Current Development of in Vitro and in Vivo Methods for Predicting Glycemic Indexes of Carbohydrate Foods

Glycemic index (GI) is a key indicator for evaluating the postprandial glycemic response to carbohydrate foods. A low GI diet can not only help to control appetite and delay hunger, but also benefit weight control and improve glucose and lipid levels in diabetic patients. The development of low GI foods has thus become a hotspot in current food research. At present, the international standard ISO 26642:2010, issued by the International Standards Organization (ISO), is the gold standard for measuring the GI values of foods using human subjects around the world. However, human testing has some disadvantages, such as individual differences may lead to significantly different results, even for the same foods, and it is costly and time consuming, and should be ethical, and it is unsuitable for high-throughput testing of food GI values. For this reason, researchers have successively developed various in vitro models to predict food GI values. This article focuses on reviewing the current in vitro and in vivo methods for predicting the GI values of foods, with a particular focus on their advantages and disadvantages, as well as their future developments. The current paper is aimed to provide new ideas for the development and promotion of low GI foods

Publisher Correction: Legume rhizodeposition promotes nitrogen fixation by soil microbiota under crop diversification

Correction to: Nature Communicationshttps://doi.org/10.1038/s41467-024-47159-x, published online 04 April 2024 In this article the affiliation ‘State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China’ for Xinhua Peng was missing. The original article has been corrected

Composition and distribution of fish species collected during the fourth Chinese National Arctic Research Expedition in 2010

There are awareness and concerns caused by the decreasing sea ice coverage around the Arctic and Antarctic due to effects of climate change. Emphasis in this study was on rapid changes in Arctic sea ice coverage and its impacts on the marine ecology during the fourth Chinese National Arctic Research Expedition in 2010. Our purpose was to establish a baseline of Arctic fish compositions, and consequent effects of climate change on the fish community and biogeography. Fish specimens were collected using a multinet middle-water trawl, French-type beam trawl, otter trawl, and triangular bottom trawl. In total, 36 tows were carried out along the shelf of the Bering Sea, Bering Strait, and Chukchi Sea in the Arctic Ocean. In total, 41 fish species belonging to 14 families in 7 orders were collected during the expedition. Among them, the Scorpaeniformes, including 17 species, accounted for almost one third of the total number (34.8%), followed by 14 species of the Perciformes (27.0%), 5 species of the Pleuronectiformes(22.3%), and 2 species of the Gadiformes (15.4%). The top 6 most abundant species were Hippoglossoides robustus, Boregadus saida, Myoxocephalus scorpius, Lumpenus fabricii, Artediellus scaber, and Gymnocanthus tricuspis. Abundant species varied according to the different fishing methods; numbers of families and species recorded did not differ with the various fishing methods; species and abundances decreased with depth and latitude; and species extending over their known geographic ranges were observed during the expedition. Station information, species list, and color photographs of all fishes are provided

Intelligent Online Path Planning for UAVs in Adversarial Environments

Online path planning (OPP) for unmanned aerial vehicles (UAVs) is a basic issue of intelligent flight and is indeed a dynamic multi-objective optimization problem (DMOP). In this paper, an OPP framework is proposed in the sense of model predictive control (MPC) to continuously update the environmental information for the planner. For solving the DMOP involved in the MPC we propose a dynamic multi-objective evolutionary algorithm based on linkage and prediction (LP-DMOEA). Within this algorithm, the historical Pareto sets are collected and analysed to enhance the performance. For intelligently selecting the best path from the output of the OPP, the Bayesian network and fuzzy logic are used to quantify the bias to each optimization objective. The DMOEA is validated on three benchmark problems characterized by different changing types in decision and objective spaces. Moreover, the simulation results show that the LP-DMOEA overcomes the restart method for OPP. The decision-making method for solution selection can assess the situation in an adversarial environment and accordingly adapt the path planner

Enhancing Cooperative Coevolution with Selective Multiple Populations for Large-Scale Global Optimization

The cooperative coevolution (CC) algorithm features a “divide-and-conquer” problem-solving process. This feature has great potential for large-scale global optimization (LSGO) while inducing some inherent problems of CC if a problem is improperly decomposed. In this work, a novel CC named selective multiple population- (SMP-) based CC (CC-SMP) is proposed to enhance the cooperation of subproblems by addressing two challenges: finding informative collaborators whose fitness and diversity are qualified and adapting to the dynamic landscape. In particular, a CMA-ES-based multipopulation procedure is employed to identify local optima which are then shared as potential informative collaborators. A restart-after-stagnation procedure is incorporated to help the child populations adapt to the dynamic landscape. A biobjective selection is also incorporated to select qualified child populations according to the criteria of informative individuals (fitness and diversity). Only selected child populations are active in the next evolutionary cycle while the others are frozen to save computing resource. In the experimental study, the proposed CC-SMP is compared to 7 state-of-the-art CC algorithms on 20 benchmark functions with 1000 dimensionality. Statistical comparison results figure out significant superiority of the CC-SMP. In addition, behavior of the SMP scheme and sensitivity to the cooperation frequency are also analyzed

A Bilateral Cooperative Strategy for Swarm Escort under the Attack of Aggressive Swarms

With the development of swarm intelligence and low-cost unmanned systems, the offence and defense of a swarm have become essential issues in defense and security technologies. A swarm of drones can be used to attack some high-value units (HVUs), such as bases or fuel tanks. Moreover, some moving HVUs such as cargo ships are also greatly threatened when attacked by a swarm of unmanned surface vehicles. A promising approach to protect a HVU from the attack of an aggressive swarm is to use another low-cost swarm. However, escorting a HVU with a swarm is challenging since defenders must respond to attacks and carry out escorts in a noncentralized manner. It is difficult to balance the above tasks well using the unilateral escort strategy adopted by defenders in previous studies. Therefore, this paper proposes a bilateral cooperative strategy for the swarm escort problem under the attack of aggressive swarms. In this bilateral cooperative strategy, the HVU adaptively select different evasion strategies by inferring the threat level according to the spatial distributions of the defenders and attackers. Meanwhile, the defenders of the swarm take a noncentralized escort algorithm by moving around the HVU in a dual-layer formation. Within each layer, the defenders cluster into several uniformly distributed subswarms to counteract the attackers. Numerical simulations are conducted using different aggressive swarm models to demonstrate the effectiveness of the proposed bilateral cooperative strategy

A Bilateral Cooperative Strategy for Swarm Escort under the Attack of Aggressive Swarms

With the development of swarm intelligence and low-cost unmanned systems, the offence and defense of a swarm have become essential issues in defense and security technologies. A swarm of drones can be used to attack some high-value units (HVUs), such as bases or fuel tanks. Moreover, some moving HVUs such as cargo ships are also greatly threatened when attacked by a swarm of unmanned surface vehicles. A promising approach to protect a HVU from the attack of an aggressive swarm is to use another low-cost swarm. However, escorting a HVU with a swarm is challenging since defenders must respond to attacks and carry out escorts in a noncentralized manner. It is difficult to balance the above tasks well using the unilateral escort strategy adopted by defenders in previous studies. Therefore, this paper proposes a bilateral cooperative strategy for the swarm escort problem under the attack of aggressive swarms. In this bilateral cooperative strategy, the HVU adaptively select different evasion strategies by inferring the threat level according to the spatial distributions of the defenders and attackers. Meanwhile, the defenders of the swarm take a noncentralized escort algorithm by moving around the HVU in a dual-layer formation. Within each layer, the defenders cluster into several uniformly distributed subswarms to counteract the attackers. Numerical simulations are conducted using different aggressive swarm models to demonstrate the effectiveness of the proposed bilateral cooperative strategy

Multimodal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization

Cooperative coevolutionary algorithms decompose a problem into several subcomponents and optimize them separately. Such a divide-and-conquer strategy makes cooperative coevolutionary algorithms potentially well suited for large-scale optimization. However, decomposition may be inaccurate, resulting in a wrong division of the interacting decision variables into different subcomponents and thereby a loss of important information about the topology of the overall fitness landscape. In this paper, we suggest an idea that concurrently searches for multiple optima and uses them as informative representatives to be exchanged among subcomponents for compensation. To this end, we incorporate a multi-modal optimization procedure into each subcomponent, which is adaptively triggered by the status of subcomponent optimizers. In addition, a non-dominance based selection scheme is proposed to adaptively select one complete solution for evaluation from the ones that constructed by combining informative representatives from each subcomponent with a given solution. The performance of the proposed algorithm has been demonstrated by comparing five popular cooperative coevolutionary algorithms on a set of selected problems that are recognized to be hard for traditional cooperative coevolutionary algorithms. The superior performance of the proposed algorithm is further confirmed by a comprehensive study that compares 17 state-of-the-art cooperative coevolutionary algorithms and other metaheuristic algorithms on 20 1000-dimensional benchmark functions