Improved scaling of the entanglement entropy of quantum antiferromagnetic Heisenberg systems

In this paper, we derive corrections to the subleading logarithmic term of the entanglement entropy in systems with spontaneous broken continuous symmetry. Using quantum Monte Carlo simulations, we show that the improved scaling formula leads to much better estimations of the number of Goldstone modes in the two-dimensional square lattice spin-1/2 Heisenberg model and bilayer spin-1/2 Heisenberg model in systems of rather small sizes, compared with previous results. In addition, the universal geometry-dependent finite constant in the entanglement entropy scaling is also obtained in good agreement with the theoretical value.Comment: 7 pages, 4 figure

Efficacy and Safety of Low-Dose Peginterferon Alpha-2a Plus Ribavirin on Chronic Hepatitis C

Background. The purpose of this study was to assess the efficacy and safety of low-dose peg-IFN α-2a plus ribavirin on the treatment of patients with chronic hepatitis C virus (HCV) infection. Patients and Methods. A total of 243 HCV patients treated with different doses of peg-IFN α-2a plus ribavirin were stratified into three groups. End-of-treatment response (ETR) and sustained viral response (SVR) were evaluated for efficacy. Adverse events and laboratory abnormalities were conducted for safety. Results. ETR and SVR in group I were obtained in 83.9% and 68.9% of the patients, separately, which was similar to groups II (84.1% and 68.3%) and III (81.7% and 66.7%). The received peg-IFN α-2a dose was not the independent factor-related SVR in our population (OR, 1.31; 95% CI, 0.94–1.81; P=0.106). The frequency of no adverse events reported in group III (24.7%) was significantly higher than that in group I (11.5%) and group II (12.7%) (P=0.036). Conclusions. The peg-IFN α-2a 90 μg/week plus ribavirin is as effective as, and better tolerated than, peg-IFN α-2a standard dose with ribavirin in the treatment of chronic hepatitis C. This low-dose combination achieves high SVR rates and may be cost-saving

Single-cell transcriptome sequencing reveals heterogeneity of gastric cancer: progress and prospects

Gastric cancer is one of the most serious malignant tumor and threatens the health of people worldwide. Its heterogeneity leaves many clinical problems unsolved. To treat it effectively, we need to explore its heterogeneity. Single-cell transcriptome sequencing, or single-cell RNA sequencing (scRNA-seq), reveals the complex biological composition and molecular characteristics of gastric cancer at the level of individual cells, which provides a new perspective for understanding the heterogeneity of gastric cancer. In this review, we first introduce the current procedure of scRNA-seq, and discuss the advantages and limitations of scRNA-seq. We then elaborate on the research carried out with scRNA-seq in gastric cancer in recent years, and describe how it reveals cell heterogeneity, the tumor microenvironment, oncogenesis and metastasis, as well as drug response in to gastric cancer, to facilitate early diagnosis, individualized therapy, and prognosis evaluation

Elexacaftor–tezacaftor–ivacaftor for cystic fibrosis with Phe508del mutation: Evidence from randomized controlled trials

Objective: This study aimed to conduct a systematic review and meta-analysis of randomized controlled trials to evaluate the effects of elexacaftor–tezacaftor–ivacaftor (ELX-TEZ-IVA) on patients with cystic fibrosis (CF). Methods: A systematic search was performed in PubMed, Embase, and the Cochrane Library from inception to August 1, 2022. Meta-analysis was conducted using Review Manager 5.3 software. Results: Six studies comprising seven reports involving a total of 1125 CF patients were included. The meta-analyses indicated that ELX-TEZ-IVA significantly improved the percentage predicted forced expiratory volume in 1 s (ppFEV1) by 10.29% (95% confidence interval (CI) (6.44, 14.14), p  < 0.00001) and the CF questionnaire-revised respiratory domain (CFQ-R RD) by 14.59 points (95% CI (9.25, 19.94), p  < 0.00001) compared to placebo, ivacaftor (IVA), or tezacaftor–ivacaftor (TEZ-IVA). In addition, the ELX-TEZ-IVA group showed significantly lower sweat chloride concentrations by 40.30 mmol/L (95% CI (−49.85, −30.74), p  < 0.00001). However, the incidence of adverse events in the ELX-TEZ-IVA group was slightly higher than that in the placebo, IVA, or TEZ-IVA groups. Conclusion: ELX-TEZ-IVA demonstrated efficacy in improving ppFEV1, CFQ-R RD, and sweat chloride concentrations in patients with CF. However, caution should be exercised regarding the incidence of AEs, particularly mild and moderate ones

Reward optimization for content providers with mobile data subsidization: a hierarchical game approach

Mobile data subsidization launched by mobile network operators is a promising business model to provide economic benefits for the mobile data market and beyond. It allows content providers to partly subsidize mobile data consumption of mobile users in exchange for displaying a certain amount of advertisements. From a content provider perspective, it is of great interest to determine the optimal strategy for offering appropriate data subsidization (reward) in order to compete against others to earn more revenue and gain higher profit. In this paper, we take a hierarchical game approach to model the reward optimization process for the content providers. To analyze the relationship between the provider and the user, we first focus on the one-to-one interaction in a single-provider single-user system, and formulate a Mathematical Program with Equilibrium Constraints (MPEC). We apply the backward induction to solve the MPEC problem and prove the existence and uniqueness of the Stackelberg equilibrium. We then formulate an Equilibrium Program with Equilibrium Constraints (EPEC) to characterize the many-to-many interactions among multiple providers and multiple users. Considering the inherent high complexity of the EPEC problem, we utilize the distributed Alternating Direction Method of Multipliers (ADMM) algorithm to obtain the optimum solutions with fast-convergence and decomposition properties of ADMM.Agency for Science, Technology and Research (A*STAR)AI SingaporeEnergy Market Authority (EMA)Ministry of Education (MOE)Nanyang Technological UniversityNational Research Foundation (NRF)The work of Zehui Xiong is supported by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University, Singapore. The work of Jun Zhao is supported by 1) Nanyang Technological University (NTU) Startup Grant, 2) Alibaba-NTU Singapore Joint Research Institute (JRI), 3) Singapore Ministry of Education Academic Research Fund Tier 1 RG128/18, Tier 1 RG115/19, Tier 1 RT07/19, Tier 1 RT01/19, and Tier 2 MOE2019-T2-1-176, 4) NTU-WASP Joint Project, 5) Singapore National Research Foundation (NRF) under its Strategic Capability Research Centres Funding Initiative: Strategic Centre for Research in Privacy-Preserving Technologies & Systems (SCRIPTS), 6) Energy Research Institute @NTU (ERIAN), 7) Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoEDeST-SCI2019-0012, 8) AI Singapore (AISG) 100 Experiments (100E) programme, and 9) NTU Project for Large Vertical Take-Off & Landing (VTOL) Research Platform. The work of Dusit Niyato is supported by the National Research Foundation (NRF), Singapore, under Singapore Energy Market Authority (EMA), Energy Resilience, NRF2017EWT-EP003-041, Singapore NRF2015-NRF-ISF001-2277, Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeST-SCI2019-0007, A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing RGANS1906, Wallenberg AI, Autonomous Systems and Software Programand Nanyang Technological University (WASP/NTU) under grant M4082187 (4080), Singapore Ministry of Education (MOE) Tier 1 (RG16/20), and Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI). The work of Ruilong Deng was supported in part by the National Natural Science Foundation of China under Grant 61873106 and 62061130220, and in part by the Fundamental Research Funds for the Central Universities (Zhejiang University NGICS Platform)