The Impact of Financial Renewable Energy Policy Incentives vs. Government Renewable Energy Regulatory Policies on CO2 Emissions and Employment in US States

With a clear political influence spearheading the fight against climate change, this paper investigates renewable energy policies in U.S. states from 2000 to 2018 by utilizing panel data and OLS regression analysis to pinpoint the most effective renewable energy policies. Policy data in each state comes from DSIRE, a database of state incentives for renewables & efficiency. Specific policies examined in this paper include Sales Tax Incentives, Grant Programs, Loan Programs, Renewable Portfolio Standards, Energy Standards for Public Buildings, Building Energy Codes, and Solar/Wind Access Policies. Controls for CO2 emission analysis include total state GDP, transportation GDP, manufacturing GDP, utilities GDP, number of registered vehicles, and population. All GDP controls are lagged to avoid endogeneity with CO2 emissions. Employment analysis includes sex and race as controls. Both dependent variables are run with state and year fixed effects. Contrary to existing literature, results vary depending upon the high-level subsamples in the analysis: High Emission Group, Low Emission Group, High Population Group, Low Population Group, Red States, and Blue States. Most policies examined have opposite effects in their subsample counterparts. For example, an RPS Policy increased emissions in Red States by 2.1% but decreased emissions by 3.4% in Blue States. However, a Grant or Loan Policy has positive impacts on employment across all subsamples. Overall, the results discussed in this paper give insight into how popular policies can be effective when implemented in the right situation. These findings indicate that policy-makers should make decisions on a case-by-case basis to reach their desired goals

High nuclear MSK1 is associated with longer survival in breast cancer patients

Purpose: Mitogen- and stress- activated kinases (MSKs) are important substrates of the mitogen-activated protein kinase (MAPK)-activated protein kinase family. MSK1 and MSK2 are both nuclear serine/threonine protein kinases, with MSK1 being suggested to potentially play a role in breast cancer cell proliferation, cell cycle progression, cell migration, invasion and tumour growth. The aim of the current study was to assess MSK1 protein expression in breast cancer tumour specimens, evaluating its prognostic significance. Methods: A large cohort of 1902 early stage invasive breast cancer patients was used to explore the expression of MSK1. Protein expression was examined using standard immunohistochemistry on tissue microarrays. Results: Low MSK1 protein expression was associated with younger age (P=0.004), higher tumour grade (P<0.001), higher Nottingham Prognostic Index scores (P=0.007), negative ER (P<0.001) and PR (P<0.001) status, and with triple-negative (P<0.001) and basal-like (P<0.001) phenotypes. Low MSK1 protein expression was significantly associated with shorter time to distant metastasis (P<0.001), and recurrence (P=0.013) and early death due to breast cancer (P=0.01). This association between high MSK1 expression and improved breast cancer-specific survival was observed in the whole cohort (P=0.009) and in the HER2 negative and non-basal like tumours (P=0.006 and P=0.024, respectively). Multivariate analysis including other prognostic variables indicated that MSK1 is not an independent marker of outcome. Conclusions: High MSK1 is associated with improved breast cancer-specific survival in early stage invasive breast cancer patients, and has additional prognostic value in HER2 negative and non-basal like disease. Although not an independent marker of outcome we believe such findings, and significant associations with well-established negative prognostic factors (age, grade, Nottingham Prognostic Index, hormone receptor status, time to distant metastasi

Clinical and cost-effectiveness of a diabetes education and behavioural weight management programme versus a diabetes education programme in adults with a recent diagnosis of type 2 diabetes: study protocol for the Glucose Lowering through Weight management (GLoW) randomised controlled trial

Introduction: People with type 2 diabetes (T2D) can improve glycaemic control or even achieve remission through weight loss and reduce their use of medication and risk of cardiovascular disease. The Glucose Lowering through Weight management (GLoW) trial will evaluate whether a tailored diabetes education and behavioural weight management programme (DEW) is more effective and cost-effective than a diabetes education (DE) programme in helping people with overweight or obesity and a recent diagnosis of T2D to lower their blood glucose, lose weight and improve other markers of cardiovascular risk. Methods and analysis: This study is a pragmatic, randomised, single-blind, parallel group, two-arm, superiority trial. We will recruit 576 adults with body mass index>25 kg/m2 and diagnosis of T2D in the past 3 years and randomise them to a tailored DEW or a DE programme. Participants will attend measurement appointments at a local general practitioner practice or research centre at baseline, 6 and 12 months. The primary outcome is 12-month change in glycated haemoglobin. The effect of the intervention on the primary outcome will be estimated and tested using a linear regression model (analysis of covariance) including randomisation group and adjusted for baseline value of the outcome and the randomisation stratifiers. Participants will be included in the group to which they were randomised, under the intention-to-treat principle. Secondary outcomes include 6-month and 12-month changes in body weight, body fat percentage, systolic and diastolic blood pressure and lipid profile; probability of achieving good glycaemic control; probability of achieving remission from diabetes; probability of losing 5% and 10% body weight and modelled cardiovascular risk (UKPDS). An intention-to-treat within-trial cost-effectiveness analysis will be conducted from NHS and societal perspectives using participant-level data. Qualitative interviews will be conducted with participants to understand why and how the programme achieved its results and how participants manage their weight after the programme ends. Ethics and dissemination: Ethical approval was received from East of Scotland Research Ethics Service on 15 May 2018 (18/ES/0048). This protocol (V.3) was approved on 19 June 2019. Findings will be published in peer-reviewed scientific journals and communicated to other stakeholders as appropriate. Trial registration number: ISRCTN18399564

Calpain-1 expression is associated with relapse-free survival in breast cancer patients treated with trastuzumab following adjuvant chemotherapy

The calpain family, and their endogenous inhibitor calpastatin, has been implicated in cancer progression, and recent in vitro data have indicated a role in trastuzumab resistance. The aims of our study were to examine expression levels of calpastatin, calpain‐1 and calpain‐2 in breast tumours from patients treated with trastuzumab following adjuvant chemotherapy to determine their potential as biomarkers to predict therapeutic response. The expression of calpastatin, calpain‐1 and calpain‐2 was determined, using immunohistochemistry (IHC), in tumours from a series of 93 patients with primary breast cancer treated with surgery and adjuvant chemotherapy with or without trastuzumab followed by trastuzumab to complete 1 year of therapy. IHC was performed using tissue microarrays constructed from cores taken from intratumour and peripheral tumour areas. Expression was correlated with clinicopathologic variables and patient outcome. Calpastatin expression was correlated with Nottingham prognostic index (p = 0.003) and lymph node status (p = 0.007). Trastuzumab resistance was defined as disease relapse during therapy. Calpain‐1 expression is associated with relapse‐free survival (p = 0.001) and remained significant in multivariate analysis accounting for confounding pathological and treatment variables (hazard ratio 4.60, 95% confidence interval 1.05–20.25; p = 0.043). Calpain‐1 may be a useful biomarker to predict relapse‐free survival in breast cancer patients treated with adjuvant trastuzumab and chemotherapy. A larger verification study is warranted

Sheep Updates 2007 - part 3

This session covers seven papers from different authors: PROFITABILITY 1. Benchmarking demonstrates both the potential and realised productivity gains in the sheep and wool industry, Andrew Ritchie, Edward Riggall and James Hall, ICON Agriculture, Darkan 2. Improving sheep genetics will increase farm profitability, Gus Rose, Johan Greeff Department of Agriculture and Food Western Australia, John Young Farming Systems Analysis Service, WA 3. Meat, Merinos and making money in WA Pastoral Zone, M. Alchin, M. Young and T. Johnson, Department of Agriculture and Food Western Australia, GRAZING 4. Nitrogen - farmers\u27 friend or foe? John Lucy and Martin Staines, Department of Agriculture and Food Western Australia 5. Drought proofing grazing systems - a case study from Binnu 2006/7, Tim Wiley & Rob Grima, Department of Agriculture & Food Western Australia 6. Minimising \u27Esperance Storm\u27 livestock losses, Sandra Prosser and Matt Ryan, Department of Agriculture and Food Western Australia 7. Sub-tropical grasses in WA - what is their potential? Geoff Moore, Tony Albertsen, Department of Agriculture & Food Western Australia, Phil Barrett-Lennard, Evergreen Farming, George Woolston, John Titterington, Department of Agriculture and Food Western Australia, Sarah Knight, Irwin-Mingenew Group, Brianna Peake, Liebe Group, Buntine, W

A Comparison of Different Approaches to Unravel the Latent Structure within Metabolic Syndrome

Background: Exploratory factor analysis is a commonly used statistical technique in metabolic syndrome research to uncover latent structure amongst metabolic variables. The application of factor analysis requires methodological decisions that reflect the hypothesis of the metabolic syndrome construct. These decisions often raise the complexity of the interpretation from the output. We propose two alternative techniques developed from cluster analysis which can achieve a clinically relevant structure, whilst maintaining intuitive advantages of clustering methodology. Methods: Two advanced techniques of clustering in the VARCLUS and matroid methods are discussed and implemented on a metabolic syndrome data set to analyze the structure of ten metabolic risk factors. The subjects were selected from the normative aging study based in Boston, Massachusetts. The sample included a total of 847 men aged between 21 and 81 years who provided complete data on selected risk factors during the period 1987 to 1991. Results: Four core components were identified by the clustering methods. These are labelled obesity, lipids, insulin resistance and blood pressure. The exploratory factor analysis with oblique rotation suggested an overlap of the loadings identified on the insulin resistance and obesity factors. The VARCLUS and matroid analyses separated these components and were able to demonstrate associations between individual risk factors. Conclusions: An oblique rotation can be selected to reflect the clinical concept of a single underlying syndrome, howeve

Working with collinearity in epidemiology: development of collinearity diagnostics, identifying latent constructs in exploratory research and dealing with perfectly collinear variables in regression

Collinearity plays an integral role in regression studies involving epidemiological data. Variables often form part of a common biological mechanism or measure the same element of a latent structure. It is a natural feature of most data and as such it is rarely possible to physically control for collinearity in data collection. A focus is placed on the analytical assessment of the data. Departures from independence can severely distort the interpretation of a model and the role of each covariate. This leads to increased inaccuracy as expressed through the regression coefficients and increased uncertainty as expressed through coefficient standard errors. Such a feature has the potential to impact on the clinical conclusions formed from regression studies. The work in this thesis first considers an assessment of the impact of collinearity on model parameters and the conclusions formed. A new collinearity index is developed which incorporates the role of the response in moderating the impact of collinearity. The idea for the new index is developed using vector geometry and extended to a general measure. The work in collinearity is later extended to consider the formation of a dependency structure from a collection of collinear variables. A novel methodology, labelled the matroid approach, is coded and implemented on a metabolic syndrome dataset to extract a latent structure that could represent this clinical construct. Comparisons are subsequently made to existing exploratory factor analysis and clustering methods in the literature. Finally, the unique problem of perfect collinearity is considered in a lifecourse and age-period-cohort setting. The justification of constraint and non-constraint regression methods is considered in an attempt to provide ‘solutions’ to the identification problem generated by collinearity

Putative effectors for prognosis in lung adenocarcinoma are ethnic and gender specific

Lung adenocarcinoma possesses distinct patterns of EGFR/KRAS mutations between East Asian and Western, male and female patients. However, beyond the well-known EGFR/KRAS distinction, gender and ethnic specific molecular aberrations and their effects on prognosis remain largely unexplored. Association modules capture the dependency of an effector molecular aberration and target gene expressions. We established association modules from the copy number variation (CNV), DNA methylation and mRNA expression data of a Taiwanese female cohort. The inferred modules were validated in four external datasets of East Asian and Caucasian patients by examining the coherence of the target gene expressions and their associations with prognostic outcomes. Modules 1 (cis-acting effects with chromosome 7 CNV) and 3 (DNA methylations of UBIAD1 and VAV1) possessed significantly negative associations with survival times among two East Asian patient cohorts. Module 2 (cis-acting effects with chromosome 18 CNV) possessed significantly negative associations with survival times among the East Asian female subpopulation alone. By examining the genomic locations and functions of the target genes, we identified several putative effectors of the two cis-acting CNV modules: RAC1, EGFR, CDK5 and RALBP1. Furthermore, module 3 targets were enriched with genes involved in cell proliferation and division and hence were consistent with the negative associations with survival times. We demonstrated that association modules in lung adenocarcinoma with significant links of prognostic outcomes were ethnic and/or gender specific. This discovery has profound implications in diagnosis and treatment of lung adenocarcinoma and echoes the fundamental principles of the personalized medicine paradigm

The correlations between cluster components.

<p>The correlations between cluster components are analogous to inter-cluster correlations in a factor analysis with oblique rotation. Cluster 3 and cluster 4 demonstrate the strongest correlation (0.414), indicating an association between obesity and insulin resistance risk factors.</p