Evolution Of Population Research Articles

Contrasting patterns of spatial genetic structure in endangered southern damselfly (Coenagrion mercuriale) populations facing habitat fragmentation and urbanisation

AbstractAimHuman‐induced environmental changes result in habitat loss and fragmentation, impacting wildlife population genetic structure and evolution. Urbanised and geographically peripheral areas often represent unfavourable environments, reducing connectivity among populations and causing higher population genetic differentiation and lower intra‐population genetic diversity. We examined how geographic peripherality and anthropogenic pressures affect genetic diversity and genetic differentiation in the protected southern damselfly (Coenagrion mercuriale, Odonata), which has low dispersal capabilities and specific habitat requirements and whose populations are declining.LocationWe studied two areas: one in semi‐natural habitats at the periphery of the species geographic range (northern France) and the other more central to the species' range, in an urbanised area surrounding the city of Strasbourg (Alsace, eastern France).MethodsWe genotyped 2743 individuals from 128 populations using 11 microsatellite loci. We analysed the spatial distribution of neutral genetic diversity (allelic richness, heterozygosity, levels of inbreeding and genetic relatedness), the extent of genetic differentiation and population affiliations (sPCAs) within the two areas. We also examined fine‐scale patterns of gene flow in the urbanised area of Alsace by investigating patterns of isolation by distance and estimating effective migration surfaces (EEMS) method.ResultsNorthern peripheral populations showed lower levels of genetic diversity and higher levels of genetic differentiation than central Alsacian populations. Although located in anthropised habitats, geographically central Alsacian populations showed high levels of gene flow, with dispersal events mainly occurring overland and not restricted to watercourses. However, the highly urbanised city of Strasbourg negatively impacted nearby populations by reducing levels of genetic diversity and increasing population genetic differentiation.Main ConclusionsThese results showed the need for management action by restoring breeding sites and creating migratory corridors for peripheral southern damselfly populations. However, our results also highlighted the resilience of southern damselfly in central range populations facing strong urbanisation pressures.

Multi-objective optimization algorithm for multi-workflow computation offloading in resource-limited IIoT

Industrial internet of things (IIoT) connects traditional industrial devices with the network to provide intelligent services, which is regarded as the key technology for achieving Industry 4.0 and enabling the transformation of the manufacturing sector. Multi-access edge computing (MEC) has brought significant opportunities to expedite the development of IIoT. However, the unique task characteristics and dense deployment of IIoT devices, coupled with the resource starvation problem (RSP) arising from the limited resources of edge servers, pose challenges to the direct applicability of existing MEC algorithms in MEC-assisted IIoT scenarios. To this end, a multi-objective evolutionary algorithm is proposed to simultaneously optimize delay and energy consumption for multi-workflow execution in resource-limited IIoT. First, the initialization of execution location based on delay and the initialization of execution order satisfying the priority constraint can generate high-quality initial solutions. Then, the improved crossover and mutation operations guide the population evolution, which can span the large infeasible solution region. Finally, dynamic task scheduling (DTS) dynamically changes the execution location of tasks affected by RSP according to the execution efficiency, so as to avoid the tasks blindly waiting for server resources. The comprehensive simulation results demonstrate the effectiveness of the proposed method in achieving a balance between the execution delay and energy consumption of IIoT devices.

The biogeographic and evolutionary processes shaping population divergence in Laupala.

Speciation generates biodiversity and the mechanisms involved are thought to vary across the tree of life and across environments. For example, well-studied adaptive radiations are thought to be fuelled by divergent ecological selection, but additionally are influenced heavily by biogeographic, genomic and demographic factors. Mechanisms of non-adaptive radiations, producing ecologically cryptic taxa, have been less well-studied but should likewise be influenced by these latter factors. Comparing among contexts can help pinpoint universal mechanisms and outcomes, especially if we integrate biogeographic, ecological and evolutionary processes. We investigate population divergence in the swordtail cricket Laupala cerasina, a wide-spread endemic on Hawai'i Island and one of 38 ecologically cryptic Laupala species. The nine sampled populations show striking population genetic structure at small spatio-temporal scales. The rapid differentiation among populations and species of Laupala shows that neither a specific geographical context nor ecological opportunity are pre-requisites for rapid divergence. Spatio-temporal patterns in population divergence, population size change, and gene flow are aligned with the chronosequence of the four volcanoes on which L. cerasina occurs and reveal the composite effects of geological dynamics and Quaternary climate change on population dynamics. Spatio-temporal patterns in genetic variation along the genome reveal the interplay of genetic and genomic architecture in shaping population divergence. In early phases of divergence, we find elevated differentiation in genomic regions harbouring mating song loci. In later stages of divergence, we find a signature of linked selection that interacts with recombination rate variation. Comparing our findings with recent work on complementary systems supports the conclusion that mostly universal factors influence the speciation process.

Efficient and scalable prediction of stochastic reaction–diffusion processes using graph neural networks

The dynamics of locally interacting particles that are distributed in space give rise to a multitude of complex behaviours. However the simulation of reaction–diffusion processes which model such systems is highly computationally expensive, the cost increasing rapidly with the size of space. Here, we devise a graph neural network based approach that uses cheap Monte Carlo simulations of reaction–diffusion processes in a small space to cast predictions of the dynamics of the same processes in a much larger and complex space, including spaces modelled by networks with heterogeneous topology. By applying the method to two biological examples, we show that it leads to accurate results in a small fraction of the computation time of standard stochastic simulation methods. The scalability and accuracy of the method suggest it is a promising approach for studying reaction–diffusion processes in complex spatial domains such as those modelling biochemical reactions, population evolution and epidemic spreading.

Exploring the ecological security evaluation of water resources in the Yangtze River Basin under the background of ecological sustainable development

The Yangtze River (hereafter referred to as the YZR), the largest river in China, is of paramount importance for ensuring water resource security. The Yangtze River Basin (hereafter referred to as the YRB) is one of the most densely populated areas in China, and complex human activities have a significant impact on the ecological security of water resources. Therefore, this paper employs theories related to ecological population evolution and the Driving Force-Pressure-State-Impact-Response (DPSIR) model to construct an indicator system for the ecological security of water resources in the YRB. The report evaluates the ecological security status of water resources in each province of the YRB from 2010 to 2019, clarifies the development trend of its water resource ecological security, and proposes corresponding strategies for regional ecological security and coordinated economic development. According to the results of the ecological population evolution competition model, the overall indicator of the ecological security of water resources in the YRB continues to improve, with the safety level increasing annually. Maintaining sound management of water resources in the YRB is crucial for sustainable socioeconomic development. To further promote the ecological security of water resources in the YRB and the coordinated development of the regional economy, this paper proposes policy suggestions such as promoting the continuous advancement of sustainable development projects, actively adjusting industrial structure, continuously enhancing public environmental awareness, and actively participating in international ecological construction and seeking cooperation among multiple departments.

A Co-evolutionary Multi-population Evolutionary Algorithm for Dynamic Multiobjective Optimization

Dynamic multiobjective optimization problems (DMOPs) widely appear in various real-world applications and have attracted increasing attention worldwide. However, how to obtain both good population diversity and fast convergence speed to efficiently solve DMOPs are two challenging issues. Inspired by that the multiple populations for multiple objectives (MPMO) framework can provide algorithms with good population diversity and fast convergence speed, this paper proposes a new efficient algorithm called a co-evolutionary multi-population evolutionary algorithm (CMEA) based on the MPMO framework together with three novel strategies, which are helpful for solving DMOPs efficiently from two aspects. First, in the evolution control aspect, a convergence-based population evolution strategy is proposed to select the suitable population for executing the evolution in different generations, so as to accelerate the convergence speed of the algorithm. Second, in the dynamic control aspect, a multi-population-based dynamic detection strategy and a multi-population-based dynamic response strategy are proposed to help the algorithm maintain the population diversity, which are efficient for detecting and responding to the dynamic changes of environments. Integrating with the above strategies, the CMEA is proposed to solve the DMOP efficiently. The superiority of the proposed CMEA is validated in experiments on widely-used DMOP benchmark problems.

Multi-level guided evolution algorithm for solving fuzzy flexible job shop problem

Traditional flexible job shop scheduling problems (FJSP) are mostly limited to deterministic environments. In actual production, some uncertain factors lead to changes in job processing time. When solving multi-objective fuzzy flexible job shop scheduling problems (MOFFJSP), the existing evolutionary algorithms do not fully utilize information transfer between levels during the population evolution process. Therefore, a multi-level guided evolutionary (MLGE) optimization method is proposed to solve MOFFJSP. In the proposed MLGE method, decomposition and dominance techniques are combined to balance the diversity and convergence performance of the algorithm. Meanwhile, the idea of the Jaya algorithm approaching good individuals and distancing poor individuals is introduced. Combined with improved Jaya operator operations, it is used to guide individual evolution, preserving and inheriting solutions that perform well in terms of convergence and diversity. Finally, numerous experiments are carried out to evaluate the effectiveness of MLGE. The results show that MLGE can provide promising results for MOFFJSP. Additionally, it shows how MLGE outperforms other comparison algorithms in terms of convergence and variety of solutions.

Recent evolution of the star cluster population in the Andromeda’s disk

The most accurate parameters of star clusters are determined by analyzing colour-magnitude diagrams (CMDs) constructed from their member stars. The recent study applying this method to the analysis of the Panchromatic Hubble Andromeda Treasury (PHAT) survey star clusters includes objects up to ages of 300 Myr. In this study, we aim to extend a sample of star clusters with parameters determined based on CMDs of individual stars to older ages, up to ~700 Myr. We have developed an isochrone fitting procedure for CMDs of resolved and semi-resolved clusters to determine their parameters. The Bayesian approach is employed in determining star cluster ages and interstellar extinctions. We determined ages and extinctions for a sample of 854 star clusters from the M31 PHAT survey. We found that the star formation rate in the M31 galaxy was rather constant during the last ~130 Myr; however, a strong cluster formation episode occurred ~220 Myr ago, and can be attributed to the predicted passage of the M32 galaxy through the Andromeda’s disk.

Cross-correlated relaxation in the NMR of near-equivalent spin pairs: Longitudinal relaxation and long-lived singlet order.

The evolution of nuclear spin state populations is investigated for the case of a 13C2-labeled triyne in solution, for which the near-equivalent coupled pairs of 13C nuclei experience cross-correlated relaxation mechanisms. Inversion-recovery experiments reveal different recovery curves for the main peak amplitudes, especially when the conversion of population imbalances to observable coherences is induced by a radio frequency pulse with a small flip angle. Measurements are performed over a range of magnetic fields by using a sample shuttle apparatus. In some cases, the time constant TS for decay of nuclear singlet order is more than 100 times larger than the time constant T1 for the equilibration of longitudinal magnetization. The results are interpreted by a theoretical model incorporating cross-correlated relaxation mechanisms, anisotropic rotational diffusion, and an external random magnetic field. A Lindbladian formalism is used to describe the dissipative dynamics of the spin system in an environment of finite temperature. Good agreement is achieved between theory and experiment.

Rapidly rotating Population III stellar models as a source of primary nitrogen

Context. The first stars might have been fast rotators. This would have important consequences for their radiative, mechanical, and chemical feedback. Aims. We discuss the impact of fast initial rotation on the evolution of massive Population III models and on their nitrogen and oxygen stellar yields. Methods. We explore the evolution of Population III stars with initial masses in the range of 9 M⊙ ≤ Mini ≤ 120 M⊙, starting with an initial rotation on the zero-age main sequence equal to 70% of the critical one. Results. We find that with the physics of rotation considered here, our rapidly rotating Population III stellar models do not follow a homogeneous evolution. They lose very little mass in the case in which mechanical winds are switched on when the surface rotation becomes equal to or larger than the critical velocity. The impact on the ionising flux appears to be modest when compared to moderately rotating models. Fast rotation favours, in models with initial masses above ∼20 M⊙, the appearance of a very extended intermediate convective zone around the H-burning shell during the core He-burning phase. This shell has important consequences for the sizes of the He- and CO-cores, and thus impacts the final fate of stars. Moreover, it has a strong impact on nucleosynthesis, boosting the production of primary 14N. Conclusions. Fast initial rotation significantly impacts the chemical feedback of Population III stars. Observations of extremely metal-poor stars and/or starbursting regions are essential to provide constraints on the properties of the first stars.

The heart of galaxy clusters: Demographics and physical properties of cool-core and non-cool-core halos in the TNG-Cluster simulation

We analyzed the physical properties of the gaseous intracluster medium (ICM) at the center of massive galaxy clusters with TNG-Cluster, a new cosmological magnetohydrodynamical simulation. Our sample contains 352 simulated clusters spanning a halo mass range of 1014 < M500c/M⊙ < 2 × 1015 at z = 0. We focused on the proposed classification of clusters into cool-core (CC) and non-cool-core (NCC) populations, the z = 0 distribution of cluster central ICM properties, and the redshift evolution of the CC cluster population. We analyzed the resolved structure and radial profiles of entropy, temperature, electron number density, and pressure. To distinguish between CC and NCC clusters, we considered several criteria: central cooling time, central entropy, central density, X-ray concentration parameter, and density profile slope. According to TNG-Cluster and with no a priori cluster selection, the distributions of these properties are unimodal, whereby CCs and NCCs represent the two extremes. Across the entire TNG-Cluster sample at z = 0 and based on the central cooling time, the strong CC fraction is fSCC = 24%, compared to fWCC = 60% and fNCC = 16% for weak and NCCs, respectively. However, the fraction of CCs depends strongly on both halo mass and redshift, although the magnitude and even direction of the trends vary with definition. The abundant statistics of simulated high-mass clusters in TNG-Cluster enabled us to match observational samples and make a comparison with data. The CC fractions from z = 0 to z = 2 are in broad agreement with observations, as are the radial profiles of thermodynamical quantities, globally as well as when divided as CC versus NCC halos. TNG-Cluster can therefore be used as a laboratory to study the evolution and transformations of cluster cores due to mergers, AGN feedback, and other physical processes.

Wheat dwarfing reshapes plant and fungal development in arbuscular mycorrhizal symbiosis.

The introduction of Reduced height (Rht) dwarfing genes into elite wheat varieties has contributed to enhanced yield gain in high input agrosystems by preventing lodging. Yet, how modern selection for dwarfing has affected symbiosis remains poorly documented. In this study, we evaluated the response of both the plant and the arbuscular mycorrhizal fungus to plant genetic variation at a major Quantitative Trait Locus called QTL 4B2, known to harbor a Rht dwarfing gene, when forming the symbiosis. We used twelve inbred genotypes derived from a diversity base broadened durum wheat Evolutionary Pre-breeding Population and genotyped with a high-throughput Single Nucleotide Polymorphism (SNP) genotyping array. In a microcosm setup segregating roots and the extra-radical mycelium, each wheat genotype was grown with or without the presence of Rhizophagus irregularis. To characterize arbuscular mycorrhizal symbiosis, we assessed hyphal density, root colonization, spore production, and plant biomass. Additionally, we split the variation of these variables due either to genotypes or to the Rht dwarfing genes alone. The fungus exhibited greater development in the roots of Dwarf plants compared to non-Dwarf plants, showing increases of 27%, 37% and 51% in root colonization, arbuscules, and vesicles, respectively. In addition, the biomass of the extra-radical fungal structures increased by around 31% in Dwarf plants. The biomass of plant roots decreased by about 43% in mycorrhizal Dwarf plants. Interestingly, extraradical hyphal production was found to be partly genetically determined with no significant effect of Rht, as for plant biomasses. In contrast, variations in root colonization, arbuscules and extraradical spore production were explained by Rht dwarfing genes. Finally, when mycorrhizal, Dwarf plants had significantly lower total P content, pointing towards a less beneficial symbiosis for the plant and increased profit for the fungus. These results highlight the effect of Rht dwarfing genes on both root and fungal development. This calls for further research into the molecular mechanisms governing these effects, as well as changes in plant physiology, and their implications for fostering arbuscular mycorrhizal symbiosis in sustainable agrosystems.

High-quality reference genome decoding and population evolution analysis of prickly Sechium edule

Sechium edule (chayote) is an important vegetable crop belonging to the Cucurbitaceae family. To decipher the chayote genome, a high-quality chromosome-level chayote genome was obtained by genome sequencing and bioinformatic analysis. The total length was 612.91 Mb, and 25 755 genes were detected in the chayote genome. The contig N50 was more than 20.01 Mb, and the scaffold N50 was over 47.11 Mb. Of the genome, 60.35% were composed of repetitive sequences, and 31.18% of genome sequences belonged to long-terminal repeats. A global alignment of homologous regions in chayote and other Cucurbitaceae plant genomes was constructed using grape as a reference. Based on this genome-wide and global alignment map, researchers can easily identify homologous collinear genes of the studied genomes in most Cucurbitaceae species. Twenty-five chayote accessions were divided into two subgroups based on phylogenetic tree, population structure analysis, and principal component analysis using genome re-sequencing data. The chayote genome, re-sequencing dataset, and comprehensive genomic analysis will accelerate comparative and functional genomic analysis of chayote and other Cucurbitaceae species in the future.

Population Genomics analysis of Leptosphaeria biglobosa Associated with Brassica napus in China Reveals That Geographical Distribution Influences Its Genetic Polymorphism.

Blackleg disease, a major threat to Brassica crops worldwide, is primarily caused by the pathogen Leptosphaeria biglobosa. Investigating the genetic variation of L. biglobosa is crucial for managing and preventing the disease in Brassica napus. To date, there is scarce genomic variation information available for populations of L. biglobosa in China. In this study, 73 L. biglobosa strains of canola stalks were collected from 12 provinces in China and subjected to re-sequencing. The 73 assemblies averaged 1340 contigs, 72,123 bp N50, and 30.17 Mb in size. In total, 9409 core orthogroups and 867 accessory orthogroups were identified. A total of 727,724 mutation loci were identified, including 695,230 SNPs and 32,494 indels. Principal component analysis (PCA) and population structure analysis showed that these strains could be divided into seven subgroups. The strains in most provinces were clustered into a single subgroup, suggesting a strong influence of the geographic environment on strain variation. The average nucleotide diversity (θπ) of all strains was 1.03 × 10-3, indicating important genetic diversity among strains from different regions of China. This study provides valuable resources for future comparative genomics, gives new insights into the population evolution of L. biglobosa, and supports the development of strategies for managing blackleg disease in canola.

Analytical model and flow velocity control of electrohydrodynamics system with multi-needle corona discharge

For the flow field distribution and control mechanism generated by the electrohydrodynamics (EHD) system with multi-needle corona discharge, this paper takes the multi-needle EHD pump as the research object, establishes different types of physical models through regional division, constructs multi-physical field coupling relationship, and derives a simplified EHD flow velocity equation suitable for the EHD system with multi-needle corona discharge. Combined with the intelligent optimization method of population evolution, a novel and effective intelligent algorithm is designed for the numerical analysis of the velocity profile distribution of a multi-needle EHD pump, and the flow velocity control law of the multi-needle EHD pump is analyzed by quantitative calculation. The validity of the model and analysis is verified by the electric field and flow field simulation of the multi-needle EHD pump system. The calculation results show that the voltage parameter is more dominant than the electrode spacing parameter in the steady-state flow velocity control of the multi-needle EHD pump, and both the maximum flow velocity and the average flow velocity are superlinearly controlled by voltage. In the design of multi-needle EHD pump with an electrode spacing of 1 cm, the simulation results show that the maximum gas flow velocity of 0.82 m/s can be obtained by providing 5000 V voltage, which verifies the design of a miniaturized multi-needle EHD pump and its feasibility in gas lasers and other application scenarios.

SPRISS: Scalable and precise resistive impact sensor for smallsats, architecture description and tests

Space debris are a tangible risk for satellites in Earth orbits. Indeed, in the last decades fragmentation events have generated a large number of uncontrolled objects that have made the debris population grow. Modelling the space debris environment is becoming a fundamental task to evaluate the vulnerability of operational satellites, the probability of accidental collisions with uncontrolled objects, and the evolution of the debris population. With this aim, remote and in-situ measurements provide valuable data to tune the space debris population models and improve their reliability. While large satellites can be observed and tracked from ground, the sub-millimeter debris population requires in-situ measurements. In this context, in-orbit impact sensors are a key technology to obtain information about the sub-mm space debris environment. In this framework, a small-scale impact sensor sized to be integrated in a 2U CubeSat is being developed at the University of Padova. The sensor consists of a multitude of thin, conductive stripes arranged on a thin film of non-conductive material. When a debris hits the sensor, one or more stripes are severed, and the impact is detected. Moreover, the sensor design ensures low power consumption, making it feasible for CubeSat space missions. This work presents the latest outcomes obtained from the development of the sensor. Specifically, structural analyses are performed to assess that the sensor can withstand the launch loads, as well as thermal analyses to confirm its endurance capability with in-orbit temperatures. The number of expected impacts during the mission is predicted through orbital propagation, using state-of-the-art debris environment modelling tools. Finally, the paper presents a functional shooting test and vibration tests, executed onto a development model of the sensor, which verify its functionality and validate a Technology Readiness Level (TRL) of 4.

Predicting the evolution of bacterial populations with an epistatic selection-mutation model

Predicting the evolution of bacterial populations with an epistatic selection-mutation model

The Gauss-cos model for the autocorrelation function of fertility rate

Based on the fertility data, we derive a Gauss-cosh model for autocorrelation functions in the Fourier transform space by adopting the mass-point accumulation principle of the Grassmann space to the amplitude-frequency to study the evolution of population in social problems. By using the Gauss-cosh model in the Fourier transform space, a Gauss-cos model for autocorrelation functions is suggested on time domain. The physical interpretation of the Gauss-cos model is also provided that it is a quadratic damping oscillator. Then a new algorithm is derived to approximate the autocorrelation function based on the Gauss-cos model. Numerical experiments verify that the Gauss-cos model effectively fits the autocorrelation function of the fertility rate. After analyzing the numerical results, we detect that the autocorrelation of the fertility rate is described by two major principal phases, which are caused by the public influence and the mother-daughter influence. The mother-daughter influence strongly expresses a periodic regression of the generation.

Optimization of Sewing Equipment Based on Improved Genetic-ant Colony Hybrid Algorithm

The optimization of the cutting path of the sample can effectively reduce the cutting time, thereby improving the production efficiency of numerical control processing. This paper comprehensively considers the impact of the cutting order and the position of the knife entry point on the cutting path, converts the cutting path problem into a type of traveling salesman problem (TSP), and proposes an improved genetic-particle swarm optimization algorithm. The selection mechanism of the algorithm combines the elitist retention strategy and roulette wheel selection method to accelerate the search for the optimal solution; the mutation strategy designs a linear decreasing mutation rate, which enhances the global search ability; at the same time, introduces the ant colony optimization algorithm to process the fitness function, adjusts the population evolution difference, and speeds up the optimization process. Through this hybrid algorithm, the cutting order of the sample can be quickly optimized, and the nearest neighbor algorithm is used to determine the position of the knife entry point. Tests are conducted on clothing patterning charts and standard examples. Compared with several commonly used algorithms, experimental results verify the feasibility and effectiveness of this algorithm

The response of the cometary ionosphere to space weather forcing

ABSTRACT The Rosetta spacecraft observed the temporal evolution of the ion populations within the ionosphere of comet 67P/Churyumov–Gerasimenko. A striking feature of the ion spectrum is represented by the so-called medium-energy ion peaks, which recurrently emerge from the low-energy ion background with their energy levels typically reaching 50 to 1000 eV before their energy gradually decreases, and they disappear from the measurements. These peaks are believed to be caused by space weather forcing, but there was no conclusive evidence until now. We investigated the characteristics of these ions, paying special attention to the connection between the solar wind dynamic pressure and the amount and energy of the medium-energy ions. Our findings reveal a strikingly accurate direct correlation between the dynamic pressure of the solar wind at the position of the comet and the amount of medium-energy ions measured by Rosetta. The ion energy also unquestionably reacts to the effects of solar wind pressure variation, but this parameter is strongly affected by the production rate and the distance from the nucleus as well. We explain these close correlations between cometary ion and solar wind characteristics using the well-founded assumption that certain boundary layers of the magnetosphere can move in and out under the influence of higher and lower solar wind pressure.