as well as a deep learning model (Multi-layer perceptron) to classify patients with normal (<5.7%) and elevated (≥5.7%) levels of HbA1c. We also integrated current visit data with historical (longitudinal) data from previous visits. Explainable machine learning methods were used to interrogate the models and provide an understanding of the reasons behind the decisions made by the models. All models were trained and tested using a large dataset from Saudi Arabia with 18,844 unique patient records. Results: The machine learning models achieved promising results for predicting current HbA1c elevation risk. When employed with longitudinal data, the machine learning models outperformed the Multiple Logistic Regression model employed in the comparative study. The multi-layer perceptron model achieved an accuracy of 83.22% for the AUC-ROC when used with historical data. All models showed close level of agreement on the contribution of random blood sugar and age variables with and without longitudinal data. Conclusions: This study shows that machine learning models can provide promising results for the task of predicting current HbA1c levels (≥5.7% or less). Utilizing the patient's longitudinal data improved the performance and affected the relative importance for the predictors used. The models showed results that are consistent with comparable studies

Carbon footprint of inhalers in respiratory treatment : SABA CARBON International

Funding statement: The study was designed and funded by AstraZenecaPeer reviewedPostprin

Greenhouse Gas Emissions from Respiratory Treatments : Results from the SABA CARBON International Study

Acknowledgements Medical Writing, Editorial, and Other Assistance Medical writing and editorial support were provided by Tejaswini Subbannayya, PhD, of Cactus Life Sciences (part of Cactus Communications, Mumbai, India), in accordance with Good Publication Practice (GPP3) guidelines (http://www.ismpp.org/gpp3). This support was fully funded by AstraZeneca. Funding AstraZeneca funded the study; was involved in the study design, protocol development, study conduct and statistical analysis; and was given the opportunity to review the manuscript before submission. AstraZeneca also funded medical writing support and the development of the graphical abstract. AstraZeneca funded the journal’s Rapid Service and Open Access fees.Peer reviewedPublisher PD

Development and Characterisation of a Human Chronic Skin Wound Cell Line:Towards an Alternative for Animal Experimentation

Background: Chronic skin wounds are a growing financial burden for healthcare providers, causing discomfort/immobility to patients. Whilst animal chronic wound models have been developed to allow for mechanistic studies and to develop/test potential therapies, such systems are not good representations of the human chronic wound state. As an alternative, human chronic wound fibroblasts (CWFs) have permitted an insight into the dysfunctional cellular mechanisms that are associated with these wounds. However, such cells strains have a limited replicative lifespan and therefore a limited reproducibility/usefulness. Objectives: To develop/characterise immortalised cell lines of CWF and patient-matched normal fibroblasts (NFs). Methods and Results: Immortalisation with human telomerase resulted in both CWF and NF proliferating well beyond their replicative senescence end-point (respective cell strains senesced as normal). Gene expression analysis demonstrated that, whilst proliferation-associated genes were up-regulated in the cell lines (as would be expected), the immortalisation process did not significantly affect the disease-specific genotype. Immortalised CWF (as compared to NF) also retained a distinct impairment in their wound repopulation potential (in line with CWF cell strains). Conclusions: These novel CWF cell lines are a credible animal alternative and could be a valuable research tool for understanding both the aetiology of chronic skin wounds and for therapeutic pre-screening