Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Editorial: The psychological process of stereotyping: Content, forming, internalizing, mechanisms, effects, and interventions

Stereotype is a pervasive and persistent human tendency that stems from a basic cognitive need to categorize, simplify, and process the complex world. This tendency is a precondition for social bias, prejudice, and discrimination. Amid the COVID-19 outbreak, the discrimination, exclusion, and even hostility caused by stereotypes have increasingly become an important social issue that concerns political and social stability. Therefore, the current issue focuses on a broad spectrum of research addressing four main themes: (1) the psychological processes involved in forming and internalizing social stereotypes, (2) the negative consequences of stereotypes, (3) the neurocognitive mechanisms underlying stereotypes, and (4) the interventions addressing the consequences of negative stereotypes in this era with changes and challenges. Specifically, the Research Topic consists of 13 papers by 54 scholars that target stereotypes among different social groups, including males and females, older people and young generation, minority races, people living with HIV/AIDS (PLWHA), people with mental health problems, juvenile transgressors, refugees, and Asian-Americans during COVID-19 outbreak. These studies are conducted in culturally diverse countries including Brazil, China, Germany, Hungary, and the USA, contributing to a more holistic picture of contemporary stereotypes. </p

Three dimensional titanium molybdenum nitride nanowire assemblies as highly efficient and durable platinum support for methanol oxidation reaction

To achieve the practical application of direct methanol fuel cells, the development of highly efficient and durable electrocatalyst for methanol oxidation reaction is urgently needed. Herein, an effective approach is developed to fabricate three dimensional porous titanium nitride with an urchin-like structure, which are composed of numerous interactive nanowire assemblies. When being used as the platinum support, the resulting electrocatalyst delivers a mass activity and specific activity that are both around 4.3 times greater with respect to Pt/C catalyst. Moreover, the activity stability and structural durability of the novel catalyst are also confirmed by comprehensive experimental analysis. This study provides a new way for improving the performance of methanol oxidation reaction by combining the advantages of three dimensional structure, inherent electrochemical durability and strong metal support interaction. (C) 2018 Elsevier Ltd. All rights reserved

Multi-Objective Optimization of a Task-Scheduling Algorithm for a Secure Cloud

As more and more power information systems are gradually deployed to cloud servers, the task scheduling of a secure cloud is facing challenges. Optimizing the scheduling strategy only from a single aspect cannot meet the needs of power business. At the same time, the power information system deployed on the security cloud will face different types of business traffic, and each business traffic has different risk levels. However, the existing research has not conducted in-depth research on this aspect, so it is difficult to obtain the optimal scheduling scheme. To solve the above problems, we first build a security cloud task-scheduling model combined with the power information system, and then we define the risk level of business traffic and the objective function of task scheduling. Based on the above, we propose a multi-objective optimization task-scheduling algorithm based on artificial fish swarm algorithm (MOOAFSA). MOOAFSA initializes the fish population through chaotic mapping, which improves the global optimization capability. Moreover, MOOAFSA uses a dynamic step size and field of view, as well as the introduction of adaptive weight factor, which accelerates the convergence and improves optimization accuracy. Finally, MOOAFSA applies crossovers and mutations, which make it easier to jump out of a local optimum. The experimental results show that compared with ant colony (ACO), particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA), MOOAFSA not only significantly accelerates the convergence speed but also reduces the task-completion time, load balancing and execution cost by 15.62&ndash;28.69%, 66.91&ndash;75.62% and 32.37&ndash;41.31%, respectively

Multi-Objective Optimization of a Task-Scheduling Algorithm for a Secure Cloud

As more and more power information systems are gradually deployed to cloud servers, the task scheduling of a secure cloud is facing challenges. Optimizing the scheduling strategy only from a single aspect cannot meet the needs of power business. At the same time, the power information system deployed on the security cloud will face different types of business traffic, and each business traffic has different risk levels. However, the existing research has not conducted in-depth research on this aspect, so it is difficult to obtain the optimal scheduling scheme. To solve the above problems, we first build a security cloud task-scheduling model combined with the power information system, and then we define the risk level of business traffic and the objective function of task scheduling. Based on the above, we propose a multi-objective optimization task-scheduling algorithm based on artificial fish swarm algorithm (MOOAFSA). MOOAFSA initializes the fish population through chaotic mapping, which improves the global optimization capability. Moreover, MOOAFSA uses a dynamic step size and field of view, as well as the introduction of adaptive weight factor, which accelerates the convergence and improves optimization accuracy. Finally, MOOAFSA applies crossovers and mutations, which make it easier to jump out of a local optimum. The experimental results show that compared with ant colony (ACO), particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA), MOOAFSA not only significantly accelerates the convergence speed but also reduces the task-completion time, load balancing and execution cost by 15.62–28.69%, 66.91–75.62% and 32.37–41.31%, respectively

Reliability Assurance Dynamic SSC Placement Using Reinforcement Learning

Software-defined networking (SDN) and network function virtualization (NFV) make a network programmable, resulting in a more flexible and agile network. An important and promising application for these two technologies is network security, where they can dynamically chain virtual security functions (VSFs), such as firewalls, intrusion detection systems, and intrusion prevention systems, and thus inspect, monitor, or filter traffic flows in cloud data center networks. In view of the strict delay constraints of security services and the high failure probability of VSFs, we propose the use of a security service chain (SSC) orchestration algorithm that is latency aware with reliability assurance (LARA). This algorithm includes an SSC orchestration module and VSF backup module. We first use a reinforcement learning (RL) based Q-learning algorithm to achieve efficient SSC orchestration and try to reduce the end-to-end delay of services. Then, we measure the importance of the physical nodes carrying the VSF instance and backup VSF according to the node importance of VSF. Extensive simulation results indicate that the LARA algorithm is more effective in reducing delay and ensuring reliability compared with other algorithms

Expression and Variations in EPAS1 Associated with Oxygen Metabolism in Sheep

Endothelial PAS domain protein 1 gene (EPAS1) is a member of the HIF gene family. This gene encodes a transcription factor subunit that is involved in the induction of oxygen-regulated genes. Several studies have demonstrated that a mutation in EPAS1 could affect oxygen sensing, polycythemia, and hemoglobin level. However, whether EPAS1 mutation affects sheep oxygen metabolism is still unknown. Therefore, we explored the relationship between the variation of EPAS1 and oxygen metabolism in sheep. In this study, variations in ovine EPAS1 exon 15 were investigated in 332 Tibetan sheep and 339 Hu sheep by polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) analysis. In addition, we studied the effect of these variations on blood gas in 176 Tibetan sheep and 231 Hu sheep. Finally, the mRNA expression of EPAS1 in six tissues of Hu sheep and Tibetan sheep living at different altitudes (2500 m, 3500 m, and 4500 m) was analyzed by real-time quantitative PCR (RT-qPCR). Four alleles (A, B, C, and D) were detected, and their distributions highly differed between Tibetan sheep and Hu sheep. In Tibetan sheep, B was the dominant allele, and C and D alleles were rare, whereas all four alleles were common in Hu sheep. Six single nucleotide polymorphisms (SNPs) were identified between the four alleles and one of them was non-synonymous (p.F606L). While studying the blood gas levels in Tibetan sheep and Hu sheep, one variant region was found to be associated with an elevated pO2 and sO2, which suggested that variations in EPAS1 are associated with oxygen metabolism in sheep. RT-qPCR results showed that EPAS1 was expressed in the six tissues of Hu sheep and Tibetan sheep at different altitudes. In addition, the expression of EPAS1 in four tissues (heart, liver, spleen, and longissimus dorsi muscle) of Hu sheep was lower than that in Tibetan sheep from three different altitudes, and the expression of EPAS1 was positively correlated with the altitude. These results indicate that the variations and expression of EPAS1 is closely related to oxygen metabolism

Construction of Naphthalene Diimide Derived Nanostructured Cathodes through Self-Assembly for High-Performance Sodium–Organic Batteries

Organic nanostructured electrodes are very attractive for next-generation sodium-ion batteries. Their great advantages in improved electron and ion transport and more exposed redox-active sites would lead to a higher actual capacity and enhanced rate performance. However, facile and cost-effective methods for the fabrication of nanostructured organic electrodes are still highly challenging and very rare. In this work, we utilize a bioinspired self-assembly strategy to fabricate nanostructured cathodes based on a rationally designed N-hydroxy naphthalene imide sodium salt (NDI-ONa) for high-performance sodium–organic batteries. Such a well-organized nanostructure can greatly enhance both ion and electron transport. When used as cathode for sodium–organic batteries, it provides among the best battery performances, such as high capacity (171 mA h g–1 at 0.05 A g–1), excellent rate performance (153 mA h g–1 at 5.0 A g–1), and ultralong cycling life (93% capacity retention after 20000 cycles at 3.0 A g–1). Even at low temperature or without a conductive additive, it can also perform well. It is believed that self-assembly is a very powerful strategy to construct high-performance nanostructured electrodes

Construction of Naphthalene Diimide Derived Nanostructured Cathodes through Self-Assembly for High-Performance Sodium–Organic Batteries

Organic nanostructured electrodes are very attractive for next-generation sodium-ion batteries. Their great advantages in improved electron and ion transport and more exposed redox-active sites would lead to a higher actual capacity and enhanced rate performance. However, facile and cost-effective methods for the fabrication of nanostructured organic electrodes are still highly challenging and very rare. In this work, we utilize a bioinspired self-assembly strategy to fabricate nanostructured cathodes based on a rationally designed N-hydroxy naphthalene imide sodium salt (NDI-ONa) for high-performance sodium–organic batteries. Such a well-organized nanostructure can greatly enhance both ion and electron transport. When used as cathode for sodium–organic batteries, it provides among the best battery performances, such as high capacity (171 mA h g–1 at 0.05 A g–1), excellent rate performance (153 mA h g–1 at 5.0 A g–1), and ultralong cycling life (93% capacity retention after 20000 cycles at 3.0 A g–1). Even at low temperature or without a conductive additive, it can also perform well. It is believed that self-assembly is a very powerful strategy to construct high-performance nanostructured electrodes