Slime Mold Research Articles

Self-adaptive hybrid mutation slime mould algorithm: Case studies on UAV path planning, engineering problems, photovoltaic models and infinite impulse response

There are many classic highly complex optimization problems in the world, therefore, it is still necessary to find an applicable and effective algorithm to solve these problems. In this paper, self-adaptive hybrid cross mutation slime mold algorithm is proposed, which is AHCSMA, to solve these problems efficiently. Specifically, there are three innovations in this paper: (i) new self-adaptive Cauchy mutation operator is developed to improve the mutation ability of the population; (ii) the crossover rate balance mechanism is proposed to make up for the neglected relationship between individuals and crossover rates. Then the differential vector information between the dominant individual and other individuals in the population is highly utilized to increase the evolution speed of the algorithm; (iii) self-adaptive restart hybrid opposition learning is designed to alleviate the situation where the algorithm falls into local optimality. To verify the competitive of AHCSMA, UAV path planning problems, engineering problems, nonlinear parameter extraction of photovoltaic model problems and parameter identification problems of highly nonlinear infinite impulse response are used to test the ability of the AHCSMA, accumulation more than 50 algorithms are used as comparison algorithms, and results report that AHCSMA is extremely competitive and performs better when optimizing these real-life problems.

Slime Mould Algorithm Based on a Gaussian Mutation for Solving Constrained Optimization Problems

The slime mould algorithm may not be enough and tends to trap into local optima, low population diversity, and suffers insufficient exploitation when real-world optimization problems become more complex. To overcome the limitations of SMA, the Gaussian mutation (GM) with a novel strategy is proposed to enhance SMA and it is named as SMA-GM. The GM is used to increase population diversity, which helps SMA come out of local optima and retain a robust local search capability. Additionally, the oscillatory parameter is updated and incorporated with GM to set the balance between exploration and exploitation. By using a greedy selection technique, this study retains an optimal slime mould position while ensuring the algorithm’s rapid convergence. The SMA-GM performance was evaluated by using unconstrained, constrained, and CEC2022 benchmark functions. The results show that the proposed SMA-GM has a more robust capacity for global search, improved stability, a faster rate of convergence, and the ability to solve constrained optimization problems. Additionally, the Wilcoxon rank sum test illustrates that there is a significant difference between the optimization outcomes of SMA-GM and each compared algorithm. Furthermore, the engineering problem such as industrial refrigeration system (IRS), optimal operation of the alkylation unit problem, welded beam and tension/compression spring design problem are solved, and results prove that the proposed algorithm has a better optimization efficiency to reach the optimum value.

Isolation and Structure Determination of New Pyrones from Dictyostelium spp. Cellular Slime Molds Coincubated with Pseudomonas spp.

Cellular slime molds are excellent model organisms in the field of cell and developmental biology because of their simple developmental patterns. During our studies on the identification of bioactive molecules from secondary metabolites of cellular slime molds toward the development of novel pharmaceuticals, we revealed the structural diversity of secondary metabolites. Cellular slime molds grow by feeding on bacteria, such as Klebsiella aerogenes and Escherichia coli, without using medium components. Although changing the feeding bacteria is expected to affect dramatically the secondary metabolite production, the effect of the feeding bacteria on the production of secondary metabolites is not known. Herein, we report the isolation and structure elucidation of clavapyrone (1) from Dictyostelium clavatum, intermedipyrone (2) from D. magnum, and magnumiol (3) from D. intermedium. These compounds are not obtained from usual cultural conditions with Klebsiella aerogenes but obtained from coincubated conditions with Pseudomonas spp. The results demonstrate the diversity of the secondary metabolites of cellular slime molds and suggest that widening the range of feeding bacteria for cellular slime molds would increase their application potential in drug discovery.

Adaptive reinforcement learning-based control using proximal policy optimization and slime mould algorithm with experimental tower crane system validation

This paper presents a novel optimal reference tracking control approach resulted from the combination of a popular policy gradient Reinforcement Learning (RL) algorithm, namely Proximal Policy Optimization (PPO), and a metaheuristic Slime Mould Algorithm (SMA). One of the most important parameters in the PPO-based RL process is the learning rate, which has a big impact on how the parameters of the actor neural network (NN) are iteratively updated. In every episode of the RL process, the weights and the biases of the actor NN are multiplied with the learning rate, determining how much the learning agent will step into a certain direction computed based on previous experiences. The classical PPO algorithm usually relies on fixed values for the learning rates which rarely change, or not at all, during the learning process. However, its main drawback is that the learning agent cannot take advantage of positive momentum in the learning process by accelerating towards good learning experiences or slow down and quickly change the direction in the case of consecutive negative learning experiences. The main objective of the combination proposed in this paper is to create an adaptive SMA-based PPO approach applied to control systems, which instead of using fixed learning rate values, it uses the SMA to compute optimal values of the learning rates in each time step of the learning process based on the progress of the learning agent. This paper investigates if the adaptive SMA-based PPO control approach can be considered as an alternative to the classical PPO version, which employs fixed values of the learning rate. A comparison is carried out using control system performance indices gathered while performing an optimal reference tracking control task on tower crane system laboratory equipment.

Optimal low-impact development Facility Design in Urban Environments: A multidimensional optimization approach employing slime mould and nondominated sorting genetic algorithms

This study presents the application of the slime mould algorithm (SMA) and the nondominated sorting genetic algorithm II (NSGA-II) to optimize low-impact development (LID) strategies in urban areas. The focus is on minimizing costs and improving water quality, using three LID practices (vegetated swales, bioretention systems, and porous pavements) in combination with a drainage system. The effectiveness of this stormwater management model (SWMM)-SMA-NSGA-II model is demonstrated in Tehran, Iran, where bioretention is found to be the most successful approach for improving water quality. The results also show that the SMA outperforms the NSGA-II in optimizing cost-effective LID solutions and is more computationally efficient, potentially due to its crossover operator. This research highlights the importance of considering qualitative aspects of urban runoff management. The study demonstrates that the combination of multiple LID strategies and the use of SMA and NSGA-II have the potential to achieve optimal solutions for managing stormwater in urban areas.

Predicting corrosion behaviour of steel reinforcement in eco-friendly coral aggregate concrete based on hybrid machine learning methods

ABSTRACT This study proposes a machine learning-assisted method for assessing steel reinforcement corrosion, utilising data on chloride ion concentration (CIC) and chloride ion concentration threshold (CCT) from eco-friendly coral aggregate concrete (EFCAC). A total of 2185 data points were collected to establish the EFCAC-CIC database. A multi-objective slime mould optimised support vector regression (MOSMA-SVR) model for EFCAC-CIC was developed. The significance of each feature to EFCAC-CIC was analysed. Subsequently, a graphical user interface (GUI) was developed based on the MOSMA-SVR model. Finally, the GUI and EFCAC-CCT were used to assess the corrosion behaviour of reinforcement. Results indicate that the MOSMA-SVR model provides predictions that are closer to the actual values, with smaller mean errors and standard deviations. The performance indicators, including coefficient of determination, mean absolute error, mean absolute percentage error, mean square error, root mean square error, and a20-index, of the MOSMA-SVR model are 0.987, 0.0124, 0.0332, 2.91e − 4, 0.0171 and 0.9991, respectively, and they are superior to those of the other tested models. The water type and the water–binder ratio are identified as the two most critical factors. Furthermore, by integrating the GUI and feature importance analysis results, chloride salt-resistant EFCAC can be designed. When this is combined with the GUI and EFCAC-CCT, the corrosion of rebar in EFCAC can be assessed in real time.

Predictive Analysis of Concrete Slump Using a Stochastic Search-Consolidated Neural Network

Attaining a dependable measurement of concrete slump is crucial as it is a valuable indication of concrete workability. On the other hand, complexities associated with costly traditional approaches have driven engineers to use indirect efficient models such as metaheuristic-based machine learning for approximating the slump. While the literature shows promising application of some metaheuristic techniques for this purpose, the large variety of these algorithms calls for evaluating the most capable ones to keep the solution updated. Stochastic fractal search (SFS) is one of the most powerful optimization algorithms in the literature that has not received appropriate attention in analyzing concrete mechanical parameters. In the present research, a multi-layer perceptron neural network (NN-MLP), is enhanced using the SFS. The proposed SFS-NN-MLP model aims to predict the slump based on the amount of ingredients in the mixture, as well as the curing age of specimens. Accuracy assessment revealed that the proposed model can deal with the assigned task with excellent accuracy. It indicates that the SFS could properly tune the parameters required for training the NN-MLP, and consequently, the trained network could reliably calculate the slump of specimens that were not analyzed before. For comparative validation, the SFS was replaced with two similar optimizers, namely elephant herding optimization algorithm (EHO) and slime mould algorithm (SMA). Based on the calculated mean square errors of 5.6526, 6.1129, and 7.3561 along with mean absolute errors of 4.6657, 5.0078, and 6.3066, as well as the percentage-Pearson correlation coefficients of 78.06%, 73.95%, and 58.11%, respectively for the SFS-NN-MLP, EHO-NN-MLP, and SMA-NN-MLP, it was shown that the SFS-NN-MLP is the most accurate predictor. Hereupon, the SFS-NN-MLP model is recommended to be effectively used for obtaining a cost-efficient approximation of concrete slump in real-world projects.

The unconfined compressive strength estimation of rocks using a novel hybridization technique based on the regulated Gaussian processor

The unconfined compressive strength (UCS) of rocks is a crucial factor in geotechnical engineering, assuming a central role in various civil engineering undertakings, including tunnel construction, mining operations, and the design of foundations. The precision in forecasting UCS holds paramount importance in upholding the security and steadfastness of these endeavors. This article introduces a fresh methodology for UCS prognostication by amalgamating Gaussian process regression (GPR) with two pioneering optimization techniques: sand cat swarm optimization (SCSO) and the equilibrium slime mould algorithm (ESMA). Conventional techniques for UCS prediction frequently encounter obstacles like gradual convergence and the potential for becoming ensnared in local minima. In this investigation, GPR is the foundational predictive model due to its adeptness in managing nonlinear associations within the dataset. The fusion of GPR with cutting-edge optimizers is envisioned to elevate the precision and expeditiousness of UCS prognostications.An extensive collection of rock samples, each accompanied by UCS measurements, is harnessed to assess the suggested methodology. The efficacy of the GPSC and GPES models is juxtaposed with the conventional GPR technique. The findings reveal that incorporating SCSO and ESMA optimizers into GPR brings about a noteworthy enhancement in UCS prediction accuracy and expedites convergence. Notably, the GPSC models exhibit exceptional performance, evidenced by an exceptional R2 value of 0.995 and an impressively minimal RMSE value of 1.913. These findings emphasize the GPSC model’s potential as an exceedingly auspicious tool for experts in the realms of engineering and geology. It presents a sturdy and dependable method for UCS prediction, a resource of immense value in augmenting the security and efficiency of civil engineering endeavors.

AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature

To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems.

An innovative intelligent design method of alkali-activated foamed geopolymer: Mixture optimization and performance prediction

The complex design parameters and lack of standard mix design methods generate a difficulty in the high efficiently preparation of alkali-activated foamed geopolymer (AAFG). Therefore, this paper proposes an innovative intelligent design method of AAFG based on machine learning (ML) and Metaheuristic Optimization Algorithms (MOA). The effects of twelve factors on drying density (DD) and compressive strength (CS) of AAFG are revealed. The DD and CS predictive models of AAFG with high accuracy (R2 = 0.96 and 0.84) and strong generalization are trained through Random Forest (RF). The enlargements of proportions of granulated blast furnace slag (GBFS) and metakaolin (MK) in precursors can improve the CS but are not conducive to reduction of DD. Higher proportion of fly ash (FA) in precursors can efficiently reduce DD but can weaken the CS. Higher or lower Na2O content, silicate modulus (Ms) and water to binder (W/B) can reduce DD and CS with the optimal foaming conditions of 20 %, 1.0 and 0.75. More additions of foaming agents and foam stabilizers can reduce DD, while the CS has a remarkable degradation. Higher or lower foaming temperature (FT) is both not beneficial for the reduction of DD and enhancement of CS with the optimal FT of 60 °C. The intelligent design system of AAFG developed by RF and Slime Mould Algorithm (SMA) has easy operability, high efficiency, and good stability. The optimal preparation parameters can be efficiently acquired and the corresponding performances can be quickly and accurately predicted.

The ring rules the chain - inositol pyrophosphates and the regulation of inorganic polyphosphate.

The maintenance of phosphate homeostasis serves as a foundation for energy metabolism and signal transduction processes in all living organisms. Inositol pyrophosphates (PP-InsPs), composed of an inositol ring decorated with monophosphate and diphosphate moieties, and inorganic polyphosphate (polyP), chains of orthophosphate residues linked by phosphoanhydride bonds, are energy-rich biomolecules that play critical roles in phosphate homeostasis. There is a complex interplay between these two phosphate-rich molecules, and they share an interdependent relationship with cellular adenosine triphosphate (ATP) and inorganic phosphate (Pi). In eukaryotes, the enzymes involved in PP-InsP synthesis show some degree of conservation across species, whereas distinct enzymology exists for polyP synthesis among different organisms. In fact, the mechanism of polyP synthesis in metazoans, including mammals, is still unclear. Early studies on PP-InsP and polyP synthesis were conducted in the slime mould Dictyostelium discoideum, but it is in the budding yeast Saccharomyces cerevisiae that a clear understanding of the interplay between polyP, PP-InsPs, and Pi homeostasis has now been established. Recent research has shed more light on the influence of PP-InsPs on polyP in mammals, and the regulation of both these molecules by cellular ATP and Pi levels. In this review we will discuss the cross-talk between PP-InsPs, polyP, ATP, and Pi in the context of budding yeast, slime mould, and mammals. We will also highlight the similarities and differences in the relationship between these phosphate-rich biomolecules among this group of organisms.

Optimizing solar panel maximum power point tracking and parasitic parameter extraction in partial shading with Enhanced Slime Mold optimization

Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find near-optimal power points by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). The proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5 S–5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABSO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to the above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.

Sparse Optimal Design of an Ultrasonic Sensor Array for Fast TFM Based on a Discrete Slime Mold Algorithm

Sparse Optimal Design of an Ultrasonic Sensor Array for Fast TFM Based on a Discrete Slime Mold Algorithm

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Optimizing Built Environment in Urban Negative Spaces Using Parametric Methods—Research on a High-Density City in China

In the early stage of architectural design, addressing the challenges posed by negative spaces in high-density urban environments is crucial for enhancing spatial efficiency and building sustainability. Multiple studies employed digital methods and tools to address these issues, such as parametric design, simulation, and genetic algorithms, to investigate architectural generation approaches for urban negative spaces. This article proposes an integrated design process that involves finding the location and form of negative spaces, generating solutions using slime mold and wasp algorithms, and optimizing and analyzing solutions using the Wallacei plugin in Grasshopper. This comprehensive approach underscores the potential of parametric design to yield a multitude of solutions while also acknowledging the convergence challenges encountered during simulations, particularly in optimizing for optimal sunlight exposure during the winter solstice and minimal solar radiation in the summer. Analyzing the optimization goals and parameter values of the 15th Pareto optimal solution in the 100th generation reveals: (1) a higher number of units leads to positive correlation growth in both objectives; (2) within a certain number of units, parametrically generated solutions facilitate the convergence of optimization goals, yielding optimal outcomes. Therefore, factors such as the range of unit quantities and proportions need consideration during early-stage parametric design and simulation. This study explores a design methodology for negative spaces in high-density urban cities, validating the feasibility of various mainstream generation methods and offering insights for future research.

Improving Support Vector Regression for Predicting Mechanical Properties in Low-Alloy Steel and Comparative Analysis

Low-alloy steel is widely employed in the aviation industry for its exceptional mechanical properties. These materials are frequently used in critical structural components such as aircraft landing gear and engine mounts, where a high strength-to-weight ratio is crucial for optimal performance. However, the mechanical properties of low-alloy steel are influenced by various components and their compositions, making identification and prediction challenging. Accurately predicting these mechanical properties can significantly reduce the development time of new alloy steel, lower production costs, and offer valuable insights for design analysis. support vector regression (SVR) is known for its superior learning and generalization capabilities. However, optimizing SVR performance can be challenging due to the significant impact of the penalty factor and kernel parameters. To address this issue, a hybrid method called SMA-SVR is proposed, which combines the Slime Mould Algorithm (SMA) with SVR. This hybrid approach aims to efficiently and accurately predict two crucial mechanical parameters of low-alloy steel: tensile strength and 0.2% proof stress. Detailed descriptions of the modeling processes and principles that are involved in the hybrid method are provided. Furthermore, three other popular hybrid models for comparison are introduced. To evaluate the performance of these models, four statistical measures are utilized: Mean Absolute Error, Root Mean Square Error, R-Squared, and computational time. Using data from the NIMS database and from material tests conducted on a universal testing machine, experiments were carried out to compare the performance of these models. The results indicate that SMA-SVR outperforms the other methods in terms of accuracy and computational efficiency.

Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm

In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA.

Forecasting the total building energy based on its architectural features using a combination of CatBoost and meta-heuristic algorithms

This research examines the overall energy usage in residential buildings, focusing on architectural characteristics. The study utilizes a combination of the CatBoost method and meta-heuristic algorithms for analysis. The main approach of this research is based on the accuracy defects of individual models, which leads to the employment of CatBoost as a group model. Due to the lack of enough examinations while utilizing CatBoost method, this model and its hyperparameters are optimized using various meta-heuristic methods, including Phasor Particle Swarm Optimization (PPSO), Slime Mould Algorithm (SMA), Sparrow Search Algorithm (SSA), Ant Lion Optimizer (ALO), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO). Eventually, the performance of all models is compared by conduction of a case study, using diverse statistical examination indexes divided by the dwelling types i.e., (1) Standard efficiency upgraded dwellings (D1), (2) High efficiency upgraded dwellings (D2), and (3) Ultra high efficiency upgraded dwellings (D3). The results show that the hybrid proposed method has a proper ability to investigate the total site energy. The results show that for the D1 dwelling and according to the test dataset, the integrated CatBoost-SMA model indicates the most desired performance in predicting the total site energy. But for D2 and D3 dwellings and referring to the test dataset, the statistical evaluation indexes emphasize that the integrated CatBoost-PPSO method shows the most reliable performance.

A longer-chain acylated derivative of Dictyostelium differentiation-inducing factor-1 enhances the antimalarial activity against Plasmodium parasites

The spread of malarial parasites resistant to first-line treatments such as artemisinin combination therapies is a global health concern. Differentiation-inducing factor 1 (DIF-1) is a chlorinated alkylphenone (1-(3,5-dichloro-2,6-dihydroxy-4-methoxyphenyl) hexan-1-one) originally found in the cellular slime mould Dictyostelium discoideum. We previously showed that some derivatives of DIF-1, particularly DIF-1(+2) (1-(3,5-dichloro-2,6-dihydroxy-4-methoxyphenyl) octan-1-one), exert potent antimalarial activities. In this study, we synthesised DIF-1(+3) (1-(3,5-dichloro-2,6-dihydroxy-4-methoxyphenyl) nonan-1-one). We then evaluated the effects of DIF-1(+3) in vitro on Plasmodium falciparum and in vivo over 7 days (50–100 mg/kg/day) in a mouse model of Plasmodium berghei. DIF-1(+3) exhibited a half-maximal inhibitory concentration of approximately 20–30 % of DIF-1(+2) in three laboratory strains with a selectivity index > 263, including in strains resistant to chloroquine and artemisinin. Parasite growth and multiplication were almost completely suppressed by treatment with 100 mg/kg DIF-1(+3). The survival time of infected mice was significantly increased (P = 0.006) with no apparent adverse effects. In summary, addition of an acyl group to DIF-1(+2) to prepare DIF-1(+3) substantially enhanced antimalarial activity, even in drug-resistant malaria, indicating the potential of applying DIF-1(+3) for malaria treatment.

Molecular phylogenetic analyses support the validity of Ceratiomyxa porioides (Amoebozoa, Eumycetozoa) at species level

The frequently encountered macroscopic slime molds of the genus Ceratiomyxa have long been recognized by mycologists and protistologists for hundreds of years. These organisms are amoebozoan amoebae that live and grow inside and on the surface of decaying wood. When conditions are favorable, they form subaerial sporulating structures called fruiting bodies which take on a variety of forms. These forms are typically some arrangement of column and/or branches, but one is uniquely poroid, forming folds instead. Originally, this poroid morphology was designated as its own species. However, it was not always clear what significance fruiting body morphology held in determining species. Currently, Ceratiomyxa fruticulosa var. porioides, the poroid form, is considered a taxonomic variety of Ceratiomyxa fruticulosa based on morphological designation alone. Despite its long history of observation and study, the genus Ceratiomyxa has been paid little molecular attention to alleviate these morphological issues. We have obtained the first transcriptomes of the taxon C. fruticulosa var. porioides and found single gene phylogenetic and multigene phylogenomic support to separate it from C. fruticulosa. This provides molecular evidence that fruiting body morphology does correspond to species level diversity. Therefore, we formally raise Ceratiomyxa porioides to species level.