2. Experimental observations on detachment in the heliotron LHD and comparison with tokamaks

#### 2.1 Main characteristics of divertor plasmas

By rising the plasma density in tokamaks, the plasma in the scrape-off layer (SOL) and divertor goes through several qualitatively different "regimes" [7]. At a low density level, neutral particles, appearing by the recombination of electrons and ions on the divertor target plates, escape freely into the confined plasma volume. Here, these so-called recycling neutrals are ionized and charged species generated diffuse across the magnetic field back into the SOL. Such a particle convection effectively transports heat coming from the plasma core, and the temperatures of the plasma components vary weakly along the magnetic field in the SOL. This regime is referred as either the sheath-limited one or as that of a weak recycling.

With the increasing plasma density, the fraction of recycling neutrals ionized in the vicinity of the targets is growing up. Therefore, beyond the recycling zone, the plasma convection becomes relatively weaker. As a result, a significant parallel temperature gradient develops in the main part of the SOL and the energy toward the targets is transported predominantly by the heat conduction. This regime is called as the conduction limited or a high recycling one. On the one hand, due to the strong temperature dependence of the parallel heat conductivity, T2:5 , the temperature in

Experimental Studies of and Theoretical Models for Detachment in Helical Fusion Devices DOI: http://dx.doi.org/10.5772/intechopen.87130

the SOL changes weakly with parameters such as the plasma density at the separatrix, ns, being comparable with the density in the confined plasma, and the heat flux from the core. On the other hand, the plasma density near the divertor targets, nt, and the plasma flux to the targets, Γt, rise rapidly with ns, as nt ns 3 and Γt ns 2, respectively [8]. As a result, the divertor plasma can be brought into a state of a very high density of 1020–21 m3 and a temperature below 5 eV. Under these conditions, the impurity radiation plays an important role in the divertor power balance.

Contrarily to tokamaks in helical devices, such as LHD and W7-AS, it has been found that the SOL and divertor plasma characteristics do not show such strong nonlinear variation with ns, even if this is already close to the threshold at the detachment onset, nt ns 11:5 [9, 10]. It has been interpreted as a result of the momentum (pressure) loss in the stochastic field line region [11, 12] or in the island divertor structure [10]. In these regions, parallel plasma particle flows, along flux tubes of very different connection lengths or even streaming in opposite directions [13], are strongly interconnected through the cross-field momentum transfer. Therefore, the divertor plasma density remains relatively low, of 1019 m3 , and the temperature relatively high, of 10 eV, till the detachment transition [9]. (Numerical simulations for W7-X [14] predict, however, a high recycling regime, due to the larger spatial separation of the counter-streaming flows in the larger island.)

Nonetheless, experimental observations demonstrate that in the LHD impurity, radiation plays an important role for the divertor plasma cooling, detachment onset, and stability conditions. Here, however, the main radiation source is not located in the divertor legs but in the stochastic layer. Figure 1 shows the tomographic reconstruction of carbon impurity emission in the edge stochastic layer of LHD just before the detachment transition, as well as the magnetic field line connection length (LC) [15]. Although, the emission of low charge state, CII (C1+), is distributed along divertor leg and the very periphery of the stochastic layer, that from the higher charge state, CIV (C3+), being the main radiating species [16], comes from the stochastic layer only.

In the LHD, studies on the divertor detachment are performed by seeding impurities deliberately [17–19] and by applying RMP from special coils [20–22]. In this chapter, we focus on the detachment with the RMP application, which is a unique feature of the LHD.


*3.3. Middleware Support*

A multi-robot system, as any other distributed system, benefits from middleware that hides the complexities of distributed computing in heterogeneous environments and, thus, eases the job of the developer of a distributed application. In general, middleware for robotic applications needs to satisfy the same basic requirements as any middleware in a distributed computing system, i.e., to simplify the application design by making transparent the low-level details of the underlying hardware, software, communication, sensing, and actuating activities. Moreover, middleware facilitates the integration of new technologies, and it improves software maintenance and evolution, as well as software re-use across multiple development efforts, thus reducing application development costs.

Taking up experiences with agent reference models (e.g., FIPA and its offspring NAWG (FIPA P2P Nomadic Agents WG (P2PNA WG6), http://www.fipa.org/subgroups/P2PNA-WG.html)) and agent platform implementations (see the FIPA web page at http://www.fipa.org/resources/livesystems.html for an (outdated) list of publicly available FIPA compliant implementations of agent platforms), as well as with popular general-purpose middleware systems, many middleware architectures specifically for multi-robot systems were proposed in the literature. We point the reader to surveys such as [23,24].

Key challenges are as follows:

• How important are quality of service guarantees for the application at hand?

There are a variety of models underlying middleware for multi-agent coordination. Middleware frameworks such as Orocos [25], CLARAty [26], and MIRO [27] use event-based behavior coordination. The events are triggered by either communication or timer events that are mostly realized as remote procedure calls. This results in an insufficient decoupling between the initiator and receiver of an event. Orocos and MIRO rely on heavyweight architectures, i.e., CORBA [28] and Ice [29], respectively. In contrast, CLARAty which was developed for communication of NASA rovers, explicitly handles unreliable communication and can operate in either centralized or decentralized mode.

The most common communication concept of robot middleware is publish–subscribe due to its higher degree of decoupling. Examples are RoboFrame [30], Spica [31], and ROS [32]. RoboFrame and Spica were designed explicitly for distributed computing in multi-robot systems. They are capable of dealing with unreliable communication, as for example encountered in RoboCup soccer tournaments where standard WiFi communication channels often suffer from bandwidth limitations and packet losses due to interferences among the many WLANs at the competition site. While the robot software framework ROS 1 had limited support for distributed multi-robot applications, the new ROS 2 includes a middleware based on the popular data distribution service (DDS) (Data Distribution Service, OMG, https://www.omg.org/spec/DDS/).

Nevertheless, there are several open issues related to middleware support for robot teamwork that need more research. A truly flexible middleware toolbox would be needed that enables the designer of a teamwork application to configure the middleware by selecting the specific set of components that matches best the application requirements. Operating systems research faced basically the same problem several decades ago when more and more electronic devices became available with very different hardware architectures and application requirements.

Another open issue is the concern for security in distributed collaboration scenarios; most existing middleware solutions for multi-robot systems assume that applications, if needed, can make use of transport-level security mechanisms. Not all applications would need such security. For example, in RoboCup tournaments, there is no real need to secure the communications between the robots (and no time to do so anyway). Likewise, in the collaborative exploration of unknown territories, e.g., on another planet as part of a space mission, secure communication between the explorer robots is not required. However, when it comes to collaborative autonomous vehicles on public roads, potential vulnerabilities are a crucial concern. Moreover, in Section 3.1, we already mentioned the need for security in managing dynamic team membership. More research specifically on adaptive security for teams of autonomous robots is needed. This seems to be a research area on its own.

#### *3.4. Organizational Structure and Decision-Making*

Teams need to take team decisions. For example, vehicles need to agree on the speed and direction of a particular vehicle or soccer robots on the location of the ball on the field. Obviously, application requirements are very different. In an autonomous driving scenario, decisions on the locations and intentions of unequipped traffic participants need to be taken very fast in very dynamic short-lived coalitions. Since safety concerns are paramount, consensus is required for most decisions. On the other hand, in robot soccer, for most decisions, we tolerate a relaxed consensus level but demand swift reactions due to the high dynamics of the game situation.

Decision-making (note that our notion of decision-making is different from Reference [9] where the term is viewed as a synonym for planning and control of a multi-agent system) in a team can be organized according to three basic structural principles, i.e., centralized, hierarchical, and distributed [33]. In a *centralized* structure, decisions are made by a central leader or controller. This structure suffers from the vulnerability of a central point of failure and the potential performance bottleneck. In a *hierarchical* structure, decisions are made at different levels by a hierarchy of leaders that have decision authority according to their rank. Such a structure is more robust than a centralized one because it can potentially react faster to "lower-level" events and tolerate partial failures. Its drawback is the incurred high

organizational overhead. In a *decentralized* structure, all team members autonomously perceive their own situation and the state of the surrounding execution environment. Team members decide about their actions by themselves according to a given team plan. Team decisions that tolerate a relaxed consensus level can be taken using different kinds of voting schemes, auctions, games, and more.

Decisions are made based on the given team plan and observations about the current context. The application developer will need to evaluate and judge the required level of agreement for team actions, as well as the affordable coordination overhead. A decentralized decision structure is an obvious choice if we are concerned about the reliability of the individual robots and the communication network. Likewise, if we deal with temporary coalitions in highly dynamic environments where swift decisions are required, such as in robot soccer, there is no time for the execution of a time-consuming leader election algorithm or any other costly algorithm for establishing an organizational structure. The reader is referred to [33–35] for detailed discussions of organizational structures and decision-making in multi-agent systems.

The key questions are as follows:

Let us look in more detail at a decentralized team organization. Generally, decision-making happens in five steps:

For replication and conflict resolution, there is a choice of well-known protocols depending on the application needs. Hence, a teamwork middleware should offer flexible support for decision-making that is tunable to different application requirements. The core functionality of such a middleware function is to support the team decision-making process with respect to the current values of specified decision variables. Below, we present one concrete example for such a middleware.

The middleware PROViDE [36] is part of a multi-agent framework called ALICA [37]; ALICA teams have a decentralized team structure. PROViDE offers a choice of replication and agreement protocols for common decision variables. If a team decision about the value of an environment variable needs to be made, all team members broadcast their opinion to their teammates. The developer may choose the level of replica consistency depending on the specific application requirements in the face of unreliable communications, temporarily disconnected robots, and diverging sensor readings by the robots. The replicated values of a decision variable can lead to a situation where a robot receives several divergent observations from its teammates in addition to its own observed value. Thus, after the replication phase, a robot needs to decide which value from the set of available opinions it will accept locally as its own value. This may lead to a situation where the individual team members accept different values of the decision variable as their own individual "view of the world". Now, we need a third coordination phase where the robots agree on a single joint value. Such a decision could be based on majority voting, priorities, timestamps, or other criteria.

Thus, there are three distinct phases in team decision-making that resemble the typical process of decision-making in human teams (added in parentheses):

1. Replication of individually perceived values of the decision variable to teammates (team members learn about diverse opinions in the team).

In summary, based on the PROViDE middleware, the application developer can tune the middleware by choosing from a set of provided protocols and, thus, can adapt the quality and overhead of decision-making to diverse application requirements. However, this raises another question. One might argue that such an abundant choice of strategies shifts the complexity onto choosing the right combination of strategies. This argument cannot be ignored. One solution might be to identify reusable typical patterns of strategy combinations for specific application scenarios. This can be addressed in future work.

#### *3.5. Programming*

The complexity of teamwork in multi-robot systems in dynamic and adverse environments requires software architectures and integrated toolchains that ease the development process. Model-driven engineering (MDE) allows developers to shift their focus from implementation to modeling in the domain knowledge space. MDE is expected to promote separation of concerns, efficiency, flexibility, and evolution in application development. From a practical engineering point of view, MDE demands a toolchain that not only automates the required model transformations, but also includes tools for examining the models through simulation (e.g., using Gazebo (http://gazebosim.org/)) or model checking (e.g., using UPPAAL [38]).

In order to ease the modeling and implementation of executable plans for robot applications, domain-specific languages (DSL) were proposed. A DSL is a *computer programming language of limited expressiveness focused on a particular domain* [39]. The "limited" in this definition should not be seen as a negative point; instead, it signals that a DSL is targeted at a specific application domain. Typically, a DSL for developing plans for robots consists of two parts, i.e., a modeling language and an associated execution engine. While there are a number of DSLs available for programming single robots (e.g., [3,40–45]), only a few DSLs explicitly address teamwork for multi-robot systems (e.g., [46,47]) (see [48] for a detailed review of robot DSLs). We claim that the complexity of teamwork in dynamic environments makes such a high-level abstraction a necessity, i.e., a DSL that enables the developer to concentrate on the teamwork behavior of the distributed robot system.

Dynamic environments typically imply a dynamic allocation of tasks to individual team members. A good example is robot soccer. A soccer team continuously needs to be aware of the game situation, which may change instantaneously. Thus, tasks such as defending, attacking the ball, dribbling, blocking an opponent, etc. need to be assigned dynamically based on conditions such as whether the team possesses the ball, proximity of robots to the goal, position of the ball, distance to opponents, etc. Clearly, dynamic task allocation in a decentralized formation is a team decision where all team members should agree on their current duties. In contrast, in an Industry 4.0 scenario, allocation of tasks to robots will typically be static.

General research questions are as follows:

Let us look at three examples for high-level modeling languages for robot teamwork, i.e., STEAM, BITE, and ALICA.

*Shell for Teamwork* (STEAM) [5] is a modeling approach for teamwork. STEAM builds on two well-known teamwork theories, i.e., joint intentions theory [3] and shared plans theory [1]. It tries to combine their benefits in order to achieve a coordinated behavior of the team members. In particular, STEAM assigns sub-teams of agents to a hierarchical shared plan structure. Agents need to establish a joint intention before acting together. This makes the teamwork susceptible to degraded or failed communication links.

The *Bar Ilan Teamwork Engine* (BITE) by Kaminka and Frenkel [49] divides the team modeling into three structures. A tree-like structure, similar to hierarchical task networks [50], represents the global execution plan of the team. Another structure describes the organizational hierarchy of sub-team memberships. This results in a hierarchical task structure that provides a team-wide allocation of robots and sub-teams to behaviors. The third structure describes the social interaction behaviors, i.e., explicit communication and coordination activities between agents. A major drawback of BITE is the fact that it requires a successful negotiation before any physical action can take place. As a result, BITE is not appropriate for domains that require swift reactive behavior.

Let us look at one framework in more detail to make the descriptions more concrete. ALICA (A Language for Interactive Collaborative Agents) is a language and execution environment for developing teamwork applications. ALICA provides a formally defined modeling language, tool support for development, and an execution engine for highly adaptive multi-agent team behavior [37,46]. The design of ALICA targets dynamic environments with fast changing situations, imperfect network communication, and possibly diverging sensor data from team members. It supports the known design blocks of multi-robot systems [51], i.e., task decomposition, team formation, and task allocation, as well as task execution and control. ALICA was developed and used originally for robot soccer and then evolved and applied to other application domains such as collaborative exploration of unknown territories, autonomous driving, and service robotics [52].

The team behavior is specified from a global perspective in a single ALICA program which is deployed to all team members and executed without central control. ALICA uses hierarchically arranged annotated state machines to model robot tasks. Figure 1 shows an example where agents collaborate to explore a territory, collect objects, and assemble some structure. Note that this plan is not complete; the figure only shows the highest specification level. A characteristic feature of ALICA is that task allocation to the individual robots is not static but adaptive to the current context and capabilities of the involved robots. State transitions depend on the situation at hand as perceived by a robot. For further information on the syntax and semantics of ALICA, the reader is referred to [37].

**Figure 1.** Example of ALICA (A Language for Interactive Collaborative Agents) program for an exploration.

ALICA features a strictly decentralized team organization. Team decisions, e.g., about task allocation, result from individual decisions of the team members according to the given team plan, their observations of the environment, and the information exchange with their teammates. Nevertheless, there may be application situations where the team has to agree unanimously on the value of a certain decision variable. This might lead to decision conflicts that have to be resolved. For example, to execute a ball passing plan in robot soccer, at least the pass executor and pass receiver have to agree on their own positions, the position of the ball, and the opponents' positions. Thus, these positions represent joint decision variables. Note that "agree" in this context may mean different levels of agreement on a spectrum from simple broadcasts of opinions to strict consensus [53]. The developer of the respective ALICA plans must decide what kind of agreement is appropriate for an application in a certain situation. To facilitate this choice, ALICA contains a specific decision-support middleware (see Section 3.4 above).

Other examples of languages suitable for the specification of MRT behavior are Buzz [54], ISPL [55], and SCEL [56]. These languages differ in many properties according to their specific application focus and design paradigms. SCEL is the only language that supports open teams using attribute-based interaction (see [19] for a detailed comparison of the three languages).


*Article* **Three New Species of** *Microdochium* **(***Sordariomycetes***,** *Amphisphaeriales***) on** *Miscanthus sinensis* **and** *Phragmites australis* **from Hainan, China**

**Shubin Liu 1,2, Xiaoyong Liu 1 , Zhaoxue Zhang 2 , Jiwen Xia 2 , Xiuguo Zhang 1,2 and Zhe Meng 1,\***

**Abstract:** Species in *Microdochium*, potential agents of biocontrol, have often been reported as plant pathogens, occasionally as endophytes and fungicolous fungi. Combining multiple molecular markers (ITS rDNA, LSU rDNA, TUB2 and RPB2) with morphological characteristics, this study proposes three new species in the genus *Microdochium* represented by seven strains from the plant hosts *Miscanthus sinensis* and *Phragmites australis* in Hainan Island, China. These three species, *Microdochium miscanthi* sp. Nov., *M. sinense* sp. Nov. and *M. hainanense* sp. Nov., are described with MycoBank number, etymology, typification, morphological features and illustrations, as well as placement on molecular phylogenetic trees. Their affinity with morphologically allied and molecularly closely related species are also analyzed. For facilitating identification, an updated key to the species of *Microdochium* is provided herein.

**Keywords:** *Ascomycota*; *Amphisphaeriaceae*; taxonomy; multigene phylogeny; new taxon

## **1. Introduction**

*Microdochium* Syd. & P. Syd. is a fungal genus in the family *Amphisphaeriaceae* G. Winter of the order *Amphisphaeriales* D. Hawksw. & O.E. Erikss., which was established by Sydow [1] and typified by *M. phragmitis* Syd. & P. Syd. on living leaves of the plant host *Phragmites australis* (Cav.) Trin. ex Steud. This genus is characterized by spherical and erumpent stromata composed of minute and transparent cells, small papilla, conical sporulation cells and solitary transparent spindle-shaped-to-oval conidia. In recent years, many taxonomists have continuously enriched the known diversity in *Microdochium* [2–8]. Currently, 54 names are listed for this genus in the Index Fungorum [9], but only 37 species are accepted in the Catalogue of Life [10]. They are difficult to cultivate; therefore, just two-fifths have been studied in pure culture [4–7].

*Microdochium* sensu lato is known to be polyphyletic [2]. While one species, *M. oryzae* (Hashioka & Yokogi) Samuels & I.C. Hallett, was synonymized with *M. albescens* (Thüm.) Hern.-Restr. & Crous [2], and one species, *M. sorghi* (D.C. Bain & Edgerton ex Deighton) U. Braun, was recognized as a synonym of its basionym *Gloeocercospora sorghi* D.C. Bain & Edgerton ex Deighton [9,10], seven species were reclassified to other genera [2,11,12]. In detail, *M. dimerum* (Penz.) Arx, *M. falcatum* B. Sutton & Hodges, *M. fusarioides* D.C. Harris, *M. gracile* Mouch. & Samson, *M. lunatum* (Ellis & Everh.) Arx, *M. tabacinum* (J.F.H. Beyma) Arx, and *M. tripsaci* were transferred to genera *Bisifusarium* L. Lombard, Crous & W. Gams, *Idriella* P.E. Nelson & S. Wilh., *Hyalorbilia* Baral & G. Marson, *Paramicrodochium* Hern-Restr. & Crous, *Bisifusarium*, *Plectosphaerella* Kleb., and *Ephelis* Fr., respectively [2,13–18]. Currently, *Microdochium* sensu stricto is a monophyletic clade. A phylogenetic analysis of translation

**Citation:** Liu, S.; Liu, X.; Zhang, Z.; Xia, J.; Zhang, X.; Meng, Z. Three New Species of *Microdochium* (*Sordariomycetes*, *Amphisphaeriales*) on *Miscanthus sinensis* and *Phragmites australis* from Hainan, China. *J. Fungi* **2022**, *8*, 577. https://doi.org/ 10.3390/jof8060577

Academic Editor: Samantha C. Karunarathna

Received: 30 April 2022 Accepted: 26 May 2022 Published: 27 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

elongation factor 1-alpha gene (TEF1) showed that the isolates of *M. nivale* (Fr.) Samuels & I.C. Hallett were heterogeneous, and hence the variety *M. nivale* var. *majus* (Wollenw.) Samuels & I.C. Hallett was raised to a species rank as *M. majus* (Wollenw.) Glynn & S.G. Edwards, which was still thought to be sister to *M. nivale* [11].

*Microdochium* is an important plant pathogen in grasses and cereals. Liang et al. [19] identified *M. poae* J.M. Liang & Lei Cai as pathogen of *Poa pratensis* L. (Kentucky bluegrass) and *Agrostis stolonifera* L. (creeping bentgrass) which are both cold-season turfgrasses and widely grown on golf courses in northern China. In cold temperate regions, *M. nivale* (=*M. nivale* var. *nivale*) and *M. majus* (=*M. nivale* var. *majus*) [11,12,20] cause "Microdochium patch" on wheat and barley, resulting in significant economic losses. Some species of *Microdochium* are Brassicaceae-associated endophytes in low-Pi conditions (2.48 mg/L) and low-pH conditions (3.4–4.4) [21,22], and *M. bolleyi* is found to be endophytically associated with plant shoots and roots [23] and further to be biocontrol-active against *Gaeumannomyces graminis* var. *tritici* Walker, which causes barley's take-all disease [24]. A few species are fungicolous, such as *M. fusarioides* D.C. Harris on the oospore of *Phytophthora syringae* (Kleb.) Kleb [17].

In this study, three new pathogenic species in *Microdochium* were found among samples collected in Hainan Island, China. Two of them were isolated from *Miscanthus sinensis* Anderss, and a third one from *Phragmites australis* (Cav.) Trin. ex Steud. Their morphological characteristics and molecular-sequence data are described and discussed below.

## **2. Materials and Methods**

## *2.1. Isolation and Morphology*

Samples were collected from Hainan Province, China (108◦370–117◦500 E, 3 ◦580–20◦200 N). The strains of *Microdochium* were isolated from diseased leaves of *Miscanthus sinensis* and *Phragmites australis* using a tissue-isolation method [25]. Tissue fragments (5 × 5 mm) were taken from the margin of leaf lesions and surface-sterilized by immersing consecutively in 75% ethanol solution for 1 min, 5% sodium hypochlorite solution for 30 s, and then rinsing in sterile distilled water for 1 min [26,27]. The sterilized leaf fragments were dried with sterilized paper towels and placed on potato-dextrose agar (PDA) [28]. All the plates were incubated in a biochemical incubator at 25 ◦C for 3–4 days, after which hyphae were picked out of the periphery of the colonies and transferred onto new PDA plates and oatmeal-agar (OA) [29] plates.

Pure cultures transferred to PDA and OA plates were incubated at 25 ◦C for 15 days and photographed twice at the 7th and 15th days using a Powershot G7X mark II digital camera. Macro- and micromorphological characteristics were observed using an Olympus SZX10 stereomicroscope and an Olympus BX53 light microscope, respectively. These two microscopes were both fitted with an Olympus DP80 high-definition color digital camera to photo-document fungal structures. All fungal strains were preserved at 4 ◦C in sterilized 10% glycerin for further studies. Voucher specimens were deposited in the Herbarium Mycologicum Academiae Sinicae, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China (HMAS) and Herbarium of the Department of Plant Pathology, Shandong Agricultural University, Taian, China (HSAUP).

Living cultures were deposited in the Shandong Agricultural University Culture Collection (SAUCC). Taxonomic information on the new taxa was submitted to MycoBank (http://www.mycobank.org/, accessed on 25 April 2022).

## *2.2. DNA Extraction and Amplification*

Genomic DNAs were extracted from fungal mycelia grown on PDA, using a modified cetyltrimethylammonium bromide (CTAB) protocol as described in Guo et al. [30]. Four pairs of primers were adopted to amplify four genetic markers [2]. Partial nuclear ribosomal large subunit (LSU), entire internal transcribed spacer (ITS) of rDNA, partial beta-tubulin gene (TUB2), and partial RNA polymerase II second-largest subunit (RPB2)

were amplified and sequenced using primer pairs LR0R/LR5 [31], ITS4/ITS5 [32], Btub526F and Btub1332R [12], and RPB2-5F2/fRPB2-7cR [33,34], respectively.

PCRs were performed using an Eppendorf Master Thermocycler (Hamburg, Germany). Amplification reactions were carried out in a volume of 25 µL, containing 12.5 µL 2 × Green Taq Mix (Vazyme, Nanjing, China), 1 µL of each forward and reverse primers (10 µM) (Biosune, Shanghai, China), 1 µL of template genomic DNA (approximately 10 ng/µL), and 9.5 µL of distilled deionized water.

The PCR program consisted of an initial denaturation at 94 ◦C for 5 min, 35 cycles × [denaturation at 94 ◦C for 30 s, annealing at a suitable temperature for 30 s, extension at 72 ◦C for 1 min] and a final elongation at 72 ◦C for 10 min. Annealing temperatures were 55 ◦C for ITS, 51 ◦C for LSU, 56 ◦C for RPB2 and 53 ◦C for TUB2. PCR products were visualized through 1% agarose-gel electrophoresis. Paired-end sequencing was conducted by Biosune Company Limited (Shanghai, China). Sequences were proofread for basic authenticity and reliability according to the five simple guidelines established by Nilsson et al. [35]. Consensus sequences were obtained using MEGA 7.0 [36]. All sequences generated in this study were deposited in GenBank (Table 1).

KAS3574 = DAOM 250155 *Maize kernels* Switzerland KP858973 KP859037 KP859100 KP859146 KAS3158 = DAOM 250161 *Triticum aestivum* Canada KP858970 KP859034 KP859097 KP859143

Notes: New species established in this study are in bold. Ex-types, ex-epitypes or holotype strains are marked with "\*".

## *2.3. Phylogenetic Analyses*

Twenty-eight new sequences were generated in this study, and available reference sequences of *Microdochium* species were retrieved from GenBank [2–7]. Four genetic markers (ITS, LSU, TUB2 and RPB2) were separately aligned using MAFFT v.7.110 (Osaka, Japan) [37]. Phylogenetic analyses were conducted individually for each marker at first and then for a combined dataset of the four genetic markers (Supplementary File S1).

Phylogenetic analyses were conducted with Bayesian inference (BI) and maximumlikelihood (ML) algorithms on the CIPRES Science Gateway portal (https://www.phylo. org/, accessed on 15 April 2022;) [38]. The BI ran with MrBayes on XSEDE v. 3.2.7a (Stockholm, Sweden) [39–41], and the ML ran with RAxML-HPC2 on XSEDE v. 8.2.12 (Heidelberg, Germany) [42]. The best evolutionary model for each partition was determined using MrModelTest v. 2.3 [43]. Default parameters were used for 1000 bootstrap ML analysis. In BI analysis, starting trees were random, and four MCMC chains ran simultaneously for five million generations. Trees were sampled once every 500 generations. These chains stopped when all convergences met and the standard deviation fell below 0.01. The burn-in fraction was set to 0.25 and Posterior Probabilities (PP) were determined from the remaining trees. All resulting trees were plotted using FigTree v. 1.4.4 (http://tree.bio.ed.ac.uk/software/figtree, accessed on 15 April 2022) and the layout of the trees was carried out with Adobe Illustrator CC 2019.

## **3. Results**

## *3.1. Phylogenetic Analyses*

Seven *Microdochium* strains isolated from plant hosts were sequenced. Multilocus data (ITS, LSU, TUB2 and RPB2) were composed of 52 strains of *Microdochium* as ingroup and a strain CBS 204.56 of *Idriella lunata* as outgroup. A total of 2957 characters were fed to the phylogenetic analysis, viz. 1–573 (ITS), 574–1423 (LSU), 1424–2117 (TUB2), and 2118–2957 (RPB2). Of these characters, 2223, 97 and 637 were constant, variable parsimonyuninformative and parsimony-informative, respectively. For the BI and ML analyses, the evolutionary model of GTR+I+G was selected for ITS, TUB2 and RPB2, while SYM+I+G was selected for LSU (Figure 1). The topology of the phylogenetic tree generated by the ML method was highly similar to that by BI, and therefore it was chosen to represent the evolutionary history of *Microdochium*.

The 59 strains are assigned to 29 species clades based on the four-marker phylogeny (Figure 1). The seven strains isolated herein represent three novel species. The new species *M. miscanthi* (SAUCC211092, SAUCC211093 and SAUCC211094) has a sister relationship to another new species, *M. sinense* (SAUCC211097 and SAUCC211098), with robust support values (BIPP 1.00 and MLBV 100%). These two species are closely related to *M. rhopalostylidis* (CBS 145125), *M. phragmitis* (CBS 285.71 and CBS 423.78), *M. lycopodinum* (CBS 146.68, CBS 109397 and CBS 109398), *M. indocalami* (SAUCC1016) and *M. fisheri* (CBS 242.90) with high support values (BIPP 1.00 and MLBV 100%). The last new species, *M. hainanense* (SAUCC210781 and SAUCC210782), forms the sister group of the seven species mentioned above with reasonable support (MLBV 92%).

**Figure 1.** A maximum-likelihood phylogram of *Microdochium* based on combined ITS, LSU, TUB2 and RPB2 sequences with CBS 204.56 of *Idriella lunata* as outgroup. The maximum-likelihood Bootstrap Value (MLBV ≥ 75%) and Bayesian Inference Posterior Probability (BIPP ≥ 0.95) are shown at **Figure 1.** A maximum-likelihood phylogram of *Microdochium* based on combined ITS, LSU, TUB2 and RPB2 sequences with CBS 204.56 of *Idriella lunata* as outgroup. The maximum-likelihood Bootstrap Value (MLBV ≥ 75%) and Bayesian Inference Posterior Probability (BIPP ≥ 0.95) are shown at the first and second position, respectively. Strains marked with "\*" are ex-types, ex-epitypes or holotypes. Strains from the current study are in red. The scale bar at the bottom middle indicates 0.08 substitutions per site.

## *3.2. Taxonomy* 3.2.1. *Microdochium miscanthi* (Figure 2) S.B. Liu, X.Y. Liu, Z. Meng & X.G. Zhang, sp.

substitutions per site.

*3.2. Taxonomy*

*J. Fungi* **2022**, *8*, x FOR PEER REVIEW 8 of 17

*Microdochium miscanthi* (Figure 2) S.B. Liu, X.Y. Liu, Z. Meng & X.G. Zhang, sp. nov. nov.

the first and second position, respectively. Strains marked with "\*" are ex-types, ex-epitypes or holotypes. Strains from the current study are in red. The scale bar at the bottom middle indicates 0.08

**Figure 2.** *Microdochium miscanthi* (holotype HMAS352151, ex-holotype SAUCC211092). (**a**) Leaves of host plant; (**b**) inverse and reverse sides of colony after 15 days on PDA; (**c**) inverse and reverse sides of colony after 15 days on OA; (**d**) a colony overview; (**e**–**h**) conidiophores and conidiogenous cells; (**i**,**j**) conidia. Scale bars: (**e**–**j**) 10 μm. **Figure 2.** *Microdochium miscanthi* (holotype HMAS352151, ex-holotype SAUCC211092). (**a**) Leaves of host plant; (**b**) inverse and reverse sides of colony after 15 days on PDA; (**c**) inverse and reverse sides of colony after 15 days on OA; (**d**) a colony overview; (**e**–**h**) conidiophores and conidiogenous cells; (**i**,**j**) conidia. Scale bars: (**e**–**j**) 10 µm.

MycoBank No.: 843867

Etymology—The epithet "*miscanthi*" refers to the genus name of the host plant *Miscanthus sinensis*.

Type—China, Hainan Province: Diaoluoshan National Forest Park, on diseased leaves of *Miscanthus sinensis*, 21 May 2021, S.B. Liu, holotype HMAS352151, isotype HSAUP211092, ex-holotype living culture SAUCC211092.

Description—Colonies on PDA at 25 ◦C for 14 days attain 87.2–89.1 mm in diameter. When young, round in shape, dark green in the center and white at the edge, with some dark green parts covered with continuously growing mycelia. When old, tight, uneven and pale yellow in the center, fluffy, flat, white at the edge. Mycelia are superficial and immersed, 1.5–2.3 µm wide, transparent, branched and diaphragmatic. Conidiophores are straight or slightly curved, produced on aerial mycelia, septate and often reduced to conidiogenous cells borne directly from hyphae. Conidiogenous cells are mono- or polyblastic, terminal, denticulate, transparent, smooth and cylindrical, 9.7–14.5 × 3.6–4.1 µm. Conidia are solitary, transparent, spindle-to-rod-shaped, 0–2-septate, 7.0–16.1 × 2.5–4.7 µm, 0–5 guttulate when mature and sometimes borne directly from hyphae. Chlamydospores were not observed. Sexual morphs unknown.

Culture characteristics—Colonies on OA at 25 ◦C for 14 days, reach 88.4–89.3 mm in diameter, and are circular, black-green in the center and irregular in shape, covered with a thin layer of white mycelia, dense at the edge and forming a white ring. Substrate hyphae are transparent and smooth. Vegetative hyphae are transparent, smooth, branched and diaphragmatic.

Notes—Strains SAUCC211092, SAUCC211093 and SAUCC211094 are identified as the same new species *Microdochium miscanthi*. They have similar morphological characteristics, including culture characteristics, sporodochia and conidia. They are also the same in DNA sequences, gathering together with robust support values (MLBV 100% and BIPP 1.00, Figure 1). Phylogenetic analyses on a combined dataset of four genetic markers showed that *M. miscanthi*, *M. lycopodinum*, *M. phragmites*, *M. rhopalostylidis*, *M. fisheri* and *M. sinense* formed a clade. *M. miscanthi* and *M. sinense* form sister clades on the phylogenetic trees, but they are different in culture characteristics, conidia and DNA sequences. In *M. miscanthi*, colonies on PDA are overall white, with central dark-green plaque covered by white mycelia; conidiogenous cells are 9.7–14.5 × 3.6–4.1 µm, without diaphragms; conidia are 7.0–16.1 × 2.5–4.7 µm, spindle-to-rod-shaped. In *M. sinense*, colonies are overall pale yellow; conidiogenous cells are 6.3–22.4 × 4.1–5.7 µm, with single or multiple diaphragms; conidia are 11.5–19.34 × 2.8–5.4 µm, spindle-shaped or cylindrical. As for molecular differences between *M. miscanthi* and *M. sinense*, ITS, BTUB, LSU and RPB2 had 10, 21, 2 and 35 bp of dissimilarity, respectively. Therefore, we assign them in two different species. In addition, conidiogenous cells in *M. miscanthi* are terminal or sympodial, denticulate, transparent, smooth and cylindrical, which are similar to the species in this clade. The conidia of *M. miscanthi* (7.0–16.1 × 2.5–4.7 µm) differs in size from those of *M. lycopodinum* (8.0–15.5 × 2.5–4.0 µm), *M. phragmites* (10.0–14.5 × 2.0–3.0 µm), *M. fisheri* (7.0–12.0 × 3.0–4.0 µm) and *M. rhopalostylidis* (16.0–20.0 × 3.0–4.0 µm) [2,5]. Furthermore, mature conidia are guttulate in *M. miscanthi*.

*Microdochium sinense* (Figure 3) S.B. Liu, X.Y. Liu, Z. Meng & X.G. Zhang, sp. nov. 3.2.2. *Microdochium sinense* (Figure 3) S.B. Liu, X.Y. Liu, Z. Meng & X.G. Zhang, sp. nov.

**Figure 3.** *Microdochium sinense* (holotype HMAS352154, ex-holotype SAUCC211097). (**a**) Leaves of host plant; (**b**) inverse and reverse sides of colony after 15 days on PDA; (**c**) inverse and reverse sides of colony after 15 days on OA; (**d**) colony overview; (**e**–**h**) conidiophores and conidiogenous cells; (**i**,**j**) conidia. Scale bars: (**e**–**j**) 10 μm. **Figure 3.** *Microdochium sinense* (holotype HMAS352154, ex-holotype SAUCC211097). (**a**) Leaves of host plant; (**b**) inverse and reverse sides of colony after 15 days on PDA; (**c**) inverse and reverse sides of colony after 15 days on OA; (**d**) colony overview; (**e**–**h**) conidiophores and conidiogenous cells; (**i**,**j**) conidia. Scale bars: (**e**–**j**) 10 µm.

MycoBank—No: 843868 MycoBank—No: 843868

Etymology—The epithet "*sinense*" (Lat.) refers to China, where the species was col-Etymology—The epithet "*sinense*" (Lat.) refers to China, where the species was collected.

lected. Type—China, Hainan Province: *Diaoluoshan* National Forest Park, on diseased leaves of *Miscanthus sinensis*, 21 May 2021, S.B. Liu, holotype HMAS352154, isotype Type—China, Hainan Province: *Diaoluoshan* National Forest Park, on diseased leaves of *Miscanthus sinensis*, 21 May 2021, S.B. Liu, holotype HMAS352154, isotype HSAUP211097, ex-holotype living culture SAUCC211097.

HSAUP211097, ex-holotype living culture SAUCC211097. Description—Colonies on PDA at 25 °C for 14 days attain 87.2‒89.3 mm in diameter; when young, they are irregular in shape, dark green in the center and covered by white Description—Colonies on PDA at 25 ◦C for 14 days attain 87.2–89.3 mm in diameter; when young, they are irregular in shape, dark green in the center and covered by white

hyphae; when old, they are dark green overall, covered completely by white, lush, fluffy and beige hyphae. Mycelia are superficial and immersed, 1.3–2.3 µm wide, transparent, branched and diaphragmatic. Conidiophores are straight or slightly curved, produced from aerial hyphae, septate and often reduced to conidiogenous cells borne directly from hyphae. Conidiogenous cells are monoblastic, terminal, hyaline, smooth and cylindrical, 16.3–22.4 × 4.1–5.7 µm. Conidia are solitary, hyaline, spindle-shaped or cylindrical, 1–3-septate, 11.5–19.34 × 2.8–5.4 µm, 2–9 guttulate when mature and sometimes borne directly from hyphae. Chlamydospores were not observed. Sexual morphs unknown. branched and diaphragmatic. Conidiophores are straight or slightly curved, produced from aerial hyphae, septate and often reduced to conidiogenous cells borne directly from hyphae. Conidiogenous cells are monoblastic, terminal, hyaline, smooth and cylindrical, 16.3‒22.4 × 4.1‒5.7 μm. Conidia are solitary, hyaline, spindle-shaped or cylindrical, 1–3 septate, 11.5‒19.34 × 2.8‒5.4 μm, 2–9 guttulate when mature and sometimes borne directly from hyphae. Chlamydospores were not observed. Sexual morphs unknown. Culture characteristics—Colonies on OA at 25 °C for 14 days, reach 86.4‒88.9 mm in diameter; when young, they are circular gray in the center and wax yellow at the edge;

hyphae; when old, they are dark green overall, covered completely by white, lush, fluffy and beige hyphae. Mycelia are superficial and immersed, 1.3‒2.3 μm wide, transparent,

*J. Fungi* **2022**, *8*, x FOR PEER REVIEW 11 of 17

Culture characteristics—Colonies on OA at 25 ◦C for 14 days, reach 86.4–88.9 mm in diameter; when young, they are circular gray in the center and wax yellow at the edge; when old, they have ravines, dense, yellow-brown overall and fluffy at the edge. Vegetative hyphae are transparent, branched and diaphragmatic. when old, they have ravines, dense, yellow-brown overall and fluffy at the edge. Vegetative hyphae are transparent, branched and diaphragmatic. Notes—Strains SAUCC211097 and SAUCC211098 are identified to the same species *Microdochium sinense* sp. nov. For details, refer to the notes for *M. miscanthi*.

Notes—Strains SAUCC211097 and SAUCC211098 are identified to the same species *Microdochium sinense* sp. nov. For details, refer to the notes for *M. miscanthi*. 3.2.3. *Microdochium hainanense* (Figure 4) S.B. Liu, X.Y. Liu, Z. Meng & X.G. Zhang, sp. nov.

*Microdochium hainanense* (Figure 4) S.B. Liu, X.Y. Liu, Z. Meng & X.G. Zhang, sp. nov.

**Figure 4.** *Microdochium hainanense* (holotype HMAS352156, ex-holotype SAUCC210781). (**a**) leaves of host plant; (**b**) inverse and reverse sides of colony after 15 days on PDA; (**c**) inverse and reverse sides of colony after 15 days on OA; (**d**) sporodochia after removing the surface mycelia; (**e**) the **Figure 4.** *Microdochium hainanense* (holotype HMAS352156, ex-holotype SAUCC210781). (**a**) leaves of host plant; (**b**) inverse and reverse sides of colony after 15 days on PDA; (**c**) inverse and reverse sides of colony after 15 days on OA; (**d**) sporodochia after removing the surface mycelia; (**e**) the mixture of conidia and secretions on mycelium; (**f**–**h**) conidiophores and conidiogenous cells; (**i**–**k**) conidia. Scale bars: (**f**–**k**) 10 µm.

MycoBank—No:843869

Etymology—The epithet "*hainanense*" is named after Hainan Province, where the fungus was collected.

Type—China, Hainan Province: Diaoluoshan National Forest Park, on diseased leaves of *Phragmites australis*, 21 May 2021, S.B. Liu, holotype HMAS352156, isotype HSAUP210781, ex-holotype living culture SAUCC210781.

Description—Colonies on PDA for 14 days attain 75.4–77.2 mm in diameter; when young, they form a conspicuously concentric circle, brown and dense in the center, white and sparse at the edge; when old, they produce sporodochia in aerial mycelia or on agar surface, slimy, hyaline or orange, colorless-to-brownish in reverse due to secreted soluble pigments. Mycelia are superficial and immersed, width 1.5–3.0 µm, transparent, smooth, branched and diaphragmatic. Conidiophores are reduced to conidiogenous cells. Conidiogenous cells are monoblastic, terminal, hyaline, smooth, ampulliform and lageniform, with percurrent proliferations, 4.8–8.2 × 2.0–2.5 µm. Conidia are solitary, hyaline, aseptate and spindle-to-rod-shaped, 7.0–16.1 × 2.5–4.7 µm, 0–8 guttulate when mature. Chlamydospores were not observed. Sexual morphs unknown.

Culture characteristics—Colonies on OA at 25 ◦C for 14 days reach 69.7–71.9 mm in diameter; they are circular, with hyphae mostly immersed in agar and occasionally scattered on the agar surface; light black and sparse in the center, white and dense at the edge. Substrate hyphae are transparent and smooth. Vegetative hyphae are transparent, septate and branched.

Notes—Strains SAUCC210781 and SAUCC210782 are identified to the same new species, *M. hainanense*. They share morphological characteristics, including culture characteristics, sporodochia and conidia. They are also identical in DNA sequences, gathering together with robust support values (MLBV 100% and BIPP 1.00, Figure 1). Phylogenetic analysis of the four genetic markers of *M. hainanense* showed that *M. hainanense* formed an independent branch, sister to the group of *M. indocalami*, *M. sinense*, *M. miscanthi*, *M. rhopalostylidis*, *M. phragmites, M. fisheri* and *M. lycopodinum* with satisfactory support (MLBV 92%, Figure 1). *Microdochium. hainanense* produces sporodochia, similar to *M. phragmitis* (CBS 423.78) and *M. rhopalostylidis*, but *M. hainanense* produces clear-to-orange soluble pigments, while the conidia of other species are directly produced from hyphae. Conidia are single, ellipsoid or spindle-shaped, similar to all the related species mentioned above. Conidia of *M. hainanense* (5.5–8.1 × 2.2–3.0 µm) differ in size from those of *M. lycopodinum* (8.0–15.5 × 2.5–4.0 µm), *M. phragmites* (10.0–14.5 × 2.0–3.0 µm), *M. rhopalostylidis* (16.0–20.0 × 3.0 –4.0 µm), *M. indocalami* (13.0–15.5 × 3.5–5.5 µm), *M. fisheri* (7.0–12.0 × 3.0–4.0 µm), *M. miscanthi* (7.0–16.1 × 2.5–4.7 µm) and *M. sinense* (11.5–19.34 × 2.8–5.4 µm) [2,5].

## *3.3. Key to the Species of Microdochium*

Together with the three new species proposed in this study, we currently accepted a worldwide total of 47 species in the genus *Microdochium*. In order to facilitate identification in the future, a key to the species of *Microdochium* is provided herein, updating the key compiled 46 years ago [15]. Characteristics adopted in the key include perithecia, septa, asci, ascospores, conidiogenous cells, conidia and chlamydospores.

## **4. Discussion**

*Microdochium* was established in 1924, and *Monographella* Petr. also established in 1924 was previously described as a sexual morph of *Microdochium* [16,44–46]. With the application of "one fungus one name" declaration [47], *Microdochium* was retained as the correct genus name because it accommodates more species and is used more frequently [2]. Due to their phylogenetic affinity, *Microdochium*, *Idriella* and *Selenodriella* were introduced into a new family, namely *Microdochiaceae* [2]. This new family is characterized by (1) *Monographella*-like sexual morphs; and (2) asexual morphs of polyblastic, sympodial or annellidic conidiogenous cells with hyaline conidia, but no appendages. As an important basis for classification, conidia of *Microdochium* vary in shape, i.e., cylindrical, fusiform, elliptical, stick-shaped, vertical or curved, with truncate bases and apices mainly rounded.

Since the inception of *Microdochium* in 1924, its delimitation has undergone changes, and currently, 47 species are accepted in the genus. Although the number is small, there are still some problems in the classification. For example, Catalogue of Life accepts the basionym *Gloeocercospora sorghi* rather than the combination *Microdochium sorghi* but without any explanation [10]. It is possibly because *M. sorghi* remains sterile and only produces black sclerotia in culture [2]. However, in this study, the phylogenetic analysis based on the based on four genetic markers showed that *M. sorghi* formed a separated branch closely related to the clade of *M. citrinidiscum* and *M. paspali* with strong support (MLBV: 100% and BIPP: 1.00, Figure 1). Upon this molecular evidence, we accept *M. sorghi* as the correct name for this species.

*Microdochium* is mainly distributed in warm and humid areas, and most prefer to parasitize on gramineous plants. Our finding of the new species *M. miscanthi*/*M. sinense* on *Miscanthus sinensis* (*Poaceae*) and *M. hainanense* on *Phragmites australis* (*Poaceae*), confirms this phenomenon well. Hainan Province is located in the tropical region of southern China. Its annual average temperature is 22–27 ◦C, and its annual precipitation is 1000–2600 mm,

with a typical tropical rainforest climate. This kind of environment is conducive to the growth of unusual microbial species, resulting in a high species diversity.

In order to accurately identify the species of *Microdochium*, molecular analysis is needed. In this study, the four genetic markers ITS, LSU, RPB2 and TUB2 were selected according to previous molecular studies of *Microdochium*. LSU provides enough information for the generic placement of *Microdochium*. Although any of the genetic markers ITS, TUB2 or RPB2 can be used for phylogenetic analysis at the species level in *Microdochium* (results not shown), TUB2 has more phylogenetic information, with longer distances between species and higher support values. This is consistent with previous studies on other xylariaceous genera [2,48,49].

**Supplementary Materials:** The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/jof8060577/s1, Supplementary File S1: The combined ITS, LSU, TUB2, and RPB2 multiple sequence alignment. Table S1: Specimens and GenBank accession numbers of DNA sequences used in this study.

**Author Contributions:** Conceptualization, S.L. and X.L.; methodology, Z.M.; software, Z.Z.; validation, J.X.; formal analysis, X.L.; investigation, S.L.; resources, Z.Z.; data curation, S.L.; writing original draft preparation, S.L.; writing—review and editing, X.L.; visualization, X.L.; supervision, X.Z.; project administration, Z.M.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Natural Science Foundation of China (nos. U2002203, 31750001, 31900014).

**Institutional Review Board Statement:** Not applicable for studies involving humans or animals.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The sequences from the present study were submitted to the NCBI database (https://www.ncbi.nlm.nih.gov/, accessed on 25 April 2022) and the accession numbers were listed in Table 1.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**