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.

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.

The Cyclin D1 (CCND1) A870G polymorphism predicts clinical outcome to lapatinib and capecitabine in HER2-positive metastatic breast cancer

BACKGROUND: Lapatinib plus capecitabine emerged as an efficacious therapy in metastatic breast cancer (mBC). We aimed to identify germline single-nucleotide polymorphisms (SNPs) in genes involved in capecitabine catabolism and human epidermal receptor signaling that were associated with clinical outcome to assist in selecting patients likely to benefit from this combination.

PATIENTS AND METHODS: DNA was extracted from 240 of 399 patients enrolled in EGF100151 clinical trial (NCT00078572; clinicaltrials.gov) and SNPs were successfully evaluated in 234 patients. The associations between SNPs and clinical outcome were analyzed using Fisher's exact test, Kaplan-Meier curves, log-rank tests, likelihood ratio test within logistic or Cox regression model, as appropriate.

RESULTS: There were significant interactions between CCND1 A870G and clinical outcome. Patients carrying the A-allele were more likely to benefit from lapatinib plus capecitabine versus capecitabine when compared with patients harboring G/G (P = 0.022, 0.024 and 0.04, respectively). In patients with the A-allele, the response rate (RR) was significantly higher with lapatinib plus capecitabine (35%) compared with capecitabine (11%; P = 0.001) but not between treatments in patients with G/G (RR = 24% and 32%, respectively; P = 0.85). Time to tumor progression (TTP) was longer in patients with the A-allele treated with lapatinib plus capecitabine compared with capecitabine (median TTP = 7.9 and 3.4 months; P < 0.001), but not in patients with G/G (median TTP = 6.1 and 6.6 months; P = 0.92).

CONCLUSION: Our findings suggest that CCND1A870G may be useful in predicting clinical outcome in HER2-positive mBC patients treated with lapatinib plus capecitabine.

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.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.

Determinants of WTP for Prosecco wine: A latent class regression with attitudinal responses

Purpose – Under investigation is Prosecco wine, a sparkling white wine from North-East Italy.
Information collection on consumer perceptions is particularly relevant when developing market
strategies for wine, especially so when local production and certification of origin play an important
role in the wine market of a given district, as in the case at hand. Investigating and characterizing the
structure of preference heterogeneity become crucial steps in every successful marketing strategy. The
purpose of this paper is to investigate the sources of systematic differences in consumer preferences.
Design/methodology/approach – The paper explores the effect of inclusion of answers to
attitudinal questions in a latent class regression model of stated willingness to pay (WTP) for this
specialty wine. These additional variables were included in the membership equations to investigate
whether they could be of help in the identification of latent classes. The individual specific WTPs from
the sampled respondents were then derived from the best fitting model and examined for consistency.
Findings – The use of answers to attitudinal question in the latent class regression model is found to
improve model fit, thereby helping in the identification of latent classes. The best performing model
obtained makes use of both attitudinal scores and socio-economic covariates identifying five latent
classes. A reasonable pattern of differences in WTP for Prosecco between CDO and TGI types were
derived from this model.
Originality/value – The approach appears informative and promising: attitudes emerge as
important ancillary indicators of taste differences for specialty wines. This might be of interest per se
and of practical use in market segmentation. If future research shows that these variables can be of use
in other contexts, it is quite possible that more attitudinal questions will be routinely incorporated in
structural latent class hedonic models.

PET-CT as a predictor of outcome in resectable colorectal liver metastases

Introduction
PET-computed tomography (PET-CT) is a useful staging imaging modality in colorectal liver metastases (CRLM). This study aimed to determine whether PET-CT parameters, standardized uptake value (SUV) and reconstructed tumour volume (RTV), are predictors of prognosis and survival.

Methods
A study of all resectable CRLM patients in the regional HPB unit from 2007–2009 was performed. Preoperative PET-CT scans were retrospectively reviewed; SUV, diameter and RTV for each lesion was recorded. Correlation analysis was performed with other pathological and biochemical parameters, by Pearson’s correlation analysis. Survival analysis was performed using Cox regression hazard model. A P value of less than 0.05 was considered statistically significant.

Results
A total of 79 patients were included. SUV moderately correlated with tumour diameter, both PET-CT (r=0.4927; P<0.0001) and histology (r=0.4513; P=0.0003); RTV (r=0.4489; P<0.001), preoperative carcinoembryonic antigen (CEA) (r=0.4977; P=0.0001), and postoperative CEA (r=0.3727; P=0.004). Multivariate analysis found that an independent predictor of SUVmax was preoperative CEA (P=0.03). RTV strongly correlated with preoperative CEA (r=0.9389; P<0.0001). SUV and RTV had a negative effect on survival.

Conclusion
PET-CT, in the setting of CRLM, may have a prognostic role in assessing survival. Although no definite conclusions can be drawn regarding the prognostic role of SUV and RTV, it acts to reinforce the need for further prospective studies to validate these findings.

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.

Validating the accuracy of a model to predict the onset of myopia in children.

PURPOSE: To assess the sensitivity and specificity of models predicting myopia onset among ethnically Chinese children. METHODS: Visual acuity, height, weight, biometry (A-scan, keratometry), and refractive error were assessed at baseline and 3 years later using the same equipment and protocol in primary schools in Xiamen (China) and Singapore. A regression model predicting the onset of myopia < -0.75 diopters (D) after 3 years in either eye among Xiamen children was validated with Singapore data. RESULTS: Baseline data were collected from 236 Xiamen children (mean age, 7.82 ± 0.63 years) and from 1979 predominantly Chinese children in Singapore (7.83 ± 0.84 years). Singapore children were significantly taller and heavier, and had more myopia (31.4% vs. 6.36% < -0.75 D in either eye, P < 0.001) and longer mean axial length. Three-year follow-up was available for 80.0% of Xiamen children and 83.1% in Singapore. For Xiamen, the area under the receiver-operator curve (AUC) in a model including ocular biometry, height, weight, and presenting visual acuity was 0.974 (95% confidence interval [CI], 0.945-0.997). In Singapore, the same model achieved sensitivity, specificity, and positive predictive value of 0.844, 0.650, and 0.669, with an AUC of 0.815 (95% CI, 0.791-0.839). CONCLUSIONS: Accuracy in predicting myopia onset based on simple measurements may be sufficient to make targeted early intervention practical in settings such as Singapore with high myopia prevalence. Models based on cohorts with a greater prevalence of high myopia than that in Xiamen could be used to assess accuracy of models predicting more severe forms of myopia.

Do Total and High Density Lipoprotein Cholesterol and Triglycerides Act Independently in the Prediction of Ischemic Heart Disease? Ten-Year Follow-Up of Caerphilly and Speedwell Cohorts

Several studies have suggested that men with raised plasma triglycerides (TGs) in combination with adverse levels of other lipids may be at special risk of subsequent ischemic heart disease (IHD). We examined the independent and combined effects of plasma lipids at 10 years of follow-up. We measured fasting TGs, total cholesterol (TC), and high density lipoprotein cholesterol (HDLC) in 4362 men (aged 45 to 63 years) from 2 study populations and reexamined them at intervals during a 10-year follow-up. Major IHD events (death from IHD, clinical myocardial infarction, or ECG-defined myocardial infarction) were recorded. Five hundred thirty-three major IHD events occurred. All 3 lipids were strongly and independently predictive of IHD after 10 years of follow-up. Subjects were then divided into 27 groups (ie, 33) by the tertiles of TGs, TC, and HDLC. The number of events observed in each group was compared with that predicted by a logistic regression model, which included terms for the 3 lipids (without interactions) and potential confounding variables. The incidence of IHD was 22.6% in the group with the lipid risk factor combination with the highest expected risk (high TGs, high TC, and low HDLC) and 4.7% in the group with the lowest expected risk (P

Cathepsin S expression: An independent prognostic factor in glioblastoma tumours - A pilot Study

Cysteine proteinases have been implicated in astrocytoma invasion. We recently demonstrated that cathepsin S (CatS) expression is up-regulated in astrocytomas and provided evidence for a potential role in astrocytoma invasion (Flannery et al., Am J Path 2003;163(1):175–82). We aimed to evaluate the significance of CatS in human astrocytoma progression and as a prognostic marker. Frozen tissue homogenates from 71 patients with astrocytomas and 3 normal brain specimens were subjected to ELISA analyses. Immunohistochemical analysis of CatS expression was performed on 126 paraffin-embedded tumour samples. Fifty-one astrocytoma cases were suitable for both frozen tissue and paraffin tissue analysis. ELISA revealed minimal expression of CatS in normal brain homogenates. CatS expression was increased in grade IV tumours whereas astrocytoma grades I–III exhibited lower values. Immunohistochemical analysis revealed a similar pattern of expression. Moreover, high-CatS immunohistochemical scores in glioblastomas were associated with significantly shorter survival (10 vs. 5 months, p = 0.014). With forced inclusion of patient age, radiation dose and Karnofsky score in the Cox multivariate model, CatS score was found to be an independent predictor of survival. CatS expression in astrocytomas is associated with tumour progression and poor outcome in glioblastomas. CatS may serve as a useful prognostic indicator and potential target for anti-invasive therapy.

Cyclooxygenase-2 expression correlates with phaeochromocytoma malignancy.

Abstract Aims: Phaeochromocytomas are rare but potentially life-threatening neuroendocrine tumours of the adrenal medulla or sympathetic nervous system ganglia. There are no histological features which reliably differentiate benign from malignant phaeochromocytomas. The current study evaluated cyclooxygenase-2 (Cox-2) and Bcl-2 as tissue-based biomarkers of phaeochromocytoma prognosis. Methods and Results: Cox-2 and Bcl-2 expression were examined immunohistochemically in tissue from forty-one sporadic phaeochromocytoma patients followed up for a minimum of five years after diagnosis. There was a statistically significant association between Cox-2 histoscore (intensity x porportion) and the development of tumour recurrence or metastases (p=0.006). A significant relationship between the co-expression of Cox-2 and Bcl-2 in the primary tumour and the presence of recurrent disease was observed (p=0.034). A highly significant association was observed between, (i) the tumour-associated expression of these two oncoproteins (p=0.001) and, (ii) Cox-2 histoscore and the presence of Bcl-2 expression (p=0.002). Cox regression analysis demonstrated no significant relationship between, (i) the presence or absence of either Cox-2 or Bcl-2 and patient survival or, (ii) between Cox-2 histoscore and patient survival. Conclusions: These results suggest that Cox-2 and Bcl-2 may promote phaeochromocytoma malignancy and that these oncoproteins may be valuable surrogate markers of an aggressive tumour phenotype.