The effectiveness of China’s regional carbon market pilots in reducing firm emissions

China has implemented an emission trading system (ETS) to reduce its ever-increasing greenhouse gas emissions while maintaining rapid economic growth. With low carbon prices and infrequent allowance trading, whether China’s ETS is an effective approach for climate mitigation has entered the center of the policy and research debate. Utilizing China’s regional ETS pilots as a quasi-natural experiment, we provide a comprehensive assessment of the effects of ETS on firm carbon emissions and economic outcomes by means of a matched difference-in-differences (DID) approach. The empirical analysis is based on a unique panel dataset of firm tax records in the manufacturing and public utility sectors during 2009 to 2015. We show unambiguous evidence that the regional ETS pilots are effective in reducing firm emissions, leading to a 16.7% reduction in total emissions and a 9.7% reduction in emission intensity. Regulated firms achieve emission abatement through conserving energy consumption and switching to low-carbon fuels. The economic consequences of the ETS are mixed. On one hand, the ETS has a negative impact on employment and capital input; on the other hand, the ETS incentivizes regulated firms to improve productivity. In the aggregate, the ETS does not exhibit statistically significant effects on output and export. We also find that the ETS displays notable heterogeneity across pilots. Mass-based allowance allocation rules, higher carbon prices, and active allowance trading contribute to more pronounced effects in emission abatement

A novel genetic map of wheat: utility for mapping QTL for yield under different nitrogen treatments

BACKGROUND: Common wheat (Triticum aestivum L.) is one of the most important food crops worldwide. Wheat varieties that maintain yield (YD) under moderate or even intense nitrogen (N) deficiency can adapt to low input management systems. A detailed genetic map is necessary for both wheat molecular breeding and genomics research. In this study, an F(6:7) recombinant inbred line population comprising 188 lines was used to construct a novel genetic map and subsequently to detect quantitative trait loci (QTL) for YD and response to N stress. RESULTS: A genetic map consisting of 591 loci distributed across 21 wheat chromosomes was constructed. The map spanned 3930.7 cM, with one marker per 6.7 cM on average. Genomic simple sequence repeat (g-SSR), expressed sequence tag-derived microsatellite (e-SSR), diversity arrays technology (DArT), sequence-tagged sites (STS), sequence-related amplified polymorphism (SRAP), and inter-simple sequence repeat (ISSR) molecular markers were included in the map. The linear relationships between loci found in the present map and in previously compiled physical maps were presented, which were generally in accordance. Information on the genetic and physical positions and allele sizes (when possible) of 17 DArT, 50 e-SSR, 44 SRAP, five ISSR, and two morphological markers is reported here for the first time. Seven segregation distortion regions (SDR) were identified on chromosomes 1B, 3BL, 4AL, 6AS, 6AL, 6BL, and 7B. A total of 22 and 12 QTLs for YD and yield difference between the value (YDDV) under HN and the value under LN were identified, respectively. Of these, QYd-4B-2 and QYddv-4B, two major stable QTL, shared support interval with alleles from KN9204 increasing YD in LN and decreasing YDDV. We probe into the use of these QTLs in wheat breeding programs. Moreover, factors affecting the SDR and total map length are discussed in depth. CONCLUSIONS: This novel map may facilitate the use of novel markers in wheat molecular breeding programs and genomics research. Moreover, QTLs for YD and YDDV provide useful markers for wheat molecular breeding programs designed to increase yield potential under N stress

Nrf2 deletion causes “benign” simple steatosis to develop into nonalcoholic steatohepatitis in mice fed a high-fat diet

BACKGROUND: Nonalcoholic fatty liver disease begins with the aberrant accumulation of triglyceride in the liver. Its spectrum includes the earliest stage of hepatic simple steatosis (SS), nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Generally, hepatic SS is often self-limited; however 10%-30% of patients with hepatic SS progress to NASH. The cause(s) of the transition from SS to NASH are unclear. We aimed to test the contribution of nuclear erythroid 2-related factor 2 (Nrf2) on the progression of “benign” SS to NASH in mice fed a high fat diet. In doing so, we discovered the influence of fatty acid in that progression. METHOD: The involvement of Nrf2 in defending against the development of NASH was studied in an experimental model induced by a high-fat diet. Wild-type and Nrf2-null mice were fed the diet. Their specimens were analyzed for pathology as well as for fatty acid content and ratios. RESULT: In feeding the high-fat diet to the Wild-type and the Nrf2-null mice, the Wild-type mice increased hepatic fat deposition without inflammation or fibrosis (i.e., simple steatosis), while the Nrf2-null mice had significantly more hepatic steatosis and substantial inflammation, (i.e., nonalcoholic steatohepatitis). In addition, as a result of the high-fat diet, SFA (C20: 0, C22: 0) and MUFA (C18: 1, C20: 1) content in Nrf2-null mice were significantly higher than in Wild-type mice. In the Nrf2-null mice the PUFA/TFA ratio decreased; conversely, the MUFA/TFA ratio increased. CONCLUSION: The deletion of Nrf2 causes “benign” SS to develop into NASH in mice fed with a high-fat diet, through prompt fatty acid accumulation and disruption of hepatic fatty acid composition in the liver

Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy

Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phases as the training data, we perform deformable image registration between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, we can generate new DVFs, which, when applied on the reference image, lead to new volumetric images. We then can reconstruct a volumetric image from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. Our algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. We generated the training data using a realistic and dynamic mathematical phantom with 10 breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. Results: The average relative image intensity error of the reconstructed volumetric images is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 mm +/- 0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 seconds (range: 0.17 and 0.35 seconds). Conclusions: We have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.Comment: 8 pages, 3 figures, submitted to Medical Physics Lette

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

Recently we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency were then evaluated on 1) a digital respiratory phantom, 2) a physical respiratory phantom, and 3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset. For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 seconds, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 seconds on the same GPU card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 seconds

Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost

Electrochemical reforming of ethanol with acetate Co-Production on nickel cobalt selenide nanoparticles

The energy efficiency of water electrolysis is limited by the sluggish reaction kinetics of the anodic oxygen evolution reaction (OER). To overcome this limitation, OER can be replaced by a less demanding oxidation reaction, which in the ideal scenario could be even used to generate additional valuable chemicals. Herein, we focus on the electrochemical reforming of ethanol in alkaline media to generate hydrogen at a Pt cathode and acetate as a co-product at a NiCoSe anode. We first detail the solution synthesis of a series of NiCoSe electrocatalysts. By adjusting the Ni/Co ratio, the electrocatalytic activity and selectivity for the production of acetate from ethanol are optimized. Best performances are obtained at low substitutions of Ni by Co in the cubic NiSe phase. Density function theory reveals that the Co substitution can effectively enhance the ethanol adsorption and decrease the energy barrier for its first step dehydrogenation during its conversion to acetate. However, we experimentally observe that too large amounts of Co decrease the ethanol-to-acetate Faradaic efficiency from values above 90% to just 50 %. At the optimized composition, the NiCoSe electrode delivers a stable chronoamperometry current density of up to 45 mA cm, corresponding to 1.2 A g, in a 1 M KOH + 1 M ethanol solution, with a high ethanol-to-acetate Faradaic efficiency of 82.2% at a relatively low potential, 1.50 V vs. RHE, and with an acetate production rate of 0.34 mmol cm h.This work was supported by the start-up funding at Chengdu University. It was also supported by the European Regional Development Funds and by the Spanish Ministerio de Economía y Competitividad through the project SEHTOP (ENE2016-77798-C4-3-R), MCIN/ AEI/10.13039/501100011033/ project, and NANOGEN (PID2020-116093RB-C43). X. Wang, C. Xing, X. Han, R. He, Z. Liang, and Y. Zhang are grateful for the scholarship from China Scholarship Council (CSC). X. Han and J. Arbiol acknowledge funding from Generalitat de Catalunya 2017 SGR 327. ICN2 acknowledges support from the Severo Ochoa Programme (MINECO, Grant no. SEV-2013-0295). IREC and ICN2 are funded by the CERCA Programme / Generalitat de Catalunya