High expression of NR1D1 is associated with good prognosis in triple-negative breast cancer patients treated with chemotherapy

Background Nuclear receptor subfamily 1, group D, member 1 (NR1D1) is a ligand-regulated nuclear receptor and transcriptional factor. Although recent studies have implicated NR1D1 as a regulator of DNA repair and proliferation in breast cancers, its potential as a therapeutic target for breast cancer has not been assessed in terms of clinical outcomes. Thus, this study aims to analyze NR1D1 expression in breast cancer patients and to evaluate its potential prognostic value. Methods NR1D1 expression was analyzed by immunohistochemistry using an anti-NR1D1 antibody in 694 breast cancer samples. Survival analyses were performed using the Kaplan–Meier method with the log-rank test to investigate the association of NR1D1 expression with clinical outcome. Results One hundred thirty-nine of these samples exhibited high NR1D1 expression, mostly in the nucleus of breast cancer cells. NR1D1 expression correlated significantly with histological grade and estrogen receptor status. Overall survival (OS) and disease-free survival (DFS) did not correlate significantly with NR1D1 expression in breast cancer patients regardless of whether they had received chemotherapy. Subgroup analysis performed according to molecular subtype of breast cancer showed a significant influence of high NR1D1 expression on OS (P = 0.002) and DFS (P = 0.007) in patients with triple-negative breast cancer (TNBC) treated with chemotherapy. Conclusions High NR1D1 expression level had a favorable impact on OS and DFS in patients with TNBC treated with chemotherapy. NR1D1 should be investigated further as a possible prognostic marker in TNBC patients receiving chemotherapeutic treatment and as a target in the development of chemotherapeutic approaches to treating TNBC.This work was supported by grants from the National Research Foundation of Korea (2014M3A9D5A01073556, 2017R1A2B3011870, 2018R1A5A2024425, and BK21 Plus program)

Optimal Scheduling for Electric Vehicle Charging under Variable Maximum Charging Power

The large-scale integration of electric vehicles (EVs) into power systems is expected to lead to challenges in the operation of the charging infrastructure. In this paper, we deal with the problem of an aggregator coordinating charging schedules of EVs with the objective of minimizing the total charging cost. In particular, unlike most previous studies, which assumed constant maximum charging power, we assume that the maximum charging power can vary according to the current state of charge (SOC). Under this assumption, we propose two charging schemes, namely non-preemptive and preemptive charging. The difference between these two is whether interruptions during the charging process are allowed or not. We formulate the EV charging-scheduling problem for each scheme and propose a formulation that can prevent frequent interruptions. Our numerical simulations compare different charging schemes and demonstrate that preemptive charging with limited interruptions is an attractive alternative in terms of both cost and practicality. We also show that the proposed formulations can be applied in practice to solve large-scale charging-scheduling problems

The impacts of transmission topology control on the European electricity network

The EU is targeting a 20% share of energy from renewable resources by 2020 and this increase is in turn expected to lead to operational challenges that will require various congestion management actions by system operators. In this paper, we deal with topology control of the transmission network as a congestion management resource and evaluate the impacts of topology control on the European electricity network. To do this, we co-optimize unit commitment and transmission switching over 24 hours and we use a decomposition scheme to tackle the resulting large-scale problem. Our analysis is conducted on different scenarios of load and renewable power generation. We find that topology control results in significant cost savings within Europe which tend to be inversely related to net load. The robustness of our results is supported by an extensive sensitivity analysis

Congestion management through topological corrections: A case study of Central Western Europe

The integration of an increasing amount of renewable generation within Europe is posing operational challenges that require various balancing actions. System operators therefore need to rely increasingly on the active control of the transmission network. Transmission topology control is a fast and economical option to add flexibility to the transmission system. We model the current methodology for controlling congestion in the Central Western European (CWE) market and quantify the benefits of topology control. We also compare the results with a nodal pricing model. Our computational results suggest that topology control can significantly reduce congestion management costs under the current market coupling regime whereas the benefits of topology control are limited under nodal pricing. Topology control emerges as an attractive and implementable means of managing congestion as it provides a significant percentage of the cost savings that would be achieved by overhauling the existing European market design and shifting to a nodal pricing regim

Multi-Period Planning of Hydrogen Supply Network for Refuelling Hydrogen Fuel Cell Vehicles in Urban Areas

The hydrogen economy refers to an economic and industrial structure that uses hydrogen as its main energy source, replacing traditional fossil-fuel-based energy systems. In particular, the widespread adoption of hydrogen fuel cell vehicles (HFCVs) is one of the key factors enabling a hydrogen economy, and aggressive investment in hydrogen refuelling infrastructure is essential to make large-scale adoption of HFCVs possible. In this study, we address the problem of effectively designing a hydrogen supply network for refuelling HFCVs in urban areas relatively far from a large hydrogen production site, such as a petrochemical complex. In these urban areas where mass supply of hydrogen is not possible, hydrogen can be supplied by reforming city gas. In this case, building distributed hydrogen production bases that extract large amounts of hydrogen from liquefied petroleum gas (LPG) or compressed natural gas (CNG) and then supply hydrogen to nearby hydrogen stations may be a cost-effective option for establishing a hydrogen refuelling infrastructure in the early stage of the hydrogen economy. Therefore, an optimization model is proposed for effectively deciding when and where to build hydrogen production bases and hydrogen refuelling stations in an urban area. Then, a case study of the southeastern area of Seoul, known as a commercial and residential center, is discussed. A variety of scenarios for the design parameters of the hydrogen supply network are analyzed based on the target of the adoption of HFCVs in Seoul by 2030. The proposed optimization model can be effectively used for determining the time and sites for building hydrogen production bases and hydrogen refuelling stations

Optimal Scheduling for Electric Vehicle Charging under Variable Maximum Charging Power

The large-scale integration of electric vehicles (EVs) into power systems is expected to lead to challenges in the operation of the charging infrastructure. In this paper, we deal with the problem of an aggregator coordinating charging schedules of EVs with the objective of minimizing the total charging cost. In particular, unlike most previous studies, which assumed constant maximum charging power, we assume that the maximum charging power can vary according to the current state of charge (SOC). Under this assumption, we propose two charging schemes, namely non-preemptive and preemptive charging. The difference between these two is whether interruptions during the charging process are allowed or not. We formulate the EV charging-scheduling problem for each scheme and propose a formulation that can prevent frequent interruptions. Our numerical simulations compare different charging schemes and demonstrate that preemptive charging with limited interruptions is an attractive alternative in terms of both cost and practicality. We also show that the proposed formulations can be applied in practice to solve large-scale charging-scheduling problems

Parallel Machine Scheduling with Generalized Precedence Relations

International audienc

A Robust Scenario Approach for the Vehicle Routing Problem with Uncertain Travel Times

We consider a vehicle routing problem with uncertain travel times in which a penalty is incurred for each vehicle that exceeds a given time limit. A traditional stochastic programming approach would require precise knowledge of the underlying probability distributions of random data. In a novel approach presented here, we assume that only rough information on future travel times is available, leading to the multiple range forecasts of travel times and the probabilities of each range being realized. In this setting, we replace the point estimates of travel times on a scenario by range estimates. For each scenario, we then find the robust routes that protect the solution against the worst case within the given ranges, and finally we find the routes with the minimum expected cost. We propose a branch-and-cut algorithm to solve the problem and report computational results on both randomly generated and the well-known Solomon's instances. The results demonstrate that our approach is a favorable one when exact information of probability distributions is not available