Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery

SARS-CoV-2 is a recently emerged, novel human coronavirus responsible for the currently ongoing COVID-19 pandemic. Recombination is a well-known evolutionary strategy of coronaviruses, which may frequently result in significant genetic alterations, such as deletions throughout the genome. In this study we identified a co-infection with two genetically different SARS-CoV-2 viruses within a single patient sample via amplicon-based next generation sequencing in Hungary. The recessive strain contained an 84 base pair deletion in the receptor binding domain of the spike protein gene and was found to be gradually displaced by a dominant non-deleterious variant over-time. We have identified the region of the receptor-binding domain (RBD) that is affected by the mutation, created homology models of the RBDΔ84 mutant, and based on the available experimental data and calculations, we propose that the mutation has a deteriorating effect on the binding of RBD to the angiotensin-converting enzyme 2 (ACE2) receptor, which results in the negative selection of this variant. Extending the sequencing capacity toward the discovery of emerging recombinant or deleterious strains may facilitate the early recognition of novel strains with altered phenotypic attributes and understanding of key elements of spike protein evolution. Such studies may greatly contribute to future therapeutic research and general understanding of genomic processes of the virus.

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Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽

10.1093/genetics/165.4.2269 ◽

2003 ◽

Vol 165 (4) ◽

pp. 2269-2282

Author(s):

D Mester ◽

Y Ronin ◽

D Minkov ◽

E Nevo ◽

A Korol

Keyword(s):

Discrete Optimization ◽

High Performance ◽

Large Scale ◽

Simulated Data ◽

Real Data ◽

Genetic Maps ◽

Chromosome 1 ◽

Evolutionary Strategy ◽

Group A ◽

The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

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Estimating a Stoichiometric Solid’s Gibbs Free Energy Model by Means of a Constrained Evolutionary Strategy

Materials ◽

10.3390/ma14020471 ◽

2021 ◽

Vol 14 (2) ◽

pp. 471

Author(s):

Constantino Grau Turuelo ◽

Sebastian Pinnau ◽

Cornelia Breitkopf

Keyword(s):

Heat Capacity ◽

Free Energy ◽

Evolutionary Strategy ◽

Data Sets ◽

Automatic Data ◽

Physical Constraints ◽

Stoichiometric Model ◽

Covariance Matrix Adaptation ◽

Dsc Measurements ◽

Capacity Data

Modeling of thermodynamic properties, like heat capacities for stoichiometric solids, includes the treatment of different sources of data which may be inconsistent and diverse. In this work, an approach based on the covariance matrix adaptation evolution strategy (CMA-ES) is proposed and described as an alternative method for data treatment and fitting with the support of data source dependent weight factors and physical constraints. This is applied to a Gibb’s Free Energy stoichiometric model for different magnesium sulfate hydrates by means of the NASA9 polynomial. Its behavior is proved by: (i) The comparison of the model to other standard methods for different heat capacity data, yielding a more plausible curve at high temperature ranges; (ii) the comparison of the fitted heat capacity values of MgSO4·7H2O against DSC measurements, resulting in a mean relative error of a 0.7% and a normalized root mean square deviation of 1.1%; and (iii) comparing the Van’t Hoff and proposed Stoichiometric model vapor-solid equilibrium curves to different literature data for MgSO4·7H2O, MgSO4·6H2O, and MgSO4·1H2O, resulting in similar equilibrium values, especially for MgSO4·7H2O and MgSO4·6H2O. The results show good agreement with the employed data and confirm this method as a viable alternative for fitting complex physically constrained data sets, while being a potential approach for automatic data fitting of substance data.

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Feeding diverse prey as an excellent strategy of mixotrophic dinoflagellates for global dominance

Science Advances ◽

10.1126/sciadv.abe4214 ◽

2021 ◽

Vol 7 (2) ◽

pp. eabe4214

Author(s):

Hae Jin Jeong ◽

Hee Chang Kang ◽

An Suk Lim ◽

Se Hyeon Jang ◽

Kitack Lee ◽

...

Keyword(s):

Genetic Factor ◽

Red Tide ◽

Biogeochemical Cycles ◽

Evolutionary Strategy ◽

Distribution Data ◽

Red Tides ◽

Trade Off ◽

Moderate Growth ◽

Prey Diversity ◽

Standing Question

Microalgae fuel food webs and biogeochemical cycles of key elements in the ocean. What determines microalgal dominance in the ocean is a long-standing question. Red tide distribution data (spanning 1990 to 2019) show that mixotrophic dinoflagellates, capable of photosynthesis and predation together, were responsible for ~40% of the species forming red tides globally. Counterintuitively, the species with low or moderate growth rates but diverse prey including diatoms caused red tides globally. The ability of these dinoflagellates to trade off growth for prey diversity is another genetic factor critical to formation of red tides across diverse ocean conditions. This finding has profound implications for explaining the global dominance of particular microalgae, their key eco-evolutionary strategy, and prediction of harmful red tide outbreaks.

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Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

Sustainability ◽

10.3390/su13063198 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3198

Author(s):

Hossein Moayedi ◽

Amir Mosavi

Keyword(s):

Residential Buildings ◽

Optimization Technique ◽

Hybrid Algorithms ◽

Evolutionary Strategy ◽

Multi Layer Perceptron ◽

Invasive Weed ◽

Dragonfly Algorithm ◽

Ant Lion Optimization ◽

Heating Load ◽

Ant Lion

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

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