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Abstract As climate risks increase, there is a challenge of combining the goals of carbon mitigation and climate adaptation into building designs. These two goals are often misaligned because adaptation measures use additional materials and equipment, which can increase greenhouse gas (GHG) emissions. This phenomenon means that building design involves tradeoffs between enhanced structural resilience and reduced GHG emissions. This paper seeks to identify the optimal investment allocation mechanisms between carbon mitigation and climate adaptation measures for the design of buildings in hurricane-prone regions. A dynamic decision-making model is developed to maximize individual investors' expected payoffs over a building's lifetime. The model is based on the damage evaluation of non-stationary hurricane occurrence and building emission performance under different mitigation scenarios. The results reveal a transition from long-advocated low-carbon investments to risk-oriented portfolios for building retrofits. A case study on Anne Arundel County, MD, for which a "60-40" resilience/abatement portfolio is recommended, shows the value of enhancing structural resilience. Discretion on the accuracy of insurance premium discounts is needed to support risk mitigation efforts. Meanwhile, subsidies for emission abatements are recommended to accommodate existing emission trading schemes and building property values. [ABSTRACT FROM AUTHOR]

Voluntary carbon trading has long been criticized for producing a large number of non-additional offsets. The reason for this production lies primarily in the use of a project-based baseline that misrepresents the business-as-usual (BAU) emissions of individual projects. Performance baseline however, as an alternative baseline approach, does not rely on evaluating individual BAU emissions. Instead, it uses a pre-defined emission threshold for a class of project activities. This paper compares the effects of the two baseline approaches on sectoral emission mitigation and compliance costs by modeling dynamic emission abatement behaviors. In the context of the U.S. commercial building sector, the modeling results indicate that while both baseline approaches are capable of reducing sectoral emissions, the use of performance baseline is especially advantageous under private cost information and uncertain market environments. The performance baseline should be stratified by region to ensure the equity in offset allocation in sectors that are highly diversified in emission magnitudes. [ABSTRACT FROM AUTHOR]

The significance of global warming is shaping building procurement, with much of the effort aimed at reducing greenhouse gas emissions through a preferential bidding mechanism. However, only a limited number of studies have quantitatively estimated the potential impact of bid preference on the emission reduction from building construction. The aim of this paper is to improve our understanding of how bid discounts change owners' procurement costs and the magnitudes of emission mitigation. A two-stage optimization model is designed to help the owner determine the discount rate that would automatically control the emission of the awarded contract within a desired level. The goal is for bidders to regard emission reduction efforts as an investment, with returns coming in the form of an increased chance of winning the bid. Our results imply that for the building retrofit project studied herein, a discount rate of 0.6 is suggested for every percentage of emission reduction. This discount level reduces greenhouse gas emissions by 28.2% while raising procurement costs by 3.7% relative to a non-preference mechanism. The increased procurement costs can be partially compensated under existing cap-and-trade scheme. The results show that the choice of discount significantly alters bidders' efforts on emission mitigation, and emission control cost can be minimized with the proposed optimization algorithm. [ABSTRACT FROM AUTHOR]

[Display omitted] Survivin, a member of the inhibitor of apoptosis protein family, exists as a homodimer and is aberrantly upregulated in a wide spectrum of cancers. It was thought to be an ideal target due to its lack of expression in most adult normal tissues and importance in cancer cell survival. However, it has been challenging to target survivin due to its "undruggable" nature. We previously attempted to target its dimerization domain with a hypothesis that inhibiting survivin dimerization would promote its degradation in proteasome, which led to identification of a lead small-molecule inhibitor, LQZ-7F. LQZ-7F consists of a flat tetracyclic aromatic core with labile hydrazone linking a 1,2,5-oxadiazole moiety. In this study, we tested the hypothesis that LQZ-7F could be developed as a prodrug because the labile hydrazone linker could be hydrolyzed, releasing the tetracyclic aromatic core. To this end, we synthesized the tetracyclic aromatic core (LQZ-7F1) using reported procedure and tested LQZ-7F1 for its biological activities. Here we show that LQZ-7F1 has a significantly improved potency with submicromolar IC 50's and induces spontaneous apoptosis in prostate cancer cells. It also more effectively inhibits survivin dimerization and induces survivin degradation in a proteasome-dependent manner than LQZ-7F. We also show that the combination of LQZ-7F1 and docetaxel have strong synergism in inhibiting prostate cancer cell survival. Together, we conclude that the hydrazone linker with the oxadiazole tail is dispensable for survivin inhibition and the survivin dimerization inhibitor, LQZ-7F, may be developed as a prodrug for prostate cancer treatment and to overcome docetaxel resistance. [ABSTRACT FROM AUTHOR]

[Display omitted] Drug resistance is the major obstacle that undermines effective cancer treatment. Recently, the application of gas signaling molecules, e.g., carbon monoxide (CO), in overcoming drug resistance has gained significant attention. Growing evidence showed that CO could inhibit mitochondria respiratory effect and glycolysis, two major ATP production pathways in cancer cells, and suppress angiogenesis and inhibit the activity of cystathionine β-synthase that is important in regulating cancer cells homeostasis, leading to synergistic effects when combined with cisplatin, doxorubicin, or phototherapy, etc. in certain resistant cancer cells. In the current review, we attempted to have a summary of these research conducted in the past decade using CO in treating drug resistant cancers, and have a detailed interpretation of the underlying mechanisms. The critical challenges will be discussed and potential solutions will also be provided. The information collected in this work will hopefully evoke more effects in using CO for the treatment of drug resistant cancers. [ABSTRACT FROM AUTHOR]

Most of the construction practices in the field of risk identification focus on the expertise, views, and judgments of subject matter experts. While the conventional expert-based approaches provide worth, several challenges exist due to time-consuming and expensive aspects. Moreover, limited experience in major projects makes public agencies susceptible to subjective judgment biases. To address these limitations, this study introduced a data-driven framework for risk identification using historical data and artificial intelligence techniques, particularly word embedding models. The model matches various risk items in past projects by considering the semantic meaning of words to find high frequency and consequence risks. Risk registers from more than 70 U.S. major transportation projects form the input dataset. The model is tested with more than 66% recall and 0.59 F 1 -score for risk detection for new projects. Acquired knowledge from previous projects assists project teams and public agencies to be well-equipped with a risk identification model instead of starting from scratch. • A novel data-driven model for risk template generation is introduced. • A comprehensive risk database for major transportation projects is developed using historical data. • The model learns semantic textual features of risk items using word embedding models. • The model customizes risk templates considering risk prevalence, risk consequences, and project characteristics. • The experimental results revealed more than 66% of testing projects' risk items are covered by risk templates. [ABSTRACT FROM AUTHOR]

eIF3i, a 36-kDa protein, is a putative subunit of the eIF3 complex important for translation initiation of mRNAs. It is a WD40 domain-containing protein with seven WD40 repeats that forms a β-propeller structure with an important function in pre-initiation complex formation and mRNA translation initiation. In addition to participating in the eIF3 complex formation for global translational control, eIF3i may bind to specific mRNAs and regulate their translation individually. Furthermore, eIF3i has been shown to bind to TGF-β type II receptor and participate in TGF-β signaling. It may also participate in and regulate other signaling pathways including Wnt/β-catenin pathway via translational regulation of COX-2 synthesis. These multiple canonical and noncanonical functions of eIF3i in translational control and in regulating signal transduction pathways may be responsible for its role in cell differentiation, cell cycle regulation, proliferation, and tumorigenesis. In this review, we will critically evaluate recent progresses and assess future prospects in studying eIF3i. [ABSTRACT FROM AUTHOR]

Among the key barriers to profit in Energy Performance Contracting (EPC) are uncertainties about attaining the realized energy cost savings and potential disputes over the guaranteed cost savings. In this paper, a methodology has been proposed to assist the Energy Service Company (ESCO): (1) to evaluate the risk threshold if the guarantee has already been made, and (2) to determine the guarantee design, if the guarantee has not been made yet, that not only promises the ESCO’s profitability from EPC but also maintains its competitiveness to win the bid. Uncertainties within the energy cost savings are modeled stochastically using Monte-Carlo simulation, taking both the energy price fluctuation and the facility performance variability into account. Based on that, a strategic energy savings guarantee design curve is derived, that all the points on it would return as appropriate guarantees. Finally, a campus case is presented to demonstrate the applicability for finding the appropriate guaranteed savings value. This method is also worth popularizing in similar performance-based projects. [ABSTRACT FROM AUTHOR]

• JD-R model were used to depict what factors affect safety leadership. • Job characteristics and personal resources affect safety leadership via engagement. • Personal resources moderate how job characteristics affect engagement. Although research has shown that safety leadership is a strong predictor of safety outcomes, it is unclear what factors lead to safety leadership. This unexplored area has hindered the development of effective interventions to promote safety leadership. This study addresses the knowledge gap by examining how job characteristics and personal resources influence construction leaders' engagement in safety leadership based on the Job Demands-Resources (JD-R) model in positive psychology. An online survey was distributed to all managerial employees in a large U.S. construction organization, resulting in 383 valid responses. The structural equation modeling analysis indicates that job characteristics (social support, work autonomy and risk perception) and personal resources (psychological capital) significantly contribute to safety-specific transformational leadership through work engagement. Moreover, psychological capital moderates the effect of social support on leaders' engagement in safety leadership. The implications of the present findings regarding safety leadership research are also discussed. [ABSTRACT FROM AUTHOR]

Hot Mix Asphalt (HMA) is used in the construction of highway roads, parking lots and other pavement repairs in the US and worldwide. During asphalt pavement installation, a large amount of asphalt fume containing volatile organic compounds (VOCs) is emitted, causing potential health risks to construction workers. The field data investigation in this paper reports the concentration of VOCs around the workers on site using the Photo Ionization Detection (PID) device. Additionally, this paper presents a health risk evaluation model based on the Monte Carlo simulation to assess the carcinogenic and non-carcinogenic risks of workers during pavement construction. More specifically, distribution and sensitivity analyses illustrate the factors that pose the greatest health risks caused by certain VOCs. The study calls for better health risk controls by targeting the emission sources, propagating pathways, and individual receptors of the VOCs to protect workers' health during pavement construction. This paper contributes to the knowledge of VOCs generated from HMA and the potential health risks to construction workers, as well as suggests the implementation of new requirements for pavement construction codes and safety regulations. [ABSTRACT FROM AUTHOR]

The development of multidrug resistance (MDR) to chemotherapy remains a major challenge in the treatment of cancer. Numerous mechanisms have been recognized that cause MDR, but one of the most important mechanisms is overexpression of adenosine triphosphate (ATP)-binding cassette (ABC) transporters, through which the efflux of various anticancer drugs against their concentration gradients is powered by ATP. In recent years, small molecular tyrosine kinase inhibitors (TKIs) have been developed for treatment in various human cancers overexpressing epidermal growth factor receptor (EGFR). At the same time, some TKIs have been shown to be capable of inhibiting ABC transporter-mediated MDR. Dacomitinib (PF-00299804) is a second generation, irreversible TKI, which has shown positive anticancer activities in some preclinical and clinical trials. As many TKIs are substrates or inhibitors of ABC transporters, this study investigates whether dacomitinib could interact with ABC subfamily members that mediate MDR, including ABCB1 (P-gp), ABCG2 (BCRP) and ABCC1 (MRP1). The results showed that dacomitinib at 1.0 μM significantly reversed drug resistance mediated by ABCB1 and ABCG2, but not ABCC1, doing so by antagonizing the drug efflux function in ABCB1- and ABCG2-overexpressing cell lines. The reversal effect on ABCB1-overexpressing cells is more potent than that on ABCG2-overexpressing cells. In addition, dacomitinib at reversal concentration affected neither the protein expression level nor the localization of ABCB1 and ABCG2. Therefore, the mechanisms of this modulating effect are likely to be the following: first, as an inhibitor of ABCB1 or ABCG2 transporters, dacomitinib binds to drug-substrate site in transmembrane domains (TMD) stably in a noncompetitive manner; or second, dacomitinib inhibits ATPase activity and maintains the stability of TMD conformation in a concentration-dependent manner thereby inhibiting the drug efflux function of ABCB1 or ABCG2 transporter. This study provides a useful combinational therapeutic strategy with dacomitinib and substrates of ABCB1 and/or ABCG2 transporters in ABCB1- or ABCG2-overexpressing cancers. [ABSTRACT FROM AUTHOR]

Varied initial energy efficiency investments would result in different annual energy savings achievements. In order to balance the savings revenue and the potential capital loss through EPC (Energy Performance Contracting), a cost-effective investment decision is needed when selecting energy efficiency technologies. In this research, an approach is developed for the ESCO (Energy Service Company) to evaluate the potential energy savings profit, and thus make the optimal investment decisions. The energy savings revenue under uncertainties, which are derived from energy efficiency performance variation and energy price fluctuation, are first modeled as stochastic processes. Then, the derived energy savings profit is shared by the owner and the ESCO according to the contract specification. A simulation-based model is thus built to maximize the owner's profit, and at the same time, satisfy the ESCO's expected rate of return. In order to demonstrate the applicability of the proposed approach, the University of Maryland campus case is also presented. The proposed method could not only help the ESCO determine the optimal energy efficiency investments, but also assist the owner's decision in the bidding selection. [ABSTRACT FROM AUTHOR]

Abstract: Chinese governments' great efforts in promoting the private sector's provision of public rental housing (PRH) have little effects, and the main reason is lack of proper provision mode and relevant valuation model. This knowledge gap is filled in by proposing a privately-owned PRH provision mode and a real option-based valuation model. In the proposed provision mode, the private sector primarily refers to private real estate developers, who will have ownership and decision rights of PRH projects. Then, the embedded real option at decision making stage is identified, and corresponding option premium (OP) is assessed by the Black–Scholes model modified with value leakage. Adding OP and conventional net present value (NPV) together, the expanded net present value (ENPV) is obtained as the investment value of a privately-owned PRH project. Finally, the proposed provision mode and valuation model are both exemplified in a PRH project, which has a negative NPV indicator but a positive ENPV indicator. Through the sensitive analysis, increasing the average rent of PRH buildings is found to be the most effective measure to enhance the ENPV indicator. Therefore, the proposed PRH provision mode is a feasible way to accelerate the private sector's provision of PRH in China, and the proposed valuation model can value a privately-owned PRH project objectively and comprehensively. [Copyright &y& Elsevier]

Promoting household energy-saving behaviors requires devising effective messages and selecting appropriate message delivery methods. Meanwhile, the specific psychological factors that shape the effectiveness of interventions remains elusive. Based on an energy conservation intervention program carried out in communities in the city of Hangzhou, China in 2016, this paper investigates the impact of eco-feedback and energy-saving tips delivered to households via an instant messaging platform (i.e., WeChat), stickers combined with leaflets, and face-to-face consultations in promoting energy conservation. Psychological attributes including personal values and aspects of decision-making that affect pro-environmental behavior, as well as social and housing demographics were collected and analyzed to identify relationships between household characteristics and the effectiveness of an intervention. The findings suggest that, coupled with eco-feedback, stickers and leaflets (mean rank 37.54) performed better than other alternatives (mean rank 55.34 for WeChat group, 57.08 for consultation group) in encouraging household energy-saving behaviors. The effectiveness of eco-feedback using an instant messaging platform was revealed to be short-lived. Moreover, the study found that psychological features such as the perception of sufficient non-material incentives and opportunity to perform energy conservation behavior, as well as valuing environmental quality, personal health, and leisure time, are significantly correlated with energy savings. The findings not only contribute to future policymaking in demand-side energy conservation, but also provide insights for the cultivation of long-term behavioral changes at the neighborhood level through the effective design of communications and engagement. Image 1 • Compared the effectiveness of three types of delivering methods. • Sticker with eco-feedback is more effective in saving energy than other methods. • Effectiveness of delivering feedback using an instant message is short-lived. • Household psychological and social attributes are correlated with energy saving. • Households valuing environmental quality and health are likely to save energy. [ABSTRACT FROM AUTHOR]

Household electricity consumption influenced by various behavioural intervention strategies is difficult to predict due to the uncertainty that arises from human behaviours and their responses to intervention. Based on an energy conservation experiment conducted in Hangzhou, China, the study aims to develop an improved Support Vector Regression model that is capable of predicting household electricity consumption under multiple intervention strategies. This paper firstly proposes a variable selection approach to determine the best subset of consumption predictors using Akaike Information Criterion. The critical predictors of energy behaviours, personality traits, demographic/building features, weather indicators and the historical monthly consumption is identified in this process. Furthermore, this research also introduces the interaction effect between the energy behaviour and all other predictors to the Support Vector Regression model which applies Gaussian radial basis function optimised by genetic algorithm as the kernel function. More importantly, the proposed model is able to select the optimal intervention strategy and to predict the maximum electricity savings for each household. It enables the households to achieve an average reduction of 12.1% in monthly electricity consumption compared with the conventional behavioural intervention. Moreover, the Monte Carlo simulation is performed to explore the relationship between personality traits, the best-fit intervention strategies and the maximum electricity savings. The study also identifies five types of households with different combinations of extraversion and conscientiousness that respond differently to the optimised interventions. The findings of this study contribute to the residential demand-side energy management by enriching and diversifying personalised behavioural intervention strategies. • The improved SVR model can predict electricity usage under behavioural interventions. • 18 critical predictors including behaviours and personality traits are selected. • WeChat with feedback can lead to the largest electricity savings up to 15.97%. • 5 types of people responding differently to optimal interventions are identified. • The proposed method can achieve an average 12.1% reduction in electricity usage. [ABSTRACT FROM AUTHOR]