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This study investigates if the Japan-Korea-Marker (JKM) price is a feasible benchmark to replace the oil indexation in Asian liquified natural gas (LNG) trade. For this purpose, we propose an equilibrium pricing model for the LNG long-term contract (LTC) in Asia. Our model incorporates the risk-averse importer and exporter who optimize their risk-profit tradeoffs by deciding their LTC-spot trade portfolios. Using the model, we compare the pricing efficiency and the risk-profit tradeoff of an importer/exporter under different benchmarks (oil price versus JKM price). The results show that the JKM price is more efficient as an LTC pricing benchmark than the oil price. The JKM pricing benchmark is favored for both exporters and importers when they are low risk-aversion. In addition, we compare the performance of the JKM benchmark based on the CIF price term (i.e., the importer pays for freight charges) with that based on the FOB price term (i.e., the exporter pays for freight charges). We find that the freight liability has little effect on the pricing efficiency of the JKM benchmark. • We propose an equilibrium model for Asian LNG long-term contract pricing. • We apply the model to investigate the feasibility of different pricing benchmarks. • JKM price is more efficient than the oil price in pricing long-term LNG contracts. • Oil-indexed LTC can spread the risks of importers when oil-gas price decoupling. [ABSTRACT FROM AUTHOR]

The lotus (Nelumbo nucifera) is one of the most popular aquatic plants in Asia, and has emerged as a novel model for studying flower and rhizome development, and primary and secondary metabolite accumulation. Here, we developed a highly efficient callus induction system for the lotus by optimizing a series of key factors that affect callus formation. The highest efficient callus production was induced on immature cotyledon and embryo explants grown on Murashige and Skoog (MS) basal medium containing an optimized combination of 3 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D) and 0.5 mg/L 6-benzylaminopurine (6-BA). In addition, lotus callus induction was proven to be influenced by lotus genotypes, light conditions, the developmental stages of explants and the time of explant sampling. Collecting immature cotyledons from seeds of the genotype "Shilihe 1", at 9 days post pollination, and to culture the explants in darkness, are proposed as the optimum conditions for lotus callus induction. Interestingly, highly efficient callus induction was also observed in explants of immature embryo derived aseptic seedlings; and a small amount of lotus benzylisoquinoline alkaloid (BIA) and obvious expression of BIA biosynthetic genes were detected in lotus callus. [ABSTRACT FROM AUTHOR]

• Establish a shipping profit model to investigate the economic feasibility of LNG-fueled ship via NSR. • Calculate the CO 2 emission reduction of LNG-fueled ship based on its economic feasibility. • Compare LNG-fueled ship via NSR with other two options, considering Sabetta as an LNG refueling center. • Propose scenarios considering the variation of factors to reflect the different circumstances in the real world. Global warming allows the Northern Sea Route (NSR) to be used as a potential alternative for Asia–Europe shipping. As a clean fuel, liquid natural gas (LNG) has been increasingly used as a marine fuel. This study aims to analyse the economic feasibility and CO2 emission reduction of using LNG-fuelled container ships to sail through the NSR, under the assumption that Sabetta will be developed into an LNG refuelling centre. We establish a shipping profit model and a CO2 emission model and then apply real data to them. Several scenarios are proposed to reflect the different circumstances in practice. We find that a shorter round-trip transit time and appropriate ship size are the most favourable factors for the proposed option. Although data suggests that it is often economically infeasible to deploy LNG-fuelled ships via the NSR, under certain circumstances where it is indeed cost-effective, considerable CO2 emission reduction can be achieved. [ABSTRACT FROM AUTHOR]

Objectives: This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-FFR)., Background: CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated., Methods: A total of 482 vessels from 314 patients (age 62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and ≥400) on a per-vessel level with invasive FFR as the reference standard., Results: The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57 to 0.85] vs. 0.85 [95% CI: 0.82 to 0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC ≥ 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001)., Conclusions: Machine-learning-based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621)., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease., Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent ( R =0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; P <0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%-63%) by CTA to 78% (75%-82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%-76%) by CTA to 85% (81%-89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR., Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR., (© 2018 American Heart Association, Inc.)