Tomas Lagos Jenschke

Remous-Aris Koutsiamanis

Georgios Z Papadopoulos

Nicolas Montavont

Multi-path selection in RPL based on replication and elimination

Keywords: Multi-path Routing, RPL, TSCH, LeapFrog Collaboration

IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is a distance vector routing protocol especially designed for the Internet of Things (IoT). RPL uses broadcast DODAG Information Object (DIO) messages to build a Destination Oriented Directed Acyclic Graph (DODAG) toward a root. Each node selects a parent node toward the root using a common Objective Function (OF). However, the use of a single route can affect the network reliability and the end-to-end latency. In this study, we propose to employ the Packet Replication and Elimination (PRE) principles to use parallel paths toward the DODAG root, over the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) as a medium access. To this aim, we propose number of algorithms to select the second or the alternative parent in RPL. Furthermore, we study the advantages of using overhearing feature over correlated paths. Our simulation campaign conducted over Cooja, the simulator of Contiki OS, demonstrate that the use of overhearing in conjunction with PRE in RPL considerably improves the robustness of a wireless network by providing greater opportunity to a packet to reach its destination.

Introduction

The IoT are set of standards and technologies developed to connect embedded and smart objects to the Internet. Its low cost, environment adaptability and easy development have allowed the massive integration of these technologies in different application domains, such as the smart grid, Intelligent Transport System (ITS) or the Industry 4.0. The use of the IoT in these various applications however requires different quality of service, which is not an inherent features provided by IoT technologies. For example, the Industry 4.0 is especially concerned with the deterministic nature of the network, i.e., to rely on bounded delay and very high delivery ratio.

Many standard solutions have appeared during the last decade for wireless technologies for embedded devices. IEEE 802.15.4 [START_REF] Kurunathan | 15.4e in a Nutshell: Survey and Performance Evaluation[END_REF] is a standard for low energy power devices focused on the industry. This standard is historically based on Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA). Few years ago, the concept called TSCH [START_REF]IEEE. IEEE Standard for Local and metropolitan area networks[END_REF] has emerged, which reduces the Multi-path fading by using frequency hopping. For extended networks where a destination node is out of the radio range of the source node, a routing protocol might be used on top of the Layer 2 technology. RPL is one of the most popular routing protocol for the IoT and is a distance vector routing protocol, which distributes the nodes within a tree hierarchy called DODAG. By default, each node has an option (route) to transmit a packet to a preferred node until it reaches the target. However is has been shown that reliability performance can be mitigated in real environment, and the retransmission schemes employed when a packet is loss introduce significant delay and jitter. In our previous works, we introduced Leapfrog Collaboration (LFC) [START_REF] Papadopoulos | Leapfrog Collaboration: Toward Determinism and Predictability in Industrial-IoT applications[END_REF], [4] which allows nodes to select additional parents to create alternate paths. To do so, we used the PRE concept, in which several copies of a data packet are introduced in a network to increase the probability of reception at the root node. In this paper, we further investigate LFC and study the effect of the parent selection algorithm. We introduce five algorithms for a node to select alternate parents, and compare their performance in the Cooja simulator.

The document is organized into five sections as follows. In Section 2 the RPL protocol, TSCH technique and the PRE mechanism are briefly explained. In Section 3, the different techniques proposed for the Alternative Parent (AP) selection to be evaluated are explained. In Section 4 contains the results obtained from the different methods proposed in Section 3. In Section 5, we find the different works carried out on multi-path in RPL before our work. Finally, Section 6 concludes our paper.

Background

2.1 IEEE802.15.4 TSCH is a recent channel access technique developed for IEEE 802.15.4. Its main objective is to reduce the impact of wireless fading on a network transmission. TSCH is composed of many timeslots that, in turn, are grouped into slotframes. In order to make this work, TSCH schedules a node to do what it has to do in a timeslot. The formula that uses TSCH to do its channel hopping is represented in the Equation 1.

f = F (ASN + chOF ) mod n ch ( 1
)
Where ASN is the slot offset, chOF is the channel offset, F is the channel input and f is the resultant frequency. TSCH is focused exclusively on the MAC layer, allowing the adjustment of time intervals in a wide range of protocols enabled for IPv6. IEEE802.15.4e currently defines the mechanisms necessary to carry out a communication through TSCH but does not establish the construction and maintenance policies of this, so there is a free interpretation.

RPL

RPL is a distance vector routing protocol designed and developed by the IETF working group ROLL [START_REF] Winter | RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks[END_REF]. This protocol organizes the nodes in a DODAG to allow upward traffic from leafs to sensors, and propose different options for the downward routes. To build a DODAG, a DODAG root sends multicast DIO packets with the necessary information so that a node can join the network. A unicast DODAG Advertisement Object (DAO) message is sent upward to propagate destination information and construct downward routes. Within a RPL instance, there is an OF which defines how to combine different metrics to perform parent selection, i.e. choose which neighbor will be the default relay node toward the root. By default, a node has a single Preferred Parent (PP) selected from its parent list. Different metrics can be used:

-Hop Counting (HC): This metric consists of the minimum amount of hops necessary for the message to reach its destination. In contiki OS [START_REF] Contiki | Contiki: The Open Source OS for the Internet of Things[END_REF], the OF0 is designed to find the nearest root. i.e, the minimum number of relay nodes. -Expected Transmission Count (ETX): ETX consists of a link quality metric, based on the average number of retransmissions needed to reach a given neighbor. This does not mean that it will be the shortest route, but it ensures the quality of the transmission links. To reach this objective, the OF uses the following formula defined by [START_REF] Rajalingham | Quality of Service Differentiation for Smart Grid Neighbor Area Networks through Multiple RPL Instances Gowdemy[END_REF]:

ET X ij = |N tx data | |N rx data | × |N tx ACK | |N rx ACK | = |N tx data | |N rx ACK | (2)
Where N tx data is the number of transmitted messages, N rx data is the number of received messages, N tx ACK is the number of transmitted ACKs and N rx data is the number of received ACKs.

6TiSCH

The IETF working group 6TiSCH [START_REF] Watteyne | Using IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) in the Internet of Things (IoT): Problem Statement. IETF[END_REF] is in charge of the interworking between the IEEE802.15.4 TSCH and the IETF upper stack 6LoWPAN, RPL and CoAP.

To make this possible, they are implementing an extra layer over TSCH MAC layer to adapt the scheduling technique to the specificities and requirements of the the upper layers. As is explained in [START_REF] Watteyne | Using IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) in the Internet of Things (IoT): Problem Statement. IETF[END_REF] LLN needs to:

1. Provide a mechanism for two devices to negotiate the allocation and deallocation of cells between them. 2. Provide a mechanism for the device to monitor and manage the capabilities of a node several hops away. 3. Define a mechanism for these different scheduling mechanisms to coexist in the same network.

In this paper we assume a centralized scheduling where controller establishes the transmission and reception slots for all RPL nodes.

PRE

We previously proposed LFC which increases the reliability and the determinism feature of a multi-hop network by using PRE concept. Replication means that several copies of a single packet are generated in the network (either by the source, by intermediate nodes, or both), and Elimination means that each node that receives a duplicates will discard it. In RPL, by default, a node can have one preferred route identified by its PP. To do the replication, LFC adds the use of an alternative route by choosing an AP from its parent set except for its chosen PP.

Once a node has chosen two PPs, it duplicates a packet to both its parents, and then send two copies of the same data packets. Note that the two parents may benefit from overhearing the transmission to the other parent, ever increasing the probability of receiving the packet. A node may select its PP in many different ways, and in the next sections we present five possible algorithms.

Algorithms for multiple parent selection

By default, RPL performs routing by forwarding packets via the selected PP. The selection of a PP amongst multiple candidate parents is made through its OF. This selection consists mainly of the ranking hierarchy, which increases as the DODAG tree becomes deeper. This type of selection avoids communication loops because a node can be a possible PP, if and only if, its rank is lower than that of its children. To find the most suitable route, there are different types of selection metrics, of which we chose to use ETX.

In this section we define different types of AP selection for Multi-path transmission. Note that if none of the potential parents of a node matches the selection process, or if several nodes do, then the one with the second best ETX will be chosen. It is also possible that no AP is selected in the case where a node has only one parent.

Overhearing

To take advantage of the nature of the wireless medium, nodes close to a transmission can overhear a packet. We implemented this feature, that we call overhearing, to let a potential parent receive a packet sent from a child to another parent. This allows a second opportunity for the information to reach its destination.

Within this work, the use of overhearing was incorporated into LFC, i.e., in all cases of Multi-path selection. In addition, we developed another algorithm where we only add the Overhearing to RPL (i.e., RPLO) without doing any duplication.

Second Best ETX (2ETX)

This algorithm consists of obtaining an AP by selecting the node that has a lower ranking than the current node and that has the best link quality among the set of possible parents. In Figure (1a) a selection example is shown by the use of 2ETX where if we define the rankings such as R and link quality as M:

-A R < B R and A R < C R and A R < D R -E R > B R and E R > C R and E R > D R E M + B M + A M = 2.5 ET X E M + C M + A M = 3.1 ET X E M + D M + A M = 2.6 ET X
(3) Since 2.5 ET X is the lowest value, node B will be the PP of node E and node D will be the AP of node E because 2.6 ET X < 3.1 ET X .

Common Ancestor (CA)

A node will select a node as its AP if it has a common ancestor with the PP. It means that the AP and the PP should have a common PP in their Metric Container (MC) [START_REF] Koutsiamanis | RPL DAG Metric Container (MC) Node State and Attribute (NSA) object type extension[END_REF]. If multiple potential parents exhibit the same characteristic, the one with the best ETX will be selected. Minet et al. in [START_REF] Minet | Increasing Reliability of a TSCH Network for the Industry 4[END_REF] call this algorithm the "Triangle Pattern". They also present the "Braided patter" which works similarly to the triangle pattern with the main difference being that the AP also has the chance to have an AP too. In our previous work [START_REF] Papadopoulos | Leapfrog Collaboration: Toward Determinism and Predictability in Industrial-IoT applications[END_REF], we used this algorithm.

Figure (1b) shows an example of CA where node C is the AP of node D and A is the common ancestor (potential parent) of node D and C. In order to implement this algorithm, a node should advertise its list of potential parents to its children. The IETF draft [START_REF] Koutsiamanis | RPL DAG Metric Container (MC) Node State and Attribute (NSA) object type extension[END_REF] provides a proposal and defines a "Type-Length-Value" (TLV) field within the "Node State and Attribute" (NSA) object type in the MC of DIO packets to carry this information.

Non-Common Preferred Ancestor (NCPA)

A node is selected as an AP if it has a different PP than the PP one. This selection mechanism works by storing in the DIO MC the PP address and the receiving child just has to compare the PP address of its potential parents. Figure (2a) illustrates the NCPA operation where node A is the preferred grandparent of node E and node B is the PP of node D. Because A is different from B, D will be the AP of node E.

Non-Common Ancestor (Disjoint)

This algorithm consists in the selection of an AP if the current PP has a PP and AP different than the PP and AP from the potential node. In this way, the composition of a triangular pattern through an alternative node is deactivated, which allows the generation of one or several disjoint patterns according to the built network topology. Like the other selection techniques, the node will classify and select its potential AP ordered by the ETX metric. The Figure (2b) illustrates the operation of the Disjoint selection.

4 Performance evaluation

Simulation environment

In this study, we employed the COOJA network simulator for Contiki OS [START_REF] Contiki | Contiki: The Open Source OS for the Internet of Things[END_REF] to perform the performance evaluation campaign. To this aim, we defined three types of topology illustrated in Figure 3 and configured as shown in Table 1. -Topology 1 -simple use-case, where each node has two potential parents: 12 nodes, where leaf node 1 will be the transmitter and node 12 will be the DODAG root. -Topology 2 -wider network, where each node has three potential parents: 17 nodes, where leaf node 1 will be the transmitter and node 17 will be the DODAG root. -Topology 3 -dense network, where a node can have up to four siblings: 24 nodes, where leaf node 1 will be the transmitter and node 24 will be the DODAG root.

The MC implementation can be found in [START_REF] Koutsiamanis | The Contiki Operating System[END_REF]. The simulation consists in the transmission of 120 packets every 5 seconds. We ran 10 trials for each algorithm and we tested six algorithms: default RPL, RPLO, 2ETX, CA, NCPA and Disjoint described in Section 3. The transmission of a node to its possible parents follows a pattern from left to right. We configured two consecutive timeslots from a node to each upper node in the topology to allow the transmission of one data packet plus a retransmission if this first attempt failed. For some algorithms, we also defined overhearing timeslots, where a node will listen a transmission intended to another node. For example, in timeslot 33, node 6 is sending to node 8, and node 9 is also listening the transmission. The same pattern is also applied for Topology 2 and 3.

Results

Packet Delivery Ratio. We first measure the average Packet Delivery Ratio (PDR), i.e., the percentage of packets that arrive at the DODAG root. As shown in Figure 5, except for RPL, topologies 2 and 3 reach almost 100% PDR while in the case of Topology 1 they approach 95% PDR. Stock RPL does not perform well with around 50% of PDR in all topologies. This highlights the improvement provided by the use of Multi-path and overhearing with respect to the standard implementation.

Delay and jitter. As shown in Figure 3, Topology 1 consists of 12 nodes where each of these has a set of two possible parents with the exception of the last hop. Considering that each timeslot lasts 10 ms, the approximate delay can be calculated with the Equations in 4:

(((P 2 × N ps2 ) + (P 3 × N ps3 ) + 1) × T R) -T = min delay ((P 2 × N ps2 ) + (P 3 × N ps3 ) + N sp ) × T R = max delay ( 4
)
where P is the parent set, N ps are the nodes that have more than a single parent, N sp are the node that have only a single parent, T is the timeslot duration and R is the amount of transmissions plus retransmissions per attempt. packets are still created because there are multiple receivers for each transmission (due to the overhearing). This results in a more uniform distribution of the copies between all siblings at the last hop, as shown in Figure 7. This figure reveals the reception traffic of the DODAG root from each of its 5 children in Topology 3, where the X axis corresponds to timeslots for each transmission from the root's children to the root from left to right. For example, TS 111 and TS 112 correspond to transmission and retransmission of node 19, TS 113 and TS 114 of node 20 and so on. We observe two things: i) RPLO shows less traffic on each children ii) it is more distributed on each children, leading to higher jitter.

Figure 8 represents the percentage of packets received for the first time in each timeslot. Thus, it represents a CDF, i.e., the proportion of packets received in all possible timeslots. We can see that the distribution is concentrated in TS 111 or node 19, which corresponds to a jitter = 0. This phenomenon is due to the nature of the static scheduling. The traffic will behave like a priority queue, since, for each loss within a timeslot, the next attempt is made in the following timeslot. This explains the low jitter variance between the algorithms and the low delay for each of the transmissions.

Because default RPL does not have Multi-path and overhearing, its information receptions are not subject to TS 111 since it is unidirectional. In the case of RPLO and CA, their distribution is focused on the overhearing nodes because RPLO only follows the route of its PP and CA makes its way based on a triangular pattern. If we analyze Figure 8, we can observe a small variation of reception in TS 113 and TS 115 respectively, due to the distributive nature of these two algorithms.

In the case of 2ETX, this can be totally variable because its distribution focuses on the selection of the best link quality, while NCPA and Disjoint seek to expand as much as possible within the DODAG tree. This tends to happen across all the nodes and, in turn, to TS 111.

Related Work

A number of works related with multi-path in RPL have been conducted in the literature, focusing either on improving energy consumption or network reliability. However, to the best of our knowledge, there is no work that investigated the alternative parent and route selection for the Multi-path objective.

In [START_REF] Pavković | Multipath opportunistic RPL routing over IEEE 802[END_REF], they propose the construction of a scheduling architecture to allow the transmission of multiple routes between a node and its different route options. To reach this goal, they adapt the transmit scheduling to resend the data packet to its different neighbors by choosing a randomly free slot and retrieving the used slots of their neighbors nodes. RPL is adapted also to take into account the delay and the packet delivery before a deadline. This is done with the purpose of handling the deadline transmission at each hop that the packet does.

In [START_REF] Minet | Increasing Reliability of a TSCH Network for the Industry 4[END_REF] the authors propose and analyze three different types of patterns. Each pattern is intended to increase the reliability of the communication by using an alternative route in addition to its preferred route. The result of each pattern is the duplication of packets in each node given more chances that the message reaches its destination. Consequently, the increase in reliability is proportional to the number of available paths that the node posses to transmit a packet. The drawbacks of the increase in possible paths is a higher networking overhead and a higher energy consumption.

Conclusion

In this work, we investigated the potentials of PRE scheme over parallel paths towards the DODAG root. To do this aim, we proposed four algorithms, the 2ETX, CA, NCPA and Disjoint where overhearing function was employed, and we compared them against default RPL and RPLO.

Our performance simulation reveals that the PDR improves considerably when there is a means of replication in the medium. Regardless of whether RPLO does not have a AP, its performance is similar to the algorithms with replication. It was also observed that due to the dispersion of NCPA and Disjoint, the use of overhearing replicates the message to the nodes in their majority.

Fig. 1 :

 1 Fig. 1: (a) 2ETX illustration (b) CA illustration

Fig. 2 :

 2 Fig. 2: (a) NCPA illustration (b) Disjoint illustration

Fig. 3 :

 3 Fig. 3: Case 1: Topology with 12 nodes. Case 2: Topology with 17 nodes. Case 3: Topology with 24 nodes.

Fig. 4 :

 4 Fig. 4: Case 1 -TSCH Scheduling.

Figure 4

 4 Figure 4 partially shows the configuration of the scheduling for Topology 1.The transmission of a node to its possible parents follows a pattern from left to right. We configured two consecutive timeslots from a node to each upper node in the topology to allow the transmission of one data packet plus a retransmission if this first attempt failed. For some algorithms, we also defined overhearing timeslots, where a node will listen a transmission intended to another node. For example, in timeslot 33, node 6 is sending to node 8, and node 9 is also listening the transmission. The same pattern is also applied for Topology 2 and 3.

Fig. 5 :

 5 Fig. 5: Packet Delivery Ratio (PDR): percentage of received packets.

Fig. 6 :Fig. 7 :

 67 Fig. 6: (a) Packet delay: time for a packet to cross the network (b) Packet jitter: delay variation. The minimum and maximum delay for the first, second and third topologies are shown in the Equations 5, 6 and 7, respectively.

Fig. 8 :

 8 Fig. 8: Path of the first copy received by the DODAG root.

Table 1 :

 1 Simulation Setup.

			Topology 1	Topology 2	Topology 3
	Topology		Multi-hop		Multi-hop	Multi-hop
	Number of nodes	12		17	24
	SlotFrame length	53		86	121
	Duration	until 120 packets	EB period	4sec
	Traffic	1pkt/5sec		TimeSlot length	10ms
	Routing	RPL		Number of source	1
	MAC	TSCH		Number of channels	1

Acknowledgements

This work was partially performed and supported under the TPI ANR-17-CE10-0007-01 project of the French National Research Agency (ANR).


Aby Streptomyces

Sp Pal114

Adel Aouiche

Atika Meklat

Christian Bijani

Abdelghani Zitouni

Nasserdine Sabaou
email: sabaou@yahoo.fr

Florence Mathieu
email: mathieu@ensat.fr

Toulouse Archive Ouverte

Keywords: Streptomyces, Vineomycin A1, Chaetoglobosin A, Chemical structure, Bioactive compounds

An actinobacteria strain PAL114, isolated from a Saharan soil in Algeria, produces bioactive compounds. Morphological and chemical studies indicated that this strain belongs to the genus Streptomyces. Analysis of the 16S rRNA gene sequence showed a similarity level of 99.8 % with S. griseoflavus LMG 19344 T , the most closely related species. Two bioactive compounds, named P44 and P40, were extracted by dichloromethane from the cell-free supernatant broth and were purified by HPLC. Minimum inhibitory concentrations (MIC) of the compounds were determined against pathogenic and toxigenic microorganisms, most of which are multiresistant to antibiotics. The P40 fraction showed a strong activity especially against Candida albicans, Bacillus subtilis, and Staphylococcus aureus and has lower MIC values than those of P44 against most microorganisms tested. Chemical structures of compounds were determined based on spectroscopic and spectrometric analyses (UV-visible, mass, 1 H, and 13 C NMR spectra). The compounds P44 and P40 were identified as vineomycin A1 and chaetoglobosin A, respectively. Vineomycin A1 is known to be produced by some Streptomyces species. However, chaetoglobosin A is known to be produced only by fungi belonging to the genera Chaetomium, Penicillium, and Calonectria. This is the first time that chaetoglobosin A, known for its antimicrobial, anticancer, and cytotoxic effects, is reported in prokaryotes.

Introduction

Actinobacteria are Gram-positive bacteria with a percentage of guanine-cytosine higher than 55 %, and most of them produce mycelia. They are particularly interesting for their high capacity to produce secondary metabolites with diverse chemical structures [START_REF] Waksman | The Actinomycetes. Classification, identification and descriptions of genera and species[END_REF]). Among these compounds, there are, for example, antivirals, antiparasitics, immunostimulants, and immunosuppressants. However, actinobacteria are especially known for the production of antibiotics [START_REF] Takahashi | Isolation of new actinomycete strains for the screening of new bioactive compounds[END_REF][START_REF] Solanki | Bioactive compounds from marine actinomycetes[END_REF]. Indeed, over 45 % of bioactive molecules of microbial origin are produced by actinobacteria [START_REF] Solecka | Biologically active secondary metabolites from actinomycetes[END_REF]). Among these molecules, about 80 % are produced by species of the genus Streptomyces, the most common genus in the environment [START_REF] Demain | From natural products discovery to commercializa tion: a success story[END_REF][START_REF] Jose | Phylogenetic affiliation, antimicrobial potential and PKS gene sequence analysis of moder ately halophilic Streptomyces sp. inhabiting an Indian saltpan[END_REF]. The antibiotics secreted by this genus may have antibacterial or antifungal activities, or cytostatic and antitumor properties, such as adriamycin and anthramycin [START_REF] Butler | The role of natural product chemistry in drug discov ery[END_REF]). Many of these molecules have found an important therapeutic application [START_REF] Jose | Non streptomycete actinomycetes nour ish the current microbial antibiotic drug discovery[END_REF].

Our previous works showed the richness of Algeria Saharan soil with actinobacteria producers of bioactive compounds [START_REF] Sabaou | Les sols des oasis du Sahara algérien, source d'actinomycètes rares producteurs d'antibiotiques[END_REF]. Therefore, many strains proved to be new species producing new bioactive molecules (Lamari et al. 2002a, b;Zitouni et al. 2004a[START_REF] Zitouni | Nocardiopsis and Saccharothrix genera in Saharan soils in Algeria: isolation, biological activities and partial characterization of antibiotics[END_REF][START_REF] Badji | Antimicrobial compounds produced by Actinomadura sp. AC104 isolated from an Algerian Saharan soil[END_REF][START_REF] Badji | Isolation and partial characterization of antimi crobial compounds from a new strain Nonomuraea sp. NM94[END_REF][START_REF] Merrouche | Dithiolopyrrolone antibiotic formation induced by adding valeric acid to the culture broth of Saccharothrix algeriensis[END_REF][START_REF] Boubetra | Taxonomy and chemical characterization of new antibiotics pro duced by Saccharothrix SA198 isolated from a Saharan soil[END_REF].

A previous work performed on a Saharan actinobacteria strain named PAL114 revealed the production of two bioactive molecules identified as saquayamycins A and C, known as anticancer agents [START_REF] Aouiche | Antimicrobial activity of saquayamycins produced by Streptomyces sp. PAL114 isolated from a Saharan soil[END_REF]). In the present article, which is the continuation of a previously published work, the taxonomy of actinomycete strain and the production, purification, structure elucidation, and bioactivity of two other bioactive molecules produced by the same strain are described.

Materials and methods

Identification of actinobacteria strain PAL114

The actinobacteria strain PAL114 was isolated from a Saharan soil collected from Ghardaïa province, south Algeria [START_REF] Aouiche | Antimicrobial activity of saquayamycins produced by Streptomyces sp. PAL114 isolated from a Saharan soil[END_REF].

The morphological and cultural characteristics were observed by naked-eye examination of 14 day-old cultures grown on various ISP (International Streptomyces Project) media: yeast extract-malt extract agar (ISP2), oatmeal agar (ISP3), inorganic salts-starch agar (ISP4), and glycerol-asparagine agar (ISP5) [START_REF] Shirling | Methods for characterization of Streptomyces species[END_REF], and also on Bennett medium [START_REF] Waksman | The Actinomycetes. Classification, identification and descriptions of genera and species[END_REF]. The micromorphology and sporulation were observed by light microscopy.

For chemotaxonomic analyses, biomass was obtained from a culture grown in shaken ISP2 medium [START_REF] Shirling | Methods for characterization of Streptomyces species[END_REF]) and incubated at 30 °C for 4 days. Analysis of diaminopimelic acid isomers and whole-cell sugar pattern were carried out using the method of [START_REF] Becker | Rapid differentiation between Nocardia and Streptomyces by paper chromatography of whole cell hydrolysates[END_REF] and [START_REF] Lechevalier | Chemical composition as a criterion in the classification of aerobic actinomycetes[END_REF]. Phospholipids were analysed according to the procedures developed by [START_REF] Minnikin | Polar lipid composition in the classification of Nocardia and related bacteria[END_REF].

For the physiological study, 18 tests were used [START_REF] Locci | Streptomyces and related genera[END_REF], including the production of melanoid pigments on ISP6 and ISP7 media, the assimilation of nine carbohydrates as sole carbon source, the degradation of xanthine, the production of nitrate reductase, the sensitivities to sodium chloride (7 % w/v), sodium azide (0.01 % w/v), phenol (0.1 % w/v), and penicillin (10 UI), and the growth at 45 °C.

For molecular analysis, DNA was extracted using the procedure recommended by [START_REF] Liu | Rapid mini preparation of fungal DNA for PCR[END_REF]. PCR amplification of the 16S rRNA gene sequence of strain PAL114 was performed using two primers: 27f (5′-AGAGTTTGATCCTGGC TCAG-3′) and 1492r (5′-GGTTACCTTGTTACGACTT-3′). The 16S rRNA gene sequence was amplified by PCR using an Invitrogen Kit and sequenced. The sequences obtained were compared with sequences present in the public sequence databases and with the EzTaxon-e server [START_REF] Kim | Introducing EzTaxon e: a prokaryotic 16S rRNA Gene sequence database with phylotypes that represent uncultured species[END_REF].

Phylogenetic analyses were conducted using MEGA version 5.0 [START_REF] Tamura | MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods[END_REF]. The 16S rRNA gene sequence of strain PAL114 was aligned using the CLUSTAL W program [START_REF] Thompson | CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighing, position specific gap penalties and weight ma trix choice[END_REF]) against corresponding nucleotide sequences of representatives of the Streptomyces genus retrieved from GenBank. Evolutionary distance matrices were generated as described by [START_REF] Jukes | Evolution of protein molecules[END_REF] and a phylogenetic tree was established using the neighborjoining method of [START_REF] Saitou | The neighbor joining method: a new method for reconstructing phylogenetic trees[END_REF]. The topology of the tree was evaluated by bootstrap analysis [START_REF] Felsenstein | Confidence limits on phylogenies: an approach using the bootstrap[END_REF] using 1,000 resamplings.

Production and purification of antimicrobial compounds

Production of bioactive compounds were conducted in ISP2 broth (malt extract 10 g, yeast extract 4 g, glucose 4 g, distilled water 1,000 mL, pH 7.2). A seed culture was prepared with the same medium and used to inoculate 80 Erlenmeyer flasks of 500 mL, each containing 100 mL of medium. The cultures were incubated on a rotary shaker (250 rpm) at 30 °C. The extraction of active compounds was performed on the fifth day, previously determined as being the day of optimum production. The ISP2 culture broth was centrifuged to eliminate cells. The cell-free supernatant was extracted with an equal volume of dichloromethane. The organic extract was concentrated to dryness by rotary evaporator under a vacuum at a temperature lower than 40 °C. The resulting dry extract was recuperated in 10 mL of methanol and bioassayed against Candida albicans M3, Aspergillus carbonarius M333, and Bacillus subtilis ATCC 6633 by paper disc diffusion method.

Preparative chromatography with silica gel plates (Merck Art. 5735, Kiesselgel 60HF 254-366; 20 × 20 cm) was employed for the partial purification of bioactive product. TLC plates were developed in the solvent system ethyl acetate-methanol (100:15 v/v). The developed plates were air dried overnight to remove all traces of solvents. The separated compounds were visualized under UVat 254 nm (absorbance) and 365 nm (fluorescence), and the active spot was detected by bioautography [START_REF] Betina | Bioautography in paper and thin layer chromatography and its scope in the antibiotic field[END_REF]. The retention factor (Rf) of the active spot was measured.

The final purification of bioactive compound was carried out by Waters reverse phase HPLC using an XBridge C18 (5 μm) column (200×10 mm, Waters) with a continuous linear gradient solvent system from 20 to 100 % methanol in water, a flow rate of 2 mL/min, and UV detection at 220 and 254 nm. All peak fractions were collected and tested against Candida albicans M3, Aspergillus carbonarius M333, and Bacillus subtilis ATCC 6633. The active fractions were re-injected into the HPLC system under the same conditions as previously, until their final purification.

Identification of bioactive compounds

These analyses were made with the pure bioactive compounds. The UV spectra were determined with a Shimadzu UV1605 spectrophotometer. The mass spectra were recorded on an LCQ ion-trap mass spectrometer (Finnigan MAT, San Jose, CA, USA) with nanospray ion electro-spray ionization (ESI) source (positive and negative ion modes). 1 H and 13 C NMR spectroscopy were used for the characterization of two bioactive molecules. NMR sample was prepared by dissolving 3 mg of each compound in 600 μL of CD 3 OD. All spectra were recorded on a Bruker Avance 500 spectrometer equipped with a cryoprobe. All chemical shifts for 1 H and 13 C are relative to TMS using 1 H (residual) or 13 C chemical shifts of the solvent as a secondary standard. The temperature was set at 298 K. All the 1 H and 13 C signals were assigned on the basis of chemical shifts, spin-spin coupling constants, splitting patterns and signal intensities, and by using 1 H-1 H COSY45, 1 H-1 H TOCSY, 1 H-13 C HSQC, and 1 H-13 C HMBC experiments. Gradient-enhanced 1 H COSY45 was realised including 36 scans for per increment. The mixing time for TOCSY was 60 ms and 96 scans per increment were accumulated. 1 H-13 C correlation spectra using a gradient-enhanced HSQC sequence (delay was optimised for 1 J CH of 145 Hz) was obtained with 200 scans per increment. Gradient-enhanced HMBC experiment was performed allowing 62.5 ms for long-range coupling evolution (340 scans were accumulated). Typically, 2,048 t2 data points were collected for 256 t1 increments.

Determination of minimum inhibitory concentrations

Minimum inhibitory concentrations (MIC) of pure bioactive molecules were carried out using conventional agar dilution [START_REF] Oki | Pradimicins A, B and C: new antifungal antibiotics. II. In vitro and in vivo biological activ ities[END_REF]). Thirteen target microorganisms, the majority of which are pathogenic or toxigenic to humans and multiresistant to antibiotics (Table 1), were used. Three bacteria (Bacillus subtilis AT C C 6 6 3 3 , S t a p h y l o c o c c u s a u re u s S 1 , a n d Escherichia coli E195), five filamentous fungi (Aspergillus carbonarius M333, A. flavus AF3, A. ochraceus AO1, Fusarium culmorum FC200, and Penicillium glabrum PG1), and five yeasts (Candida a l bi ca n s M 1 , M 3 , I PA 20 0, a nd I PA 9 8 8, an d Saccharomyces cerevisiae ATCC 4226) were inoculated onto Mueller Hinton medium for bacteria and Sabouraud medium for fungi, containing different concentrations of active compounds (1,2,5,10,20,30,50,75, and 100 μg/mL). After a growth period of 24-48 h at 37 °C for bacteria and 48-72 h at 28 °C for fungi, the plates were examined for growth and the lowest bioactive compound concentration that inhibited the growth of each organism was determined. Mueller Hinton and Sabouraud media, without active compound and inoculated with target organisms, were used as control treatments.

Results and discussion

Identification of strain PAL114

The strain PAL114 showed good growth on ISP2, ISP3, ISP4, ISP5, and Bennett media. The aerial and substrate mycelia were light to medium grey and light brown, respectively. A diffusible pigment with light brown color was produced on ISP2, ISP3, and Bennett media. The strain PAL114 formed a very well-developed aerial mycelium with long spiraled chains containing between 10 and 50 spores per chain. The spores were carried by sporophores and were oval and 1-1.5× 0.6-1 μm in size. The substrate mycelium was nonfragmented.

The chemotaxonomic study of strain PAL114 showed the presence of LL-diaminopimelic acid isomer and glycin in the cell wall. The whole-cell hydrolysates contained noncharacteristic sugars (ribose, glucose, and galactose), typical of cell wall type IC [START_REF] Lechevalier | Chemical composition as a criterion in the classification of aerobic actinomycetes[END_REF]. The phospholipid profile contained phosphatidylethanolamine, corresponding to phospholipid type PII [START_REF] Lechevalier | Chemotaxonomy of aerobic actinomycetes: phospholipid composition[END_REF]. Based on the morphological and chemical characteristics, strain PAL114 was identified to the genus Streptomyces [START_REF] Holt | Bergey's manual of determinative bacteriology[END_REF].

The strain PAL114 used arabinose, fructose, inositol, mannitol, melibiose, rhamnose, and xylose as carbon source, but not sucrose and raffinose. Xanthine was degraded and nitrate reduced. The strain grew at 45 °C in the presence of penicillin (10 UI), phenol (0.1 % w/v), sodium azide (0.01 % w/v), and NaCl (7 % w/v). The melanoid pigments were not produced on ISP6 and ISP7 media.

The alignment of the 16S rRNA gene sequence (1471 nucleotides) of strain PAL114 with those of Streptomyces reference species available in the GenBank database, can be seen in the neighbor-joining dendrogram (Fig. 1). The similarity level was 99.8 % with S. griseoflavus LMG 19344 T [START_REF] Holt | Bergey's manual of determinative bacteriology[END_REF], the most closely related species. The morphological and physiological properties of strain PAL114 are similar to those of the type strain of S. griseoflavus except for the tests of nitrate reduction and growth at 45 °C.

Purification of bioactive compounds

The dichloromethane extract was chromatographed by TLC and developed in an ethyl acetate-methanol system (100-15 v/v). After migration, we observed one active bioautographic compound, which showed a strong activity against Candida albicans M3 and Bacillus subtilis ATCC 6633. This compound (Rf=0.9), was selected and analyzed by HPLC. Two active fractions (P44 and P40) were purified with 80 % methanol in water. The fraction P40 was eluted at a retention time of 39.68 min and P44 at 44.08 min. A quantity of 5 mg was obtained for each molecule from 8 L of culture filtrate.

Identification of bioactive compounds

The UV-VIS spectrum of the bioactive compound P44 showed maxima at 218, 318, and 438 nm.

The mass spectrum of the compound was obtained in negative mode. It yielded a pseudo-molecular ion [M -H] = 933. Thus, the molecular weight of antimicrobial compound is M=934.

The 1 H and 13 C chemical shifts of P44 compound are given in Table 2 and structure in Fig. 2. The HSQC and HMBC spectra show 49 carbon signals for P44 molecule. It was possible to discern five ketone groups (δ c 182.30 to 205.28), four hydroxyl groups (δ c 71.13 to 157.40), nine ether functions (δ c 67.34 to 95.13), 13 sp 2 -hybridized carbons (δ c from 114.00 to 144.09), and 13 sp 3 -hybridized carbons (δ c 14.04 to 42.61) for the P44 molecule. The hydrogens of the hydroxyl group are not observed due to rapid exchange with MeOD. The 2D 1 H-1 H and 1 H-13 C experiments, and especially the long range 1 H-13 C couplings observed in the HMBC spectrum (see Fig. 2b), permitted us to established the connectivity between all the groups of the molecule.

The structure of the P44 molecule was determined by NMR and mass spectrometry to be vineomycin A1. It is an antibiotic belonging to the aquayamycin group, class of angucycline, family of anthracycline [START_REF] Maskey | Himalomycin A and B: isolation and structure elucidation of new fridamycin type antibiotics from a marine Streptomyces isolate[END_REF]. It is known to be active against Gram-positive bacteria and sarcoma-180 solid tumors in mice [START_REF] Imamura | Biosynthesis of vineomycins A1 and B2[END_REF][START_REF] Omura | Microbial metabolites: 45 years of wandering, wonder ing and discovering[END_REF]). Also, vineomycin A1 is a suitable treatment for hypertrophic scar tissue and keloid disease. It is known to be a potent inhibitor of prolyl 4-hydroxylase, an essential enzyme involved in the post translational modification of collagen causing several diseases such as fibrosis, arteriosclerosis, and scleroderma [START_REF] Cunliffe | Inhibition of prolyl 4 hydroxylase by hydroxyanthraquinones[END_REF][START_REF] Chen | Methodologies for the synthesis of functionalized naph thols and progress toward the total synthesis of vineomycinone B2 methyl ester and actinophyllic acid[END_REF]). Furthermore, cytostatic activity in vivo has been reported for vineomycins [START_REF] Rohr | Angucycline group antibiotics[END_REF].

Vineomycins are known to be produced by actinobacteria, especially Streptomyces species such as S. matensis and S. albogriseolus [START_REF] Ono | Maridomycin, a new macrolide antibi otic. VII[END_REF][START_REF] Chen | Methodologies for the synthesis of functionalized naph thols and progress toward the total synthesis of vineomycinone B2 methyl ester and actinophyllic acid[END_REF][START_REF] Omura | Microbial metabolites: 45 years of wandering, wonder ing and discovering[END_REF]), but have never been reported in S. griseoflavus, to which our strain was closely related.

For the P40 compound, the UV-VIS spectrum showed maxima at 220 and 276 nm.

The mass spectrum of the compound was obtained in negative mode. It yielded a pseudo-molecular ion [M -H] = 527. Thus, the molecular weight of antimicrobial compound is M=528. The 1 H and 13 C chemical shifts of the P40 compound are given in Table 3 and structure in Fig. 3. The HSQC and HMBC spectra show 32 carbon signals for P40. From these data, it was possible to discern two ketone groups ). The hydrogens of the hydroxyl group are not observed due to rapid exchange with MeOD. The 2D 1 H-1 H and 1 H-13 C experiments, and especially the long range 1 H-13 C couplings observed in the HMBC spectrum (see Fig. 3b), permitted us to establish the connectivity between all the groups of the P40 molecule.

The structure of the P40 compound was determined by NMR and mass spectrometry to be chaetoglobosin A. This compound is known for its antibacterial, antifungal, phytotoxic [START_REF] June | Isolation and structural determination of anti Helicobacter pylori compound from fungus 60686[END_REF][START_REF] Larsen | Phenotypic taxonomy and metabolite profiling in microbial drug discovery[END_REF][START_REF] Zhang | Antifungal metabolites produced by chaetomium globosum No.04, an endo phytic fungus isolated from Ginkgo biloba[END_REF], anticancer, and cytotoxic effects towards mammal cells [START_REF] Fisvad | Mycotoxins, drugs and other extrolites produced by species in Penicillium sub genus Penicillium[END_REF][START_REF] Fogle | Growth and mycotoxin production by Chaetomium globosum is favored in a neutral pH[END_REF], and nematicidal effects [START_REF] Hu | Nematicidal activity of chaetoglobosin A poduced by Chaetomium globosum NK102 against Meloidogyne incognita[END_REF]). It has also been reported that this compound increases fibrinolytic activity in bovine animals [START_REF] Shinohara | Enhancement of fibrinolytic activity of vascular endothelial cells by chaetoglobosin A, crinipellin B, geodin and triticone B[END_REF]. This compound is a cytochalasin derivative [START_REF] Ohtsubo | Acute toxic effects of chaetoglobosin A, a new cytochalasan compound pro duced by Chaetomium globosum, on mice and rats[END_REF][START_REF] June | Isolation and structural determination of anti Helicobacter pylori compound from fungus 60686[END_REF]). Furthermore, it is considered a mycotoxin, and it is known to be produced only by fungi such as Chaetomium globosum [START_REF] Hu | Nematicidal activity of chaetoglobosin A poduced by Chaetomium globosum NK102 against Meloidogyne incognita[END_REF], Penicillium discolor, P. expansum, P. marinum (Fisvad et al.

Minimum inhibitory concentrations

Minimum inhibitory concentrations (MIC) of bioactive molecules purified by HPLC are summarized in Table 4.

For vineomycin A1 (P44), MIC values were between 50 and 75 μg/mL for yeasts, and 75 and 100 μg/mL for filamentous fungi. For bacteria, Bacillus subtilis ATCC 6633 was the most sensitive (50 μg/mL). All other bacteria tested were resistant.

For chaetoglobosin A (P40), MIC values were between 30 and 75 μg/mL for yeasts, 50 and 75 μg/mL for filamentous fungi, and 20 and 30 μg/mL for Gram-positive bacteria. All Gram-negative bacteria tested were resistant. The most sensitive microorganisms were Bacillus subtilis ATCC 6633 (20 μg/mL), Staphylococcus aureus S1 (30 μg/mL), and Candida albicans M3 (30 μg/mL).

In conclusion, the strain PAL114, closely related to S. griseoflavus, showed an antibacterial and antifungal activity against pathogenic and toxigenic microorganisms, most of which are resistant to several antibiotics. The bioactive compounds produced by the strain proved to be the vineomycin A1 and chaetoglobosin A.

Vineomycin A1 belongs to the same group, class, and family as saquayamycins [START_REF] Aouiche | Antimicrobial activity of saquayamycins produced by Streptomyces sp. PAL114 isolated from a Saharan soil[END_REF]; it differs from these compounds only by the presence of a saccharide derivative bonded to the carbon 40. Chaetoglobosin A is known to be produced only by a small number of fungal species. This is the first time that chaetoglobosin A, which, in addition to its antimicrobial activity, is also considered a mycotoxin in the scientific literature, is produced by prokaryotes such as Actinobacteria.

Moreover, strain PAL114 represents a sample which confirms the potential of actinobacteria to produce a large variety of bioactive molecules.

Fig. 1

 1 Fig. 1 Neighbor joining tree based on 16S rRNA gene sequences show ing the relation between strain PAL114 and type species of the genus Streptomyces. The numbers at the nodes indicate the levels of bootstrap

Fig. 2

 2 Fig. 2 Structure of bioactive compound P44 (a) and HMBC and COSY correlations (b)

(

  δ c 197.3 and 200.5), one amide group (δ c 174.3), one hydroxyl group (δ c 81.5), one epoxy group (δ c 58.0 and 62.1), 14 sp 2 -hybridized carbons (δ c from 108.0 to 136.6), and 14 sp 3 -hybridized carbons (δ c 12.1 to 81.5

Fig. 3

 3 Fig. 3 Structure of bioactive compound P40 (a) and HMBC and COSY correlations (b)

Table 1

 1 Resistance pattern of target fungi and bacteria to antibiotics

	Target microorganisms	Resistant to:		Sensitivity to:
	Yeasts and filamentous fungi
	Saccharomyces cerevisiae ATCC 4226	N, I, Th, T		C, A
	Candida albicans M1, M3, IPA200 and IPA988 N, I, Th, T, C		A
	Penicillium glabrum PG1	C		N, I, Th, T, A
	Aspergillus carbonarius M333	C, N		I, Th, T, A
	A. flavus AF3	C		N, I, Th, T, A
	A. ochraceus AO1	C, N, T		I, Th, A
	Fusarium culmorum FC200	C, N, T, I, A		Th
	Bacteria
	Bacillus subtilis ATCC 6633	NEO		CH, CAR, CHL, ERY, K, SPI, SSS, VAN
	Staphylococcus aureus S1	CAR, K, NEO, OLE, SPI, VAN	CH
	Escherichia coli E195	AMC, AMX, CAZ, CF, CTX, CXM, FOX, TCC, TIC CH, CXC
	A, amphotericin B; C, cycloheximide; I, itraconazole; N, nystatin; T, thioconazole; Th, terbinafine; AMX, amoxicillin; AMC, amoxicillin + clavulanic
	Streptomyces labedae NBRC 15864	T (AB184704)
	Streptomyces erythrogriseus LMG 19406	T (AJ781328)
	Streptomyces griseoincarnatus LMG 19316	T (AJ781321)
	Streptomyces variabilis NBRC 12825	T (AB184884)
	Streptomyces griseorubens NBRC 12780	T (AB184139)
	Strain PAL114
	Streptomyces griseoflavus LMG 19344	T (AJ781322)
				Streptomyces malachitofuscus NBRC 13059	T (AB184282)

acid; CH, chloramphenicol; CAR, carbenicillin; CAZ, ceftazidime; CF, cefalotin; CTX, cefotaxime; CXC, cefotaxime + clavulanic acid; CXM, cefuroxime; ERY, erythromycin; FOX, cefoxitin; K, kanamycin; NEO, neomycin; OLE, oleandomycin; SPI, spiramycin; SSS, sulfamide; TCC, ticarcillin + clavulanic acid; TIC, ticarcillin; VAN, vancomycin 0.001 Streptomyces althioticus NRRL B 3981 T (AY999791)

Table 2 1

 2 H and 13 C NMR data assignments of P44 compound in CD 3 OD at 298K. See Fig.2bfor numbering of hydrogen and carbon atoms

				Table 2 (continued)
				1 H and 13 C number 1 H chemical shift, ppm 13 C chemical shift, ppm
	1 H and 13 C number 1 H chemical shift, ppm 13 C chemical shift, ppm	48	6.09	126.18
	1	1 . 3 3	1 4 . 0 4	49	5.35	95.13
	2	4 . 6 1	7 0 . 2 1
	3		197.28
	4	7 . 0 4	1 4 4 . 1 9
	5	6 . 0 8	1 2 6 . 1 3
	6	5 . 3 4	9 5 . 1 3
	7	1 . 2 5	1 6 . 1 0
	8	4 . 2 4	6 6 . 3 3
	9	3 . 7 1	7 6 . 4 0
	10	1.51 1.69	24.40
	11	1.97	23.93
	12	5.25	92.10
	13	1.39	24.90
	14		81.95
	15	1.99 2.32	42.61
	16		79.80
	17		79.50
	18		205.28
	19	2.73 2.93	50.18
	20	6.90	116.65
	21	6.41	145.40
	22		139.03
	23		138.50
	24		182.30
	25		137.68
	26		130.97
	27		182.30
	28		157.40
	29		114.00
	30	7.89	132.84
	31	7.62	118.63
	32	1.38	17.39
	33	3.56	75.18
	34	3.15	86.45
	35	3.82	71.13
	36	1.41 2.49	39.28
	37	4.92	70.98
	38	1.23	15.72
	39	4.37	67.34
	40	3.76	76.28
	41	1.99 2.11	23.93
	42	1.51 1.69	24.40
	43	5.01	98.92
	44	1.33	14.04
	45	4.61	70.21
	46		197.34
	47	7.02	144.09

Table 3 1

 3 H and 13 C NMR data assignments of the P40 compound in CD 3 OD at 298 K. See Fig.3bfor numbering of hydrogen and carbon atoms

	1 H and 13 C number 1 H chemical shift, ppm 13 C chemical shift, ppm
	1	6 . 9 5	1 2 4 . 2
	2		108.0
	3		127.5
	4		136.6
	5	7.26	111.3
	6	7.49	118.1
	7	7.00	118.4
	8	7 . 0 3	1 2 0 . 7
	9	2 . 8 4	3 2 . 1
	10	3.91	57.7
	11	2.89	46.4
	12		63.1
	13		174.3
	14	1.73	36.3
	15		58.0
	16	2.78	62.1
	17	2.05	48.9
	18	1.08	12.1
	19	1.30	18.4
	20	6.04	128.3
	21	5.13	132.7
	22	2.00 2.29	41.3
	23	2.47	31.8
	24	5.53	139.2
	25		132.3
	26	5.00	81.5
	27		200.5
	28	6.13	131.7
	29	7.27	134.4
	30		197.3
	31	1.00	19.8
	32	1 33	9.5

Table 4

 4 Minimum inhibitory concentrations (MIC) of the bioactive compounds P44 and P40 secreted by the strain PAL114 against several fungi and bacteria Calonectria morganii[START_REF] Wallbrunn | Phytotoxic chaetoglobosins are produced by the plant pathogen Calonectria morganii (anamorph Cylindrocladium scoparium)[END_REF]). It has never been described in prokaryotes. This is the first time that this compound is reported in the genus Streptomyces, belonging to the class of Actinobacteria. All experiments were made three times at different cultivation times to confirm fully the production of chaetoglobosin A by strain PAL114.

	Target microorganism	MIC (μg/mL) a
		P40	P44
	Saccharomyces cerevisiae ATCC 4226	50	50
	Candida albicans M1	50	75
	C. albicans M3	30	75
	C. albicans IPA200	50	75
	C. albicans IPA988	50	75
	Aspergillus carbonarius M333	50	100
	A. flavus AF3	75	100
	A. ochraceus AO1	50	75
	Fusarium culmorum FC200	75	100
	Penicillium glabrum PG1	75	75
	Bacillus subtilis ATCC 6633	20	50
	Staphylococcus aureus S1	30	>100
	Escherichia coli E195	>100	>100

a MIC values represent the average of two replicates 2004), and