Development and application of a multiple linear regression model to consider the impact of weekly waste container capacity on the yield from kerbside recycling programmes

The paper describes the development and application of a multiple linear regression model to identify how the key elements of waste and recycling infrastructure, namely container capacity and frequency of collection affect the yield from municipal kerbside recycling programmes. The overall aim of the research was to gain an understanding of the factors affecting the yield from municipal kerbside recycling programmes in Scotland. The study isolates the principal kerbside collection service offered by 32 councils across Scotland, eliminating those recycling programmes associated with flatted properties or multi occupancies. The results of a regression analysis model has identified three principal factors which explain 80% of the variability in the average yield of the principal dry recyclate services: weekly residual waste capacity, number of materials collected and the weekly recycling capacity. The use of the model has been evaluated and recommendations made on ongoing methodological development and the use of the results in informing the design of kerbside recycling programmes. The authors hope that the research can provide insights for the ongoing development of methods to optimise the design and operation of kerbside recycling programmes.

Analysis of IL6 Signal Transduction Model using Reduced Rank Regression

This work presents the application of reduced rank regression to the field of systems biology. A computational approach is used to investigate the mechanisms of the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in hepatic cells stimulated by interleukin-6. The results obtained identify the contribution of individual reactions to the dynamics of the model. These findings are compared to previously available results from sensitivity analysis of the model which focused on the parameters involved and their effect. This application of reduced rank regression allows for an understanding of the individual reaction terms involved in the modelled signal transduction pathways and has the benefit of being computationally inexpensive. The obtained results complement existing findings and also confirm the importance of several protein complexes in the MAPK pathway which hints at benefits that can be achieved by further refining the model.

HDAC1 and HDAC2 independently predict mortality in hepatocellular carcinoma by a competing risk regression model in a Southeast Asian population

Histone deacetylases (HDACs) are enzymes involved in transcriptional repression. We aimed to examine the significance of HDAC1 and HDAC2 gene expression in the prediction of recurrence and survival in 156 patients with hepatocellular carcinoma (HCC) among a South East Asian population who underwent curative surgical resection in Singapore. We found that HDAC1 and HDAC2 were upregulated in the majority of HCC tissues. The presence of HDAC1 in tumor tissues was correlated with poor tumor differentiation. Notably, HDAC1 expression in adjacent non-tumor hepatic tissues was correlated with the presence of satellite nodules and multiple lesions, suggesting that HDAC1 upregulation within the field of HCC may contribute to tumor spread. Using competing risk regression analysis, we found that increased cancer-specific mortality was significantly associated with HDAC2 expression. Mortality was also increased with high HDAC1 expression. In the liver cancer cell lines, HEP3B, HEPG2, PLC5, and a colorectal cancer cell line, HCT116, the combined knockdown of HDAC1 and HDAC2 increased cell death and reduced cell proliferation as well as colony formation. In contrast, knockdown of either HDAC1 or HDAC2 alone had minimal effects on cell death and proliferation. Taken together, our study suggests that both HDAC1 and HDAC2 exert pro-survival effects in HCC cells, and the combination of isoform-specific HDAC inhibitors against both HDACs may be effective in targeting HCC to reduce mortality.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

Multivariate Statistical Analysis Applied to an IL6 Signal Transduction Model in Hepatocytes

This paper introduces the application of linear multivariate statistical techniques, including partial least squares (PLS), canonical correlation analysis (CCA) and reduced rank regression (RRR), into the area of Systems Biology. This new approach aims to extract the important proteins embedded in complex signal transduction pathway models.The analysis is performed on a model of intracellular signalling along the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in interleukin-6 (IL6) stimulated hepatocytes, which produce signal transducer and activator of transcription factor 3 (STAT3).A region of redundancy within the MAPK pathway that does not affect the STAT3 transcription was identified using CCA. This is the core finding of this analysis and cannot be obtained by inspecting the model by eye. In addition, RRR was found to isolate terms that do not significantly contribute to changes in protein concentrations, while the application of PLS does not provide such a detailed picture by virtue of its construction.This analysis has a similar objective to conventional model reduction techniques with the advantage of maintaining the meaning of the states prior to and after the reduction process. A significant model reduction is performed, with a marginal loss in accuracy, offering a more concise model while maintaining the main influencing factors on the STAT3 transcription.The findings offer a deeper understanding of the reaction terms involved, confirm the relevance of several proteins to the production of Acute Phase Proteins and complement existing findings regarding cross-talk between the two signalling pathways.

Investigation of various growth mechanisms of solid tumour growth within the linear-quadratic model for radiotherapy

The standard linear-quadratic survival model for radiotherapy is used to investigate different schedules of radiation treatment planning to study how these may be affected by different tumour repopulation kinetics between treatments.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Transform both sides model: A parametric approach

A parametric regression model for right-censored data with a log-linear median regression function and a transformation in both response and regression parts, named parametric Transform-Both-Sides (TBS) model, is presented. The TBS model has a parameter that handles data asymmetry while allowing various different distributions for the error, as long as they are unimodal symmetric distributions centered at zero. The discussion is focused on the estimation procedure with five important error distributions (normal, double-exponential, Student's t, Cauchy and logistic) and presents properties, associated functions (that is, survival and hazard functions) and estimation methods based on maximum likelihood and on the Bayesian paradigm. These procedures are implemented in TBSSurvival, an open-source fully documented R package. The use of the package is illustrated and the performance of the model is analyzed using both simulated and real data sets.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained non-linear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order non-linear plant model, simulating the dominant characteristics of a 200 MW oil-fired pou er plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during large system disturbances and extremely high rate of load changes, right across the operating range. These results compare favourably to those obtained with the state-space GPC method designed under similar conditions.

Associations between maternal characteristics and pregnancy-related stress among low-risk mothers: An observational cross-sectional study

Background: Pregnancy is viewed as a major life event and, while the majority of healthy, low-risk women adapt well to pregnancy, there are those whose levels of stress are heightened by the experience.

Objectives: To determine the level of pregnancy-related stress experienced by a group of healthy, low-risk pregnant women and to relate the level of stress with a number of maternal characteristics.

Design: An observational cross-sectional study.

Setting: A large, urban maternity centre in Northern Ireland.

Participants: Of the 306 pregnant women who were invited to participate, 278 provided informed consent and were administered one self-complete questionnaire. Due to the withdrawal criteria, 15 questionnaires were removed from the analysis, resulting in a final sample of 263 healthy, low-risk pregnant women.

Methods: Levels of stress were measured using a self-report measure designed to assess specific worries and concerns relating to pregnancy. Maternal characteristics collected included age, marital status, social status, parity, obstetric history, perceived health status and 'wantedness' for the pregnancy. Regression analysis was undertaken using an ordinary linear regression model.

Results: The mean prenatal distress score in the sample was 15.1 (SD = 7.4; range 0-46). The regression model showed that women who had had previous pregnancies, with or without complications, had significantly lower mean prenatal distress scores than primiparous women (p < 0.01). Women reporting poorer physical health had higher mean prenatal distress scores than those who reported at least average health, while women aged 16-20 experienced a mean increase in the reported prenatal distress score (p < 0.05) in comparison to the reference group of 36 years and over.

Conclusions: This study brings to light the prevalence of pregnancy-related stress within a sample representative of healthy, low-risk women. Current antenatal care is ill-equipped to identify women suffering from high levels of stress; yet a growing body of research evidence links stress with adverse pregnancy outcomes. This study emphasises that healthy, low-risk women experience a range of pregnancy-related stress and identification of stress levels, either through the use of a simple stress measurement tool or through the associated factors identified within this research study, provides valuable data on maternal well-being. (C) 2010 Elsevier Ltd. All rights reserved.

Progression of retinal pigment epithelial atrophy in stargardt disease

• PURPOSE: To evaluate retinal pigment epithelial (RPE) atrophy in patients with Stargardt disease using autofluorescence imaging (AF). • DESIGN: Retrospective observational case series. • METHODS: Demographics, best-corrected visual acuity (BCVA), AF images, and electrophysiology responses (group 1, macular dysfunction; group 2, macula + cone dysfunction; group 3, macula + cone-rod dysfunction) were evaluated at presentation and follow-up in a group of 12 patients (24 eyes) with Stargardt disease. The existence, development, and rate of enlargement of areas of RPE atrophy over time were evaluated using AF imaging. A linear regression model was used to investigate the effects of AF and electrophysiology on rate of atrophy enlargement and BCVA, adjusting for age of onset and duration of disease. • RESULTS: Eight male and 4 female patients (median age 42 years; range 24-69 years) were followed for a median of 41.5 months (range 13-66 months). All 12 patients had reduced AF compatible with RPE atrophy at presentation and in all patients the atrophy enlarged during follow-up. The mean rate of atrophy enlargement for all patients was 1.58 mm /y (SD 1.25 mm /y; range 0.13-5.27 mm /y). Only the pattern of functional loss present as detected by electrophysiology was statistically significantly associated with the rate of atrophy enlargement when correcting for other variables (P <.001), with patients in group 3 (macula + cone-rod dysfunction) having the fastest rate of atrophy enlargement (1.97 mm /y, SD 0.70 mm /y) (group 1 [macula] 1.09 mm /y, SD 0.53 mm /y; group 2 [macula + cone] 1.89 mm /y, SD 2.27 mm /y). • CONCLUSION: Variable rates of atrophy enlargement were observed in patients with Stargardt disease. The pattern of functional loss detected on electrophysiology was strongly associated with the rate of atrophy enlargement over time, thus serving as the best prognostic indicator for patients with this inherited retinal disease. © 2012 Elsevier Inc. All rights reserved.