Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data

<p>Abstract</p> <p>Background</p> <p>Statistical learning (SL) techniques can address non-linear relationships and small datasets but do not provide an output that has an epidemiologic interpretation.</p> <p>Methods</p> <p>A small set of clinical variables (CVs) for stage-1 non-small cell lung cancer patients was used to evaluate an approach for using SL methods as a preprocessing step for survival analysis. A stochastic method of training a probabilistic neural network (PNN) was used with differential evolution (DE) optimization. Survival scores were derived stochastically by combining CVs with the PNN. Patients (n = 151) were dichotomized into favorable (n = 92) and unfavorable (n = 59) survival outcome groups. These PNN derived scores were used with logistic regression (LR) modeling to predict favorable survival outcome and were integrated into the survival analysis (i.e. Kaplan-Meier analysis and Cox regression). The hybrid modeling was compared with the respective modeling using raw CVs. The area under the receiver operating characteristic curve (Az) was used to compare model predictive capability. Odds ratios (ORs) and hazard ratios (HRs) were used to compare disease associations with 95% confidence intervals (CIs).</p> <p>Results</p> <p>The LR model with the best predictive capability gave Az = 0.703. While controlling for gender and tumor grade, the OR = 0.63 (CI: 0.43, 0.91) per standard deviation (SD) increase in age indicates increasing age confers unfavorable outcome. The hybrid LR model gave Az = 0.778 by combining age and tumor grade with the PNN and controlling for gender. The PNN score and age translate inversely with respect to risk. The OR = 0.27 (CI: 0.14, 0.53) per SD increase in PNN score indicates those patients with decreased score confer unfavorable outcome. The tumor grade adjusted hazard for patients above the median age compared with those below the median was HR = 1.78 (CI: 1.06, 3.02), whereas the hazard for those patients below the median PNN score compared to those above the median was HR = 4.0 (CI: 2.13, 7.14).</p> <p>Conclusion</p> <p>We have provided preliminary evidence showing that the SL preprocessing may provide benefits in comparison with accepted approaches. The work will require further evaluation with varying datasets to confirm these findings.</p

Emissions and Energy Impacts of the Inflation Reduction Act

If goals set under the Paris Agreement are met, the world may hold warming well below 2 C; however, parties are not on track to deliver these commitments, increasing focus on policy implementation to close the gap between ambition and action. Recently, the US government passed its most prominent piece of climate legislation to date, the Inflation Reduction Act of 2022 (IRA), designed to invest in a wide range of programs that, among other provisions, incentivize clean energy and carbon management, encourage electrification and efficiency measures, reduce methane emissions, promote domestic supply chains, and address environmental justice concerns. IRA's scope and complexity make modeling important to understand impacts on emissions and energy systems. We leverage results from nine independent, state-of-the-art models to examine potential implications of key IRA provisions, showing economy wide emissions reductions between 43-48% below 2005 by 2035

Gene expression signature of atypical breast hyperplasia and regulation by SFRP1

Background Atypical breast hyperplasias (AH) have a 10-year risk of progression to invasive cancer estimated at 4–7%, with the overall risk of developing breast cancer increased by ~ 4-fold. AH lesions are estrogen receptor alpha positive (ERα+) and represent risk indicators and/or precursor lesions to low grade ERα+ tumors. Therefore, molecular profiles of AH lesions offer insights into the earliest changes in the breast epithelium, rendering it susceptible to oncogenic transformation. Methods In this study, women were selected who were diagnosed with ductal or lobular AH, but no breast cancer prior to or within the 2-year follow-up. Paired AH and histologically normal benign (HNB) tissues from patients were microdissected. RNA was isolated, amplified linearly, labeled, and hybridized to whole transcriptome microarrays to determine gene expression profiles. Genes that were differentially expressed between AH and HNB were identified using a paired analysis. Gene expression signatures distinguishing AH and HNB were defined using AGNES and PAM methods. Regulation of gene networks was investigated using breast epithelial cell lines, explant cultures of normal breast tissue and mouse tissues. Results A 99-gene signature discriminated the histologically normal and AH tissues in 81% of the cases. Network analysis identified coordinated alterations in signaling through ERα, epidermal growth factor receptors, and androgen receptor which were associated with the development of both lobular and ductal AH. Decreased expression of SFRP1 was also consistently lower in AH. Knockdown of SFRP1 in 76N-Tert cells resulted altered expression of 13 genes similarly to that observed in AH. An SFRP1-regulated network was also observed in tissues from mice lacking Sfrp1. Re-expression of SFRP1 in MCF7 cells provided further support for the SFRP1-regulated network. Treatment of breast explant cultures with rSFRP1 dampened estrogen-induced progesterone receptor levels. Conclusions The alterations in gene expression were observed in both ductal and lobular AH suggesting shared underlying mechanisms predisposing to AH. Loss of SFRP1 expression is a significant regulator of AH transcriptional profiles driving previously unidentified changes affecting responses to estrogen and possibly other pathways. The gene signature and pathways provide insights into alterations contributing to AH breast lesions

An Estimate of Avian Mortality at Communication Towers in the United States and Canada

Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts

Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species’ threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project – and avert – future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups – including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems – www.predicts.org.uk). We make site-level summary data available alongside this article. The full database will be publicly available in 2015

Cumulative Impact and Equity Objectives in Energy Systems Modeling and Policy

Energy system development is driven by the complexity inherent in physical systems and the influence of a myriad of diverse, interacting stakeholders with heterogeneous preferences. Transforming energy systems entails balancing multiple and often conflicting societal objectives. This thesis presents new modeling approaches for energy systems planning and policy evaluation, with an emphasis on cumulative impacts, equity, and system heterogeneity. The application domain of this thesis is the U.S. natural gas system, although the analytical approaches and insight of this research are intended to extend to the broader domestic and global energy system.Chapter 2 adopts a traditional economic efficiency optimization approach, coupled with methane emissions and abatement cost simulations reflecting system heterogeneity, to evaluate and design system-wide and super emitter policies related to methane abatement in the U.S. transmission and storage system. We find that most emissions, given the existing suite of technologies, have the potential to be abated. We also demonstrate that there are high societal benefits from abatement policies, and minimal (if any) private costs under standard and tax instruments. Superemitter policies, which target the highest emitting facilities, may reduce the private cost burden and achieve high emission reductions, especially if emissions across facilities are highly skewed. However, detection across all facilities is necessary regardless of the policy option, and there are nontrivial societal benefits resulting from abatement of relatively low-emitting sources. Chapters 3 aims to develop and demonstrate a data-driven approach for characterizing systems-level cumulative impacts of current energy systems. Specifically, we comprehensively assess the spatially-and temporally-resolved air, climate, and employment impacts from extraction to end use and over the life of natural gas plays in the Appalachian basin from 2004 to 2016. Our approach highlights the attribution of impacts across the supply chain, the tradeoffs between near- and long-term impacts, and the evolution and accumulation of impacts over time with changing regulation, natural gas activity, and technological and operational efficiencies and practices. We show that short-lived air quality and employment impacts track with the boom-and-bust cycle, while climate impacts persist for generations well beyond the period of natural gas activity. We also find that employment effects are spatially concentrated in rural areas with thin labor markets where development is occurring, and more than half of cumulative premature mortality is within source emissions states. We show that most premature mortality is associated with end uses, while upstream and midstream segments also account for a substantial portion of impacts. With respect to climatechange impacts, the magnitude of methane emissions across the supply chain produces temperature impacts nearly equivalent to that of carbon dioxide over a 30-year time horizon, but over longer integration periods, the warming impact from carbon dioxide dominates. We estimate a tax on production of $2 per thousand cubic foot (+172%/-76%) would compensate for cumulative climate and air quality externalities across the supply chain.In Chapter 4, we develop a multiobjective optimization model incorporating cumulative impact objectives to facilitate future energy system planning. We develop natural gas system pathways by optimizing impacts with respect to sequential natural gas decisions regarding the timing and location of infrastructure and activity from extraction to end use. Environmental and employment objectives are conflicting if we follow a natural gas pathway consistent with the status quo; however, a collection siting, emissions abatement, and renewable integration policies may collectively resolve and reverse these conflicts.In Chapter 5, we develop and demonstrate an approach for evaluating the equity state of an energy system. We apply variants of standard methods and present new methods and metrics to quantify spatial, temporal, and distributional equity, leveraging impact estimates of the shale gas boom in the Appalachian basin from Chapter 3. We find that there are high temporal and spatial inequities with respect to cumulative air and employment impacts, and that spatial inequities are constant over time reflecting largely fixed infrastructure and consumption patterns. We also present indicators of temporal climate inequities, estimating that long-term global temperature impacts are 100 times that of near-term impacts. With respect to distributional equity of air quality impacts, we do not observe a disparity in mortality rates across sub-populations on the basis of income and poverty; however, there is a trend of increasing income corresponding to decreasing damages, which demonstrates the higher health burden of lower income communities. With respect to distributional equity of labor markets, we find statistically significant declines in the income disparity and poverty rates in producing counties. Pairwise comparisons of impacts reveal that changes in air and climate impacts are sensitive to changes in employment impacts.In Chapter 6, we develop future natural gas system pathways that optimize for the multiple dimensions of equity. We expand upon the multiobjective optimization model developed in Chapter 4, deriving objectives that instill different normative concepts of spatial, temporal, and distributional equity that apply to air, climate, and employment impacts. We find that there are inherent conflicts between different equity dimensions, as well as, between equity and cumulative impact objectives in a fossil-fuel dominated energy system. However, low-carbon technologies have the potential to reduce inequities. </div

Influence of high road labor policies and practices on renewable energy costs, decarbonization pathways, and labor outcomes

Achieving an economy-wide net-zero greenhouse gas emissions goal by mid-century in the United States entails transforming the energy workforce. In this study, we focus on the influence of increased labor compensation and domestic manufacturing shares on (a) renewable energy technology costs, (b) the costs of transitioning the U.S. economy to net-zero emissions, and (c) labor outcomes, including total employment and wage benefits, associated with the deployment of utility-scale solar photovoltaics (PV) and land based and offshore wind power. We find that manufacturing and installation labor cost premiums as well as increases in domestic content shares across wind and utility-scale solar PV supply chains result in relatively modest increases in total capital and operating costs. These small increases in technology costs may be partially or fully offset by increases in labor productivity. We also show that solar and wind technology cost premiums associated with high road labor policies have a minimal effect on the pace and scale of renewable energy deployment and the total cost of transitioning to a net-zero emissions economy. Public policies such as tax credits, workforce development support, and other instruments can redistribute technology cost premiums associated with high road labor policies to support both firms and workers

Rooftop solar, electric vehicle, and heat pump adoption in rural areas in the United States

The deployment of residential rooftop solar, electric vehicles (EVs), and heat pumps is critical to meet climate goals. We evaluate historical community- and household-level technology adoption patterns in rural areas, and explore associations with housing, socioeconomic, demographic, political, spatial, and energy equity characteristics. We reveal several emergent patterns and disparities in rural technology adoption. We find that higher educational attainment and democratic voting rates are significant predictors of adoption across all residential technologies. Specifically, EV adoption shows the highest association with democratic voting rates. We also find that rooftop solar adoption is most significantly inversely associated with energy burden, while there is a high degree of spatial dependence in heat pump adoption. Rooftop solar and EV adoption are particularly correlated at household, community, and state levels. Findings suggest that designing policies and programs that promote information diffusion, target low-income renters, and are tailored to the geographic and political context may together increase technology adoption rates and ensure more equitable diffusion. By identifying the factors that influence technology adoption in rural areas, we aim to inform the development of policies and programs that can facilitate the widespread deployment of clean energy technologies and contribute to the achievement of climate goals. The following includes descriptions of data, code, and functions used for data analyses and visualizations for the paper titled, "Rooftop solar, electric vehicle, and heat pump adoption in rural areas in the United States." Please cite as: Min, Yohan & Mayfield, Erin. Rooftop solar, electric vehicle, and heat pump adoption in rural areas in the United States. Energy Research & Social Science 2023;105:103292. https://doi.org/10.1016/j.erss.2023.103292