A benchmark test suite for evolutionary many-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Open Access journalIn the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary computation. The failure of conventional Pareto-based multi-objective evolutionary algorithms in dealing with MaOPs motivates various new approaches. However, in contrast to the rapid development of algorithm design, performance investigation and comparison of algorithms have received little attention. Several test problem suites which were designed for multi-objective optimization have still been dominantly used in many-objective optimization. In this paper, we carefully select (or modify) 15 test problems with diverse properties to construct a benchmark test suite, aiming to promote the research of evolutionary many-objective optimization (EMaO) via suggesting a set of test problems with a good representation of various real-world scenarios. Also, an open-source software platform with a user-friendly GUI is provided to facilitate the experimental execution and data observation

The dilemma of antibiotic susceptibility and clinical decision-making in a multi-drug-resistant Pseudomonas aeruginosa bloodstream infection

Objective: How to choose the appropriate antibiotics and dosage has always been a difficult issue during the treatment of multi-drug-resistant bacterial infections. Our study aims to resolve this difficulty by introducing our multi-disciplinary treatment (MDT) clinical decision-making scheme based on rigorous interpretation of antibiotic susceptibility tests and precise therapeutic drug monitoring (TDM)-guided dosage adjustment.Method: The treatment course of an elderly patient who developed a multi-drug-resistant Pseudomonas aeruginosa (MDRPA) bloodstream infection from a brain abscess was presented.Results: In the treatment process, ceftazidime–avibactam (CAZ–AVI) was used empirically for treating the infection and clinical symptoms improved. However, the follow-up bacterial susceptibility test showed that the bacteria were resistant to CAZ–AVI. Considering the low fault tolerance of clinical therapy, the treatment was switched to a 1 mg/kg maintenance dose of susceptible polymyxin B, and TDM showed that the AUC24h, ss of 65.5 mgh/L had been achieved. However, clinical symptoms were not improved after 6 days of treatment. Facing the complicated situation, the cooperation of physicians, clinical pharmacologists, and microbiologists was applied, and the treatment finally succeeded with the pathogen eradicated when polymyxin B dose was increased to 1.4 mg/kg, with the AUC24h, ss of 98.6 mgh/L.Conclusion: MDT collaboration on the premise of scientific and standardized drug management is helpful for the recovery process in patients. The empirical judgment of doctors, the medication recommendations from experts in the field of TDM and pharmacokinetics/pharmacodynamics, and the drug susceptibility results provided by the clinical microbiology laboratory all provide the direction of treatment

Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index

Recent genetic association studies have identified 55 genetic loci associated with obesity or body mass index (BMI). The vast majority, 51 loci, however, were identified in European-ancestry populations. We conducted a meta-analysis of associations between BMI and ∼2.5 million genotyped or imputed single nucleotide polymorphisms among 86 757 individuals of Asian ancestry, followed by in silico and de novo replication among 7488–47 352 additional Asian-ancestry individuals. We identified four novel BMI-associated loci near the KCNQ1 (rs2237892, P = 9.29 × 10−13), ALDH2/MYL2 (rs671, P = 3.40 × 10−11; rs12229654, P = 4.56 × 10−9), ITIH4 (rs2535633, P = 1.77 × 10−10) and NT5C2 (rs11191580, P = 3.83 × 10−8) genes. The association of BMI with rs2237892, rs671 and rs12229654 was significantly stronger among men than among women. Of the 51 BMI-associated loci initially identified in European-ancestry populations, we confirmed eight loci at the genome-wide significance level (P < 5.0 × 10−8) and an additional 14 at P < 1.0 × 10−3 with the same direction of effect as reported previously. Findings from this analysis expand our knowledge of the genetic basis of obesity

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Nutritional Programming of the Lifespan of Male Drosophila by Activating FOXO on Larval Low-Nutrient Diet

Nutrition during the developmental stages has long-term effects on adult physiology, disease and lifespan, and is termed nutritional programming. However, the underlying molecular mechanisms of nutritional programming are not yet well understood. In this study, we showed that developmental diets could regulate the lifespan of adult Drosophila in a way that interacts with various adult diets during development and adulthood. Importantly, we demonstrated that a developmental low-yeast diet (0.2SY) extended both the health span and lifespan of male flies under nutrient-replete conditions in adulthood through nutritional programming. Males with a low-yeast diets during developmental stages had a better resistance to starvation and lessened decline of climbing ability with age in adulthood. Critically, we revealed that the activity of the Drosophila transcription factor FOXO (dFOXO) was upregulated in adult males under developmental low-nutrient conditions. The knockdown of dFOXO, with both ubiquitous and fat-body-specific patterns, can completely abolish the lifespan-extending effect from the larval low-yeast diet. Ultimately, we identify that the developmental diet achieved the nutritional programming of the lifespan of adult males by modulating the activity of dFOXO in Drosophila. Together, these results provide molecular evidence that the nutrition in the early life of animals could program the health of their later life and their longevity

Accelerating Large-scale Multi-objective Optimization via Problem Reformulation

In this work, we propose a framework to accelerate the computational efficiency of evolutionary algorithms on largescale multi-objective optimization. The main idea is to track the Pareto optimal set directly via decision space reconstruction. To begin with, the algorithm obtains a set of reference directions in the decision space and associates them with a set of weight variables for locating the Pareto optimal set. Afterwards, the decision space is reconstructed by taking the weight variables and their corresponding solutions as the input and output of the reconstructed optimization problem, respectively. Thanks to the low dimensionality of the weight variables, a set of quasi-optimal solutions can be obtained efficiently. Finally, a multi-objective evolutionary algorithm is used to spread the quasi-optimal solutions over the approximate Pareto optimal front uniformly. Experiments have been conducted on a variety of large-scale problems with 2 or 3 objectives and up to 1000 decision variables. Four different types of well-known algorithms are embedded into the proposed framework and compared with their original versions, respectively. Furthermore, the proposed framework has been compared with two state-of-the-art algorithms for largescale multi-objective optimization. Experimental results have demonstrated the significant improvement benefited from the framework in terms of its performance and computational efficiency in large-scale multi-objective optimization

Accelerating Large-scale Multi-objective Optimization via Problem Reformulation

In this work, we propose a framework to accelerate the computational efficiency of evolutionary algorithms on largescale multi-objective optimization. The main idea is to track the Pareto optimal set directly via decision space reconstruction. To begin with, the algorithm obtains a set of reference directions in the decision space and associates them with a set of weight variables for locating the Pareto optimal set. Afterwards, the decision space is reconstructed by taking the weight variables and their corresponding solutions as the input and output of the reconstructed optimization problem, respectively. Thanks to the low dimensionality of the weight variables, a set of quasi-optimal solutions can be obtained efficiently. Finally, a multi-objective evolutionary algorithm is used to spread the quasi-optimal solutions over the approximate Pareto optimal front uniformly. Experiments have been conducted on a variety of large-scale problems with 2 or 3 objectives and up to 1000 decision variables. Four different types of well-known algorithms are embedded into the proposed framework and compared with their original versions, respectively. Furthermore, the proposed framework has been compared with two state-of-the-art algorithms for largescale multi-objective optimization. Experimental results have demonstrated the significant improvement benefited from the framework in terms of its performance and computational efficiency in large-scale multi-objective optimization

Benchmark Functions for the CEC'2017 Competition on Many-Objective Optimization

In the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary computation. The failure of conventional Pareto-based multi-objective evolutionary algorithms in dealing with MaOPs motivates various new approaches. However, in contrast to the rapid development of algorithm design, performance investigation and comparison of algorithms have received little attention. Several test problem suites which were designed for multi-objective optimization have still been dominantly used in many-objective optimization. In this competition, we carefully selects/designs 15 test problems with diverse properties, aiming to promote the research of evolutionary many-objective optimization (EMaO) via suggesting a set of test problems with a good representation of various real-world scenarios. Also, an open-source software platform with a user-friendly GUI is provided to facilitate the experimental execution and data observation