
This review addresses the topic of biofilms, including their development and the interaction between different counterparts. There is evidence that various diseases, such as cystic fibrosis, otitis media, diabetic foot wound infections, and certain cancers, are promoted and aggravated by the presence of polymicrobial biofilms. Biofilms are composed by heterogeneous communities of microorganisms protected by a matrix of polysaccharides. The different types of interactions between microorganisms gives rise to an increased resistance to antimicrobials and to the host's defense mechanisms, with the consequent worsening of disease symptoms. Therefore, infections caused by polymicrobial biofilms affecting different human organs and systems will be discussed, as well as the role of the interactions between the gram-negative bacteria Pseudomonas aeruginosa, which is at the base of major polymicrobial infections, and other bacteria, fungi, and viruses in the establishment of human infections and diseases. Considering that polymicrobial biofilms are key to bacterial pathogenicity, it is fundamental to evaluate which microbes are involved in a certain disease to convey an appropriate and efficacious antimicrobial therapy.
Citation: Manuela Oliveira, Eva Cunha, Luís Tavares, Isa Serrano. P. aeruginosa interactions with other microbes in biofilms during co-infection[J]. AIMS Microbiology, 2023, 9(4): 612-646. doi: 10.3934/microbiol.2023032
[1] | Tingxi Wen, Hanxiao Wu, Yu Du, Chuanbo Huang . Faster R-CNN with improved anchor box for cell recognition. Mathematical Biosciences and Engineering, 2020, 17(6): 7772-7786. doi: 10.3934/mbe.2020395 |
[2] | Wei-Min Zheng, Qing-Wei Chai, Jie Zhang, Xingsi Xue . Ternary compound ontology matching for cognitive green computing. Mathematical Biosciences and Engineering, 2021, 18(4): 4860-4870. doi: 10.3934/mbe.2021247 |
[3] | Carlos Castillo-Chavez, Bingtuan Li, Haiyan Wang . Some recent developments on linear determinacy. Mathematical Biosciences and Engineering, 2013, 10(5&6): 1419-1436. doi: 10.3934/mbe.2013.10.1419 |
[4] | Sanket Desai, Nasser R Sabar, Rabei Alhadad, Abdun Mahmood, Naveen Chilamkurti . Mitigating consumer privacy breach in smart grid using obfuscation-based generative adversarial network. Mathematical Biosciences and Engineering, 2022, 19(4): 3350-3368. doi: 10.3934/mbe.2022155 |
[5] | Hasan Zulfiqar, Rida Sarwar Khan, Farwa Hassan, Kyle Hippe, Cassandra Hunt, Hui Ding, Xiao-Ming Song, Renzhi Cao . Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method. Mathematical Biosciences and Engineering, 2021, 18(4): 3348-3363. doi: 10.3934/mbe.2021167 |
[6] | Bochuan Du, Pu Tian . Factorization in molecular modeling and belief propagation algorithms. Mathematical Biosciences and Engineering, 2023, 20(12): 21147-21162. doi: 10.3934/mbe.2023935 |
[7] | Liang Yu, Zhengkuan Zhang, Yangbing Lai, Yang Zhao, Fu Mo . Edge computing-based intelligent monitoring system for manhole cover. Mathematical Biosciences and Engineering, 2023, 20(10): 18792-18819. doi: 10.3934/mbe.2023833 |
[8] | Ruiping Yuan, Jiangtao Dou, Juntao Li, Wei Wang, Yingfan Jiang . Multi-robot task allocation in e-commerce RMFS based on deep reinforcement learning. Mathematical Biosciences and Engineering, 2023, 20(2): 1903-1918. doi: 10.3934/mbe.2023087 |
[9] | Michele La Rocca, Cira Perna . Nonlinear autoregressive sieve bootstrap based on extreme learning machines. Mathematical Biosciences and Engineering, 2020, 17(1): 636-653. doi: 10.3934/mbe.2020033 |
[10] | Qi Cui, Ruohan Meng, Zhili Zhou, Xingming Sun, Kaiwen Zhu . An anti-forensic scheme on computer graphic images and natural images using generative adversarial networks. Mathematical Biosciences and Engineering, 2019, 16(5): 4923-4935. doi: 10.3934/mbe.2019248 |
This review addresses the topic of biofilms, including their development and the interaction between different counterparts. There is evidence that various diseases, such as cystic fibrosis, otitis media, diabetic foot wound infections, and certain cancers, are promoted and aggravated by the presence of polymicrobial biofilms. Biofilms are composed by heterogeneous communities of microorganisms protected by a matrix of polysaccharides. The different types of interactions between microorganisms gives rise to an increased resistance to antimicrobials and to the host's defense mechanisms, with the consequent worsening of disease symptoms. Therefore, infections caused by polymicrobial biofilms affecting different human organs and systems will be discussed, as well as the role of the interactions between the gram-negative bacteria Pseudomonas aeruginosa, which is at the base of major polymicrobial infections, and other bacteria, fungi, and viruses in the establishment of human infections and diseases. Considering that polymicrobial biofilms are key to bacterial pathogenicity, it is fundamental to evaluate which microbes are involved in a certain disease to convey an appropriate and efficacious antimicrobial therapy.
Nowadays, as the Industrial Internet of Things (IIoT) is racing ahead in smart cities, various industries make full use of CGC to get more economic value and greater profits [1,2,3]. Meanwhile, green computing advocates producing minimal carbon footprint, and energy optimization for the long-term sustainable development of the power enterprise. As the driving force of the IIoT system in the smart city, the energy industry utilizes the CGC to improve the utilization of the limited electrical energy resources and alleviate the energy crisis. As the core part of the IIoT system, the smart grid utilizes NILMI technology to deeply mine the power consumption information of the various electrical equipment and to provide efficient technical support for optimizing the net-side and intelligent management of the load-side [4,5,6]. Utilizing the concept of CGC, NILMI is an efficient way to improve the system performance of the smart grid by monitoring and identifying the large masses of electrical load. Meanwhile, the rapid development of machine learning, using deep learning (DL) technology to address the bottleneck problems of NILMI, has gradually become a hot topic, which can improve the performance and stability of the NILMI model [7,8]. However, the accuracy and the reliability of the NILMI model are still the challenges, which have hindered the development of CGC in the smart grid. The existing methods have low precision of the load recognition for the multiple sources load sampled data, which leads to the poor performance of NILMI in the energy IoT system [9,10]. There are several reasons: the accuracy of the online load identification is affected by the incomplete load feature representation; the stability of the model is poor in the load dynamics consumption, especially in the superimposed scenario of the multiple electrical devices [11,12]. To this end, we construct an algorithm framework of MCTS RL-based, and this model framework is a heuristic strategy optimization way to realize the identification and prediction of load power consumption in the online scenario, which can improve the performance of CGC in the smart grid.
By contrast, the non-intrusive approach is more competitive in the energy IoT system based on CGC [13,14]. They only rely on the measured value of the load waveform (current, voltage, and power signal) as the available critical for load identification. {Further enhancement of the discriminability} of the load features can improve the efficiency of decision optimization of the identification model [15,16]. So, plenty of literature and research have taken a lot of studies for extracting the discriminative load features of these load waveform signals; the majority of existing methodologies include the statistical optimization method, artificial neural networks (ANN) method, and DL algorithms [17,18,19].
At present, DL-based methods are divided into supervised learning and unsupervised learning. Supervised learning used a lot of training data set to obtain an optimal model, and to map all the input to the corresponding output [20,21,22]. The several common approaches include k-Nearest Neighbor (kNN) [23,24], artificial neural networks (ANN) [25], and support vector machines (SVM) [26]. In [23], a hybrid structure based on an improved weighted kNN and the overshoot multiples is employed to train the sampled data sets, and this method enhances the identification accuracy of the load sampled data sets on a small scale. A similar method is mentioned in the other literature, the combination of kNN and the similar time window (STW) algorithm is proposed, but this method still has a limited scope of application. In [27], the author utilizes the SVM model with Dempster-Shafer evidence theory for the load identification, but the influence of the computational complexity on model performance is not considered. In [28], the author exploits an improved ANN algorithm to design the parallel computing model and optimize the model performance of NILMI; finally, the result shows that the method can reduce the heavy calculation in the initial stage of the training model.
Considering the advantages of the convolutional neural networks (CNNs) for image recognition, plenty of literature have proved that the CNN model has demonstrated their advancement in load identification and energy prediction [29,30], especially the various recursive neural network (RNN) [31,32] algorithms. However, supervised learning methods still have the following problems: 1) a large number of annotated data sets are needed for training the model, which will lead to the computational burden; 2) there is the risk of over-reliance on prior knowledge, and it is also easier for a final model performance limitation; 3) samples imbalance result in the poor identification effect and the over-fitting; 4) there are the local optimization problems in online load identification.
By comparison, unsupervised learning is not limited to modeling the prior knowledge, but utilizing the directly unlabelled data sets modeling. The most commonly-used algorithms include clustering analysis (CA) [33,34] and Hidden Markov Model (HMM) [35] algorithms. These methods are attractive in training the general-purpose system on the small data sets and do not require annotation; they take advantage of the deployment capabilities of the environment to simplify the operations process, which is a very effective way of improving the performance of CGC. In [36], a modified fuzzy clustering algorithm is employed to achieve the load monitoring in NILMI, and the simulation results show that the method demonstrates excellent monitoring performance. In [37], a Markov-based model is constructed to achieve the energy consumption identification in the household; the method utilizes the cost of minimizing the computational power to improve the algorithm performance. However, unsupervised learning methods still have their limitations: when the load features space is large, the performance of recognition and prediction algorithms is not very good; the model performance is unstable, and the better the clustering effect, the lower the identification accuracy, especially in the complex combined load signal; the limited capacity of feature learning restricts the improvement of load recognition accuracy.
To address the mentioned problems, we present a novel algorithm framework based on RL, namely MCTS-CI. MCTS-CI aims to improve the stability and performance of the combined load online identification model in a self-learning way. This framework is an online optimization algorithm and can realize independent learning by using various related strategies. In the MCTS-CI model, we design a fusion DNN model to extract the load feature and utilize the recurrent neural network based on time sequence to complete the load spatio-temporal representation in the frequency domain, which can enhance the representation learning ability of the online combined load. To improve the accuracy of the online combined load identification in the self-learning way, we employ MCTS to build an optimized strategy network. An attentional mechanism is introduced into the structure of DNN, namely ATM-DNN, as the initial policy network of MCTS to replace the random selection, which can only focus on the specific feature of load and ignore the irrelevant feature information.
The highlight contributions of our research include the following aspects:
● Discriminative load features learning. Building ATM-DNN model to enhance the learning ability of the discrimination load feature, which mainly focuses on the specific information.
● Comprehensive load-shared knowledge base. Designing the knowledge base of load power facilities (LPF-KB) to facilitate the load collection and features storage, which can function as an auxiliary means to help the formation of the load feature mapping.
● Optimal decision mechanism. MCTS model is proposed to establish a strategic network and to obtain the optimal decision by self-learning way for improving the accuracy of the multi-load identification.
The rest of the four sections are arranged as follows: the related work and the systematical arrangement for the paper are presented in Section II. The algorithm framework of MCTS-CI is introduced in Section III. In Section IV, the experimental details, the criterion of performance evaluation, and the discussion of the results that compared with other methods are described. The conclusion of the article is demonstrated in Section V.
This chapter mainly reviews the related studies of online load identification and then presents the applications of RL-based MCTS.
NILMI is a very effective method to improve the efficiency of load-side management in the smart grid. Especially, the applications of DL algorithms have brought more competitive effects to NILMI.
In [38], Hilbert Transform long short-term memory (HT-LSTM) model is used to improve the accuracy of load identification, and to reduce the fluctuations of the transient signal in the identification results. However, this method is only suitable for a small load sample space, and it will weaken the generalization of the model. To this end, a model based on data augmentation is presented to produce the synthetic load data for the target device, which is aim at the status switching of the appliance [39]; the results prove that using synthetic data can enhance the generalization performance of the model. Some studies concentrate on the high-quality load features learning, which determines the accuracy of load identification. In [31], a two-layer DL model based on the attention mechanism is employed to address the matter of focus from the load feature learning; the results indicate that the attention mechanisms are more advantageous for enhancing the model robustness, which ensures the stability of the model in the load combination scenarios. Multi-load combination identification and power forecasting are more challenging compared to one-class load. In [40], an improved hidden Markov model based on a Viterbi algorithm (VA) is used to achieve the multi-load identification, and the method can effectively preserve the temporal correlation of the electrical load.
Recently, the relevant literature shows that online load power prediction has gradually become a challenging research topic. In [41], the author utilizes a hybrid algorithm architecture of Fisher information theory and the online support vector regression (OSVR) to improve the load prediction accuracy. However, It is incredible to accurately capture the dynamic load features in the online operating mode by using the trained model on offline load data sets. In [42], the author uses the hidden Markov model to present an adaptive online learning method, and the method is used to update the model parameters recursively; the optimal parameters are applied to the load energy consumption prediction; the experimental result shows that the method can improve the reliability of load prediction performance. The same as a Markov decision process, another article proposes a deep RL framework to realize the energy fine-tuning and forecasting [43]. Experiments prove that the method can overcome the uncertainty of the load energy forecasting effectively, and optimize the design of the continuous-control model parameter in high-dimensional variables. As an improved Markov decision process, RL is gradually used for the online load identification and power consumption prediction, and to improve the efficiency of energy management [44,45,46].
Commonly, the RL model is used to describe and solve the maximum returns between an agent and the environment. RL is also applied to solve a balance between the exploration and the exploitation in the online learning of load feature [47,48,49]. Compared to supervised learning and unsupervised learning, RL does not rely on the pre-trained model, but it is the sequential decision-making to require a continuous selection of actions, and then to achieve the maximum benefit from completing these actions as the best outcome. In this process, the learning strategies are built, and then the parameters are updated [50]. RL has been widely used in various fields and has produced remarkable results for NILMI.
As a heuristic search algorithm in the decision-making process, MCTS has been used as an assessment strategy for RL. For instance, AlphaGo is the classic application of MCTS [51]. In recent years, much literature present that MCTS can make the optimal decision-making in dealing with online dynamically changing data. In [58], the author utilizes a model-based RL method to realize the online scheduling of the residential microgrid without the preset prediction model, and then MCTS is used to find the optimal strategy network. In [59], MCTS is employed to solve the high dependence on the prior model of the hierarchical task network (HTN) planning; in the model, MCTS can help HTN to choose the optimal decomposition approach. {Although MCTS} has made excellent contributions in many fields, the application is limited in NILMI, especially in the online combined load scenes. In this paper, a novelty algorithm framework RL-based MCTS is proposed to realize the online combined load identification and prediction.
In this section, we introduce the algorithm framework of MCTS-CI model. This model aims to realize the online combined load identification and prediction. The schematic diagram of the overall design for the proposed model is shown in Figure 1. Our design inspiration comes from AlphaGo Zero [51], which has the self-directed learning ability, and this means the improvement of learning ability by playing against itself. We introduce the actual process of NILMI, the design principle of the knowledge base of load power facilities, the detailed structure of ATM-DNN, and the load recognition mechanism based on MCTS-CI.
In the smart grid IoT system, by measuring and analyzing the real-time power consumption of the electrical equipment, NILMI can realize the actual load consumption prediction of household electricity in the short and medium term [52,53]. In the current study of NILMI, the load signals are divided into transient and steady-state signals [54]. However, the load transient signal has some limitations: the short load monitoring time will result in the incomplete acquisition of the load characteristic information; more expensive monitoring equipment is needed to maintain the load monitoring accuracy; the accuracy is not satisfactory in online load combination identification scenarios. So, in this paper, we monitor and collect the steady-state power signals of the electrical load for the online load identification.
In general, when the supply voltage meets the national standard, the steady-state current of the electrical equipment has a certain statistical law [55]. We suppose the steady-state load power also satisfies the load additivity criterion; the combined load power at time tl is described as the sum of load power from the individual electrical equipment:
Pz(Xl,tl)=P1(Xlsi,tl)+P2(Xlsi,tl)+P3(Xlsi,tl)+⋯+Pn(Xlsi,tl)+σ(tl), | (3.1) |
where Pz(Xl,tl)∈R (R is the real set) represents the total power of the load at time tl; Xl represents the status of the electrical equipment, si∈[0,1,2,3,⋯,M], si=0 indicates that the device has stopped and si=1 indicates that the device is running in the status X1; Pn(Xlsi,tl) represents the household appliance n is running in the state Xlsi at time tl, where n∈N and N is the number of the household appliances; σ(tl) represents the measurement error and noise. The expression of the load power is as
P(Xl,tl)=N∑r=0Pr=N∑r=0VrIrcos(ϕr), | (3.2) |
where V represents the magnitude of the voltage, I represents the magnitude of the current, ϕ is the phase angle, and r is the initial phase angle of the first harmonic component. Figure 2 reveals the load power waveform of the different household appliances and the combined load power waveform from a day of a real house.
In the paper, we first introduce the knowledge base of load power facilities (LPF-KB), and it is used to realize the collection and storage of the load sampled data sets. As an important part of the load identification model, LPF-KB is formed by the time-domain waveform of the load and the distribution of the frequency-domain image features at different times from the various household appliances. LPF-KB is the offline data sets of our model, and mainly realizes the data sharing and the critical load features management base on time series. The detailed structure design of LPF-KB is shown in Figure 3. In our paper, considering that the load type of households' electrical equipment and electricity usage are relatively stable, and then the steady-state load power signals are selected as the load signature (LS) to represent the load characteristics. We collect the power signals of the electricity load in real-time and to utilize the dynamic time-frequency conversion technology obtain the frequency-domain image of the load power signals.
In LPF-KB structure, we suppose that the known household appliances database F(t), where, F(t)=[F1,F2,⋯,Fi]T. Each of Fi=(fi,1,fi,2,⋯,fi,N), where F(t) represents the knowledge base of load power facilities at time t, fi,N represents the operating state of the ith load power at time t, N is the number of the facility operating state.
To improve the effectiveness of the load sampled data and to reduce the parameters complexity of the input of the neural network, we use the convolutional filtering (CF) method [56]. CF can enhance the load signal by eliminating the specific spatial frequencies. It is divided into low-pass filtering, band-pass filtering, and high-pass filtering according to the enhancement types (low, medium and, high frequency). CF method can efficiently realize the simultaneous filtering of the dynamic load signals in different frequency bands, and its filtering results are uncompressed in each frequency band, which can avoid the error caused by frequency segmentation [57].
From mathematical, the CF method is defined as
ym=n∑λ=−nPλxm−λ, | (3.3) |
where 2n+1 is the maximum value of the CF weight coefficient function. Pλ is CF weight coefficient. The ideal frequency response function of the band-pass filter is as follow:
H(f)={1,&f1≤f≤f20,&else, | (3.4) |
CF weight coefficient is described as
pλ=sin(πβλ)πβλ−sin(παλ)παλ, | (3.5) |
p0=β−α, | (3.6) |
where, β=2f2/SF, α=2f1/SF, and SF is the sample frequency. From the above formulas, we can see that the frequency truncation errors can be avoided when both sides of xm exist m sampling points. The value of m will affect the accuracy and the speed of filtering. From the formula Eq (3.5), the weights are inversely proportional to λ, which shows the weights far from the center point are negligible.
To improve the accuracy of the online combination load identification, we propose a novel algorithm architecture RL-based, namely MCTS-CI. MCTS-CI includes a DNN structure based on an attention mechanism, namely ATM-DNN, and a self-learning module based on MCTS. MCTS-CI utilizes the ATM-DNN model to capture the combined load features information in the online scenario, which can focus on the specific features in the combined load while ignoring the unimportant ones. ATM-DNN can realize the fine-grained feature extraction of the load frequency domain and enhance the discrimination of the combined loads. After this operation, the RL agent can acquire the best strategies based on the experience gathered during the different training stages. Finally, this model generates actions Lat and adjust its parameters {Lst,Lat,φ}, which is based on the feedback from the environment. The process of the agent interacting with the environment can be seen in Figure 4. The greatest advantage of MCTS-CI is that it can help the RL agents understand the more complex environments and map their states to the actions [60,61].
In this paper, we propose MCTS-CI model Ms, the initial strategy πs, and SP is the all sequence of the current state Lst:{Lst,Lakt,Lrkt+1,Lskt+1,Lakt+1,⋯,LskT}Kk=1∽Ms,πs, where, Lakt represent the current action, Lrkt+1 represent the reward, K is the number of iteration. First, MCTS is constructed based on the sampling results, and then to compute the action value function Q(Lst,Las), where Las∈A, A is the action set. The calculation expression of the action value function and its corresponding action is shown as follows:
Q(Lst,as)=1N(Lst,as)K∑k=1T∑u=t(Suk=Lst,Auk=as)Gu, | (3.7) |
Lat=argmaxQ(LSt,as), | (3.8) |
where, Q(Lst,as) is the state-action and Gu represents its gains value in all complete sequences SP, γ represents the attenuation factor. Gu is described as
Gu=Lru+1+γ1Lru+2+γ2Lru+3+⋯+γT−u−1LrT. | (3.9) |
MCTS-CI is a model-independent method and it can prevent the solution of the dynamic programming from being too complicated. MCTS-CI is often used to find the best decision in a given domain by randomly sampling from the decision space and reconstructing a search tree according to the results. We employ the MCTS algorithm to realize the optimum search action for identifying the combination status of the online load [62]. The executing processes: according to the maximum probability value pts and the reward vts of the current node, which as the judgment basis of the optimal action as, the MCTS-CI determines the next execution action and state; the method is called each time of the search action for iteration until the threshold time or the preset number of cycles; from the starting, MCTS uses the initialization parameters φλ(pts,vts), to perform the tree search, and then when the end of this search, the updated parameters φ∗λ(pts,vts) of MCTS is outputted, where zts indicates that the action with the highest probability of action as. This process can be described as MCTSφ{φ|(fts,pts,vts)}→MCTS∗φ{φ∗|(fts,πts,zts)}, and the MCTS algorithm is presented in Algorithm 2. Algorithm 2 will be described later (§3.4).
In ATM-DNN, the adjustment parameter λ is to minimize the value of zts−vts, and to minimize the value of pts and πts, where pts is the choosing probability of ATM-DNN and πts is the search probability of MCTS for the next state. In RL-MCTS, the loss function L_losss includes the mean squared error, the cross-entropy loss, and attention loss (§3.4). The cross entropy loss is used for the load classification loss. The final loss function L_losss can be described as follows:
L_losss=(zts−vts)2−πTslogpts+c||λ||2+ηloss_A. | (3.10) |
This expression includes the mean squared error, the cross entropy loss [51], and attention loss loss_A (§3.4). The meaning of the parameter c is to control the extent of the L2 weight regularization, which can effectively prevent over-fitting. η is the weighting coefficient. In each iteration of MCTS, there are four steps to build a search tree: selection, expansion, simulation, and backtracking [62]. The overall design idea of MCTS is shown in Figure 5. In MCTS, each state node Ls contains four parameters: Ws, Ns, Qs, and Ps. Ws is the sum of values in the next state; Ns is the number of times visited; Qs is the average value of the next state; Ps indicates the prior probability of selected action.
Step 1: selection. At time t the load power features based on the frequency-domain FTs=∑Nt=1fts are used as the current state of the root node LSt0, and LSt0={Wt0(ft0,as),Nt0(ft0,as),Pt0(ft0,as),Qt0(ft0,as)}, where Wt0(ft0,as) represents the total value of the next state, Nt0(ft0,as) represents the number of the visit count, Pt0(ft0,as) represents the prior probability of the selected action as, and Qt0(ft0,as) represents the mean of the next state LSt0. In our paper, Upper Confidence Bound Applied to Trees (UCT) is employed to select the node with the highest score until a node that un-extended child nodes. UCT is a function and can obtain evaluation values at different depths [63]. UCT algorithm is a special Monte Carlo search algorithm, in mathematics, UCT is defined as follows:
UCT=(fts,as)=Q(sm)N(sm)+c√log(N(sm))N(sm), | (3.11) |
s0(fts,as)→sm, | (3.12) |
a∗s=argmaxQ[(fts,as)+U(fts,as)], | (3.13) |
where sm is a child node of s0, and we assume that M steps are required to reach the leaf node, and m<M. Q(sm)N(sm) indicates the estimated probability of the child node sm; c is an explore parameters (the theoretically value is √2).
It is important to select the node with the maximum of Q+U in the sub-branch of m, which is the next state until all the nodes have been traversed or the leaf node that does not reach the final MCTS. The detailed algorithm is described in Algorithm 1.
Step 2: expansion. At the end of the selection phase, the node sm needs to be expanded, and its unexpanded action as is determined by using the tree selection strategy. Then a new node sm+1 is created as a new node in the search tree. It gives the flow diagram of algorithm of the steps 1 and 2 in Figure 6. The UCT aims to find the least visited node to explore each time of the tree search.
Algorithm 1: Detailed algorithm Tree_UCT |
Input: The load state LSi; The root node RN_s0; The tree node RN_s; The reward re_v; Time threshold TN; Number of visited node Ns; The estimated value Qs. |
Output: Tree_UCT |
1 //Create Tree_UCT sequence LSi and create the root node. |
2 TreeUCTsequence[LS_i]=0; |
3 RN_s0=LS0; |
4 //Within the calculated time limit. |
5 for tn=1 to TN do |
![]() |
Step 3: simulation. According to the original policy, from the extended position sm+1, the simulation is run with the new start node until the stop condition of the simulation is reached. The process will get an initial score for the new node sm+1, and the value is expressed as 1 or 0, which indicates true or false.
Step 4: back-propagation. The results of the simulation step are updated by the back-propagation operation, which is from the current iteration path to all the selected nodes. This process is that when the leaf nodes obtain the new observed return value Vs and the visit times of the parent node Ts through the simulation way, and then the UCT algorithm will update all internal node values on the search path by returning the results. This process can be described as
Tis=∑iTis, | (3.14) |
vs=∑ivisTisTis, | (3.15) |
where Ts indicates the visited times of the parent node and is the sum of the visits Tis of all the children nodes. The observed return value Vs is the weighted square of the observed return value Vis of all child nodes. The back-propagation starts from the leaf node and progresses up the search path to the root node. Each iteration will expand the search tree, and the depth of the tree will increase by the sum of iterations.
In the paper, the strategy network model includes the internal policy and the external policy for building the MCTS-CI model. The internal strategy utilizes the UCT algorithm to optimize the performance of MCTS; the other one is a fusion DNN model with an attention mechanism, namely ATM-DNN, to replace the random strategy for achieving the sampling of the state sequence when the new state is not in the tree. The ATM-DNN model can better focus on the important feature of load. The overall structure of the model for the external policy can be shown in Figure 7.
From Figure 7, firstly, the time-frequency conversion technology is used to obtain the load frequency-domain image of the effective power. Secondly, the image segmentation technology based on the time series is used for the load frequency-domain image segmentation, which can form the load frame sequence. Then each frame image of the load frame sequence is the input of DNN for the load feature extraction. The attentional mechanism (ATM) is proposed to realize the fine-grained discrimination of the frequency-domain image in the salient regions of the load. In this process, ATM-DNN is presented as the extractor of the load feature to finish the load spatial feature map. In Figure 7, fi(w,h) represent the load spatial feature map, and i represent the ith feature cube, and w,h are used to find the specific eigenvalues. The sequence ai={a1,a2,a3,⋯,} represent the attentional weights. The spatial feature of load can be presented as ui, and its expression is as follow:
ui=ai⊙fi=∑w,hai,w,hfi,w,h. | (3.16) |
Referencing the former studies for the image recognition with the attentional mechanism, in the mathematical, the calculation process of ai includes the following formulas:
Simai,w,h=VIatanh(Wlli+Wffw,h,:), | (3.17) |
where li denotes the state of the hidden layer in RNN at time I, VIa represents the vector, Wffw,h,: is the product of the load space features and weights, Wlli is the dynamic weight, which can highlight the focusing performance of the load frame image.
And then Simai,w,h is normalized, the expression is as follow:
ai=softmaxw,h(ai)=eai∑j=1Zeaj, | (3.18) |
where Z is the number of the feature map, the introduction of softmax can give more weight to important elements. The input and output of RNN are as follow:
Rxi=WzZi−1+Wu1u(i−1), | (3.19) |
(oi,li)=G_rnn(Rxi,li−1), | (3.20) |
O′i=softmax(W0Oi+Wu2ui). | (3.21) |
The attention scoring criteria are used to measure the performance of the ATM-DNN model, and the attention loss function is described as
loss_A=√∑p∑m∑wh(1−||xspm,wh−softmax(ysq)m,wh||2p,m,wh), | (3.22) |
where xsp indicates the scores of the attention in the spatio-temporal dimension M×W×H; ysq indicates the score of the qth dimension, and m∈(1,…,M), wh∈(1,…,WH).
The overall design of MCTS-CI algorithm for NILMI is presented in Algorithm 2.
Algorithm 2: MCTS-CI Algorithm |
Input: Load power series [P1,P2,…,P(t−1)]; Preprocessed data series [P′1,P′2,…,P′(t−1)]; |
Time threshold Tleft; Create the root node RS_node0 of the state Ls0; Initial state Ls0 |
1. Output: The RL-MCTS model RM. |
2 //Training RL-MCTS model. |
3 //Construct MCTSsearch Ls0. |
4 if: t≤Tleft then |
![]() |
In this section, we mainly present the specific experimental settings for our proposed model. It includes the acquisition of the experimental data and data preprocessing, the experimental evaluation, and then the results discussion. The following is the purpose of our experiment: 1) the performance verification of the representational capacity of the ATM-DNN model; 2) the performance evaluation of the MCTS-CI model for the online combination load identification.
The experimental datasets consist of two parts: the offline datasets and the online datasets. We use the offline datasets to train the external policy network ATM-DNN, and the online datasets are used to identify and predict the combined load. The experimental datasets mainly sample the high_frequency steady-state load power signals, from the six different houses. The data collection period is three years (2018/05/01-2020/04/30). The sampling period of the load from household appliances is 20 ns. In each dataset, there are four typical household appliances: refrigerator (REF), micro-wave oven (MWO), induction cooker (IC), and water heater (WH). The specific quantity of electrical equipment is shown in Table 1.
Load type | No. 1 house | No. 2 house | No. 3 house | No. 4 house | No. 5 house | No. 6 house |
REF | 107 | 106 | 107 | 107 | 107 | 107 |
MWO | 106 | 106 | 107 | 105 | 107 | 106 |
IC | 105 | 105 | 107 | 105 | 105 | 105 |
WH | 107 | 107 | 107 | 106 | 107 | 106 |
We select the most representative models as the baseline models to verify the stability and robustness of MCTS-CI. These models include the Factorial Hidden Markov model (FHMM) [65], KNN [66], CNN [67], and the bi-directional long short-term memory neural network (Bi-LSTM) [68].
FHMM: This method is a multi-chain model extension structure. It is widely used as a statistical model to deal with time series and state series problems. The method divides the state space into many layers, and each layer is equivalent to a complete Hidden Markov model. At any given moment, the observed probability depends on the current state of all layers. The method can overcome the overfitting of the model training with the increased sampling space and the incomplete classification of a model in the small sampling space.
kNN: This method determines the classification of the sampling data according to the category of the nearest one or several samples. The improved the kNN models generally include adding weight coefficients and using the averages distance k-nearest neighbor algorithm to realize the estimate of the multi-local mean vectors in the specific patterns of each group. In the combined load identification scenario, the kNN method is more suitable. CNN: This method mainly realizes the load features of that local are extracted by building the connection of the input of each neuron and the local acceptance domain of the previous layer. It can achieve the network parallel operation. By integrating the DNN architecture, the segmentation of the input features is formed in load signals to achieve the load event, classification, and sample regression in NILM.
Bi-LSTM: This method is used to solve the bidirectional time dependence of the load signal. LSTM realizes time memory function through the cell door switch and prevents gradient from disappearing.
To meet our experimental requirements, our experimental environment includes the hardware and software. The Pytorch 3.7 is used to achieve the design and implementation of the algorithms; the desktop PC with Windows 10 system and a NVIDA GeForce RTX 3080 Ti GPU, 128GB of RAM, and CUDA v10.2 are employed.
The core of our proposed model includes the ATM-DNN model and the MCTS algorithm. In ATM-DNN, the specific parameters set as follows: there are six blocks in the hidden layer, six filters in each block, the size and stride of the pooling layer are two; our activation function is ReLu; the dropout probability set at 0.45; the number of mini-batch is 512; Adam is as the optimizer; the learning rate set to 0.0006; the number of epochs set to 300. In the MCTS model, the self-play process will last for 20 h, implemented 105 decision-makings, and 750 simulation operations are performed in each MCTS, which will present in the following subparagraph.
We select four representative household appliances for our experiment. Their work patterns range from simple to complex, especially the bidirectional dependence of the device can be reflected. Relatively diverse working modes are beneficial to verifying the performance indicators of our model. We sample the experimental datasets from the smart meter installed at the power entrance to the house, which can realize the acquisition and practical storage of the real-time data (current, voltage, and efficient power) in the various household appliances. The steady-state power signal of load in high-frequency contains the rich load characteristic information. The information can provide the discriminative load features for the combined load identification in various online scenarios.
The CF-based signal enhancement method is used to realize the data preprocessing. In the load data sampling, signal noises are caused by equipment operation errors, the overshoot, or abnormal peak of voltage or current of the signal. The data preprocessing method can solve the signal acquisition error and instability problems effectively; this method can correct the abnormal power values and ensure the top quality of the signal.
We have prepared a lot of sampled datasets to validate the performance of our model, and these datasets are from four houses (No. 1 house, No. 2 house, No. 3 house, and No. 4 house). We divide each dataset into three parts: the training dataset, the validation dataset, and the testing dataset. The first 70% of the dataset sequence for the training model, the subsequent dataset sequence 15% for validating the model, and the rest 15% of the data sets for the testing model. In the online experimental stage, we use the collected data sets of No. 5 house and No. 6 house in real-time to further verify the model performance's competitiveness.
We employed the most common evaluation criteria to verify the performance and stability of our model, such as Micro-F1, Macro-F1, Hamming loss (H-loss), and the mean absolute error (MAE). These methods have been widely used in the field of deep learning. The criteria can be defined as follow:
Micro-F1 and Macro-F1. Micro-F1 and Macro-F1 are the two strategies for calculating F1-score of the multi-classification. It is assumed that LTPti, LFPti, LTNti, and LFNti denote the true positive at time t, false positive, true negative, false negative of classify i. The precision, Lpreti, and the recall, Lrecti, of the class i can be described as
Lpreti=LTPtiLTPti+LFPti, | (4.1) |
Lrecti=LTPtiLTPti+LFNti. | (4.2) |
So, the total precision and recall of all categories are presented as follows:
Lpret(i)micro=∑ni=1LTPti∑ni=1LTPti+n∑i=1LFPti, | (4.3) |
Lrect(i)micro=∑ni=1LTPti∑ni=1LTPti+n∑i=1LFNti, | (4.4) |
micro−F1=2⋅Lpret(i)micro⋅LrectmicroLpret(i)micro+Lrect(i)micro, | (4.5) |
Macro-F1 is described as follows:
Macro−F1=2⋅¯Lprei⋅¯Lreci¯Lprei+¯Lreci, | (4.6) |
¯Lprei indicates as the mean precision of the i−th class, ¯Lreci indicates as the mean recall of the i−th class.
Hamming-loss: Hamming−loss is employed to measure the times in the misclassification samples; namely the labels belonging to a sample are not predicted, but the labels that do not belong to the sample are predicted to belong to the sample. The smaller value, the better the model performance. The mathematical formula is described as
H−loss(xi,yi)=1|M|∑i=1|M|1|L|xor(xi,yi), | (4.7) |
where |M| represents the size of samples, |L| represents the size of labels, xi and yi indicate the predicted value and real value, respectively.
MAE: we suppose that yti represents the real data, y′ti is the estimated data of the appliance i at time t. The mathematical expression is as follows:
MAEi=1TT∑t=1|yti−y′ti|. | (4.8) |
In this section, the model's learning ability and overall accuracy are verified, respectively. The various performance scores of the models are presented to evaluate the superiority of our model.
To improve the MCTS model's decision-making efficiency to ensure the decision-making process's reliability, we utilize the ATM-DNN structure to obtain the initial tree policy pts and the action values vts, which instead of the randomly selected actions at the beginning of the program. The next time, the program of MCTS will self-learning for 20 h without any additional intervention, and then the complete search process will be completed. The outputted parameters of each iteration in MCTS are compared with the output results of ATM-DNN and updated ATM-DNN parameters as the policy improvement. We use the performance index MSE and Loss as the critical evaluation indicators to present the performance of the search process in model training. Then this convergence trend can be seen in Figure 8. MSE indicates the degree of error between the actual output of MCTS and the predicted value of ATM-DNN, which can be used as the basis to adjust the parameters of the ATM-DNN and minimize the predicted value. Loss is the crossentropy losses, and the values represent the similarity of ATM-DNN selection strategy pts to the search probabilities πs.
The training process of MCTS-CI includes two steps: the offline datasets of four different houses are used to train the ATM-DNN model, and then the results of the model training are the initial strategies for completing the initial selection in MCTS; the MCTS starts the search by self-learning and goes through exploration, simulation and, feedback. The process lasts about 20 h and implements about 105 decision-makings. In each MCTS, 750 simulation operations are performed, meaning that making the new decisions is nearly 0.5 s. After about 7000 self-decisions, the ATM-DNN can efficiently guide the MCTS search to select stable actions. Finally, the process can realize the policy evaluation and improvement in the MCTS search.
We use the mean reward of RL in the training process to further validate the performance of the MCTS-CI. The results are shown in Figure 9. We employ the two datasets of No. 1 house and No. 2 house, respectively. The two datasets provide a large amount of the sampled load data resources in four household appliances. The validation results are shown in Figure 9. From the two curves, the intelligent agent goes through the initial selection and exploration. Then they eventually converge to the global optimum, which can prove the convergence of our method. From convergence result, the average reward is approximately 0.697 and 0.676, respectively, on the two datasets.
To explain the influence of the simulation times on model performance, we try to use the different simulation times to experiment with our model; the result of the experiment can be presented in Figure 10. We performed 50, 150, 350, 750 simulation cycles, respectively, and as the number of cycles increases, so does the rate of reward. It is because the increase of the simulation numbers can make more simulation numbers of the Monte Carlo tree, which can improve the accuracy of the evaluation of the action. So, it can ensure the optimal action and improve the effectiveness of the Monte Carlo tree search. From Figure 10, as the reward reaches a certain point, the performance will tend to stabilize.
In the model training process, we use the training time to test the computing power of the model, and the comparison of the training time in five models is shown in Figure 11. The sample size and the performances of the computing devices are the main factors in computing time. From Figure 11, our proposed model has the shortest model training time on the different sizes of the sample training sets.
In this subsection, four datasets from the different houses are employed to verify the performance of FHMM, KNN, CNN, Bi-LSTM, and MCTS-CI. The validation process includes the following: 1) the comparison of the average accuracy with the five methods in training datasets; 2) the overall performance score of five different methods; 3) the comparison of the Micro−F1 and Macro−F1 scores on the specific household appliances datasets. We first present the overall accuracy of benchmark models and our model. The five-fold cross-validation on each dataset is used to ensure the reliability of our experiment results. Table 2 shows the accuracy and standard deviations of the results, which are presented in bold for the best results.
Methods | Datasets | |||
No. 1 house | No. 2 house | No. 3 house | No. 4 house | |
FHMM | 67.13 ± 0.36 | 69.83 ± 1.71 | 54.56 ± 0.98 | 63.68 ± 4.32 |
KNN | 70.34 ± 1.23 | 73.56 ± 0.89 | 62.78 ± 0.65 | 74.69 ± 2.31 |
CNN | 78.24 ± 1.12 | 75.43 ± 0.62 | 77.23 ± 4.12 | 80.68 ± 0.89 |
Bi-LSTM | 87.78 ± 4.13 | 79.56 ± 2.92 | 85.57 ± 2.54 | 85.97 ± 5.18 |
MCTS-CI | 95.56 ± 0.19 | 89.91 ± 0.47 | 90.95 ± 0.42 | 92.22 ± 0.34 |
From Table 2, the accuracy of MCTS-CI is higher than other methods on different datasets. Significantly, the average accuracy of the MCTS-CI reaches 95.56%, which is higher than the second-ranked method 7.78% on No. 1 house datasets. On the other datasets, the average accuracy of MCTS-CI is 89.91%, 90.95%, and 92.22%, which are superior to others 10.35%, 5.38%, and 6.25%, respectively. In comparison with CNN, in load combination identification, MCTS-CI is more competitive. The reason that different from image recognition, the load has the time-series feature, and CNN does not play to its strengths. It can illustrate that our model has advantages in online identification of the loads. In a word, this method will have a good application prospect for NILMI.
To go a step further and validate the performance of our model, we carry out the experimental tests from the overall and detailed aspects of the model, respectively. The scores from Micro-F1, Macro-F1, and H−loss are employed to describe the experimental results.
The overall case: the experimental results of four datasets from the different houses are shown and discussed. Table 3 demonstrates the overall performance indicators of the benchmark models and our proposed model in different datasets from four houses, respectively. The numbers in the bold represent the performance is better than the others. Especially, from Table 3 Micro-F1 score of our method can come up to 0.964, which is higher than the second-ranked model, 0.107. In addition, the score is 0.947, 0.946, 0.935 are higher than the second-ranked model Bi-LSTM 0.084, 0.092, 0.058, respectively. The same effect of model performance appears on Macro-F1 score. From H-loss, the value of MCTS-CI is relatively minimal, indicating that the number of misclassification samples is low. The second-lowest is Bi-LSTM, indicating that the LSTM model has a significant advantage for load sampling data based on time series characteristics. CNN model can complete the feature extraction of load well, but it is not sensitive to the load combination identification. From MAE, the values of MCTS-CI are smaller than the other benchmark models, and it presents a high identification accuracy in different datasets.
Criteria | Methods | Datasets | |||
No. 1 house | No. 2 house | No. 3 house | No. 4 house | ||
H−loss | FHMM | 0.021 ± 0.002 | 0.019 ± 0.001 | 0.017 ± 0.002 | 0.020 ± 0.001 |
KNN | 0.018 ± 0.001 | 0.016 ± 0.002 | 0.014 ± 0.001 | 0.015 ± 0.004 | |
CNN | 0.014 ± 0.003 | 0.012 ± 0.004 | 0.011 ± 0.003 | 0.013 ± 0.002 | |
Bi-LSTM | 0.009 ± 0.002 | 0.008 ± 0.001 | 0.006 ± 0.001 | 0.009 ± 0.003 | |
MCTS-CI | 0.003 ± 0.001 | 0.003 ± 0.001 | 0.002 ± 0.001 | 0.004 ± 0.002 | |
Micro-F1 | FHMM | 0.691 ± 0.019 | 0.636 ± 0.012 | 0.627 ± 0.021 | 0.624 ± 0.017 |
KNN | 0.724 ± 0.003 | 0.652 ± 0.014 | 0.719 ± 0.016 | 0.698 ± 0.030 | |
CNN | 0.821 ± 0.013 | 0.794 ± 0.017 | 0.814 ± 0.008 | 0.755 ± 0.023 | |
Bi-LSTM | 0.857 ± 0.019 | 0.873 ± 0.040 | 0.854 ± 0.013 | 0.877 ± 0.017 | |
MCTS-CI | 0.964 ± 0.004 | 0.947 ± 0.008 | 0.946 ± 0.011 | 0.935 ± 0.006 | |
Macro-F1 | FHMM | 0.562 ± 0.029 | 0.637 ± 0.010 | 0.587 ± 0.014 | 0.613 ± 0.011 |
KNN | 0.695 ± 0.019 | 0.642 ± 0.013 | 0.693 ± 0.012 | 0.661 ± 0.008 | |
CNN | 0.795 ± 0.015 | 0.788 ± 0.015 | 0.758 ± 0.005 | 0.714 ± 0.003 | |
Bi-LSTM | 0.828 ± 0.012 | 0.854 ± 0.010 | 0.831 ± 0.004 | 0.856 ± 0.012 | |
MCTS-CI | 0.924 ± 0.008 | 0.937 ± 0.012 | 0.912 ± 0.010 | 0.928 ± 0.014 | |
MAE | FHMM | 0.321 ± 0.018 | 0.351 ± 0.015 | 0.371 ± 0.002 | 0.251 ± 0.008 |
KNN | 0.275 ± 0.011 | 0.247 ± 0.012 | 0.284 ± 0.003 | 0.212 ± 0.013 | |
CNN | 0.172 ± 0.009 | 0.201 ± 0.022 | 0.171 ± 0.017 | 0.149 ± 0.008 | |
Bi-LSTM | 0.154 ± 0.007 | 0.132 ± 0.011 | 0.154 ± 0.012 | 0.135 ± 0.014 | |
MCTS-CI | 0.081 ± 0.013 | 0.062 ± 0.008 | 0.091 ± 0.012 | 0.061 ± 0.006 |
The detailed case: to better demonstrate our approach's significant advantages, we use our model and four benchmark models on the various appliances datasets from the four houses to verify the models performance. Finally, Micro-F1 and Macro-F1 scores of the various appliances used in the four houses are obtained, which can be found in Figures 12 and 13. From Figure 12, Micro-F1 scores of each appliance are shown from subgraph a to subgraph d; the scores of MCTS-CI in each of the appliances are the highest-scoring, and Bi-LSTM is the second-highest score, and then the FHMM is the lowest-scoring. The results similar to that presented in Figure 13, Macro-F1 scores of MCTS-CI are best. Our proposed method excels the other methods in the score of each appliance. Especially, in subgraph Figure 12(a), Micro-F1 score of the refrigerator, water heater, microwave oven, and induction cooker is up to 96.19%, 95.52%, 97.57%, and 94.37%, which is far superior to the other methods. In subgraph Figure 12(d), the highest score of Micro-F1 is 97.96%, and it is found in the water heater with our method; the worst score appears in subgraph Figure 13(d), Macro-F1, 53.23%, which is the score with FHMM in the refrigerator. The performance of FHMM is not good in our experiment, and the same situation also appears in KNN and CNN models. The Bi-LSTM is better than CNN and KNN because some memory gates mode characterizes it, and it can better represent the time-dependent relationships of sampling data.
Actual load identification and prediction test: In this subsection, we collect the online data from No. 5 house and No. 6 house as the validation datasets to verify our model performance in real-world applications. We observe the electricity consumption of two households and sample the load power signals from various household appliances on three random days. Figure 14 shows the power curve of the time-domain from the two different houses in three days. From Figure 14, we can identify four periods of electricity consumption, which mainly include 5:30 a.m. to 7:30 a.m, 11:00 a.m. to 1:00 p.m., 5:30 p.m. to 8:00 p.m., and 9:00 p.m. to 10:30 p.m. The frequency-domain image of the load power signals can also represent the load features, and they, as the input of DNN, can improve the accuracy of load feature extraction. In our model, the time-frequency conversion technology forms the load frequency-domain features. To ensure the validity of our data set, we use the CF method to improve the quality of the power signals and reduce the error of signal acquisition. After the data preparation and preprocessing, the datasets are inputted into our proposed model to extract the spatio-temporal features of load and identify the specific load power from all kinds of household appliances. The detailed load identification results can be presented in Figures 15 and 16.
As Figures 15 and 16 indicate, MCTS-CI shows a good recognition effect and stability in the test data set of the actual electricity consumption. Significantly, the experimental result of the water heater agrees with and refrigerator with the real sampled data in the usage scenarios of two different houses, which shows that MCTS-CI can achieve excellent recognition for the electrical appliance with high-frequency usage. The same results appear in the micro-wave oven of No. 5 house and the induction cooker of No. 6 house. For the household appliances with low-frequency usage, more data are needed to train the model. Our proposed method has outstanding practical value and better performance for correctly categorizing the online load.
CGC is applied in the IoT system of the smart grid for realizing the rational allocation of the various resources and improve the production efficiency. NILMI is the most efficient way, can improve the performance and stability of CGC in the smart grid. In this paper, we develop a novel algorithm framework, MCTS-CI, which is a multi-stage algorithm framework. MCTS-CI involves a load data sharing platform, LPF-KB, a load spatio-temporal feature extraction mechanism based on ATM-DNN, and a set of optimized policy networks based on MCTC. LPF-KB can enhance the utilization rate of load sampled data to realize the fine-grained storage of load; ATM-DNN can improve the distinctiveness and effectiveness of the combined load features; the extraction of load spatio-temporal features can improve the learning ability of load representations; MCTS is used to achieve the optimal strategy for reducing the error rate of load identification and energy prediction in the online scenario. By comparison with four benchmark models, the experimental results prove that MCTS-CI has outstanding performance for improving the accuracy of the load combination identification and reducing the identification error rate of the unknown load.
The method can be widely applied for predicting household electricity consumption in online scenarios. {The method will as a design idea for realizing the short and medium-term load} identification with the external weather and periodic temporal features to improve the intelligent adjustment performance of the IoT system in the smart grid. In the future, we will utilize MCTS-CI to address the more complex and variable load combination scenarios.
The authors of this paper were supported by S & T Program of Hebei through grant 20310101D, and National Key R & D Program of China through grant 2021YFB1714800.
The authors declare there is no conflict of interest.
[1] |
Pouget C, Dunyach-Remy C, Pantel A, et al. (2020) Biofilms in diabetic foot ulcers: Significance and clinical relevance. Microorganisms 8. https://doi.org/10.3390/microorganisms8101580 ![]() |
[2] |
Costerton JW, Stewart PS, Greenberg EP (1999) Bacterial biofilms: A common cause of persistent infections. Science 284: 1318-1322. https://doi.org/10.1126/science.284.5418.1318 ![]() |
[3] |
Lam JS, MacDonald LA, Lam MY, et al. (1987) Production and characterization of monoclonal antibodies against serotype strains of Pseudomonas aeruginosa. Infect Immun 55: 1051-1057. https://doi.org/10.1128/iai.55.5.1051-1057.1987 ![]() |
[4] |
Leid JG, Willson CJ, Shirtliff ME, et al. (2005) The exopolysaccharide alginate protects Pseudomonas aeruginosa biofilm bacteria from IFN-γ-mediated macrophage killing1. J Immunol 175: 7512-7518. https://doi.org/10.4049/jimmunol.175.11.7512 ![]() |
[5] |
Wolcott R, Costerton JW, Raoult D, et al. (2013) The polymicrobial nature of biofilm infection. Clin Microbiol Infect 19: 107-112. https://doi.org/10.1111/j.1469-0691.2012.04001.x ![]() |
[6] |
Bjarnsholt T (2013) The role of bacterial biofilms in chronic infections. APMIS Suppl : 1-51. https://doi.org/10.1111/apm.12099 ![]() |
[7] |
Brogden KA, Guthmiller JM, Taylor CE (2005) Human polymicrobial infections. Lancet 365: 253-255. https://doi.org/10.1016/s0140-6736(05)17745-9 ![]() |
[8] |
Larsen MK, Thomsen TR, Moser C, et al. (2008) Use of cultivation-dependent and -independent techniques to assess contamination of central venous catheters: a pilot study. BMC Clin Pathol 8: 10. https://doi.org/10.1186/1472-6890-8-10 ![]() |
[9] | Kumar A, Seenivasan MK, Inbarajan A (2021) A literature review on biofilm formation on silicone and poymethyl methacrylate used for maxillofacial prostheses. Cureus 13: e20029. https://doi.org/10.7759/cureus.20029 |
[10] |
Rohacek M, Weisser M, Kobza R, et al. (2010) Bacterial colonization and infection of electrophysiological cardiac devices detected with sonication and swab culture. Circulation 121: 1691-1697. https://doi.org/10.1161/circulationaha.109.906461 ![]() |
[11] |
Peters BM, Jabra-Rizk MA, O'May GA, et al. (2012) Polymicrobial interactions: impact on pathogenesis and human disease. Clin Microbiol Rev 25: 193-213. https://doi.org/10.1128/cmr.00013-11 ![]() |
[12] |
Percival SL, McCarty SM, Lipsky B (2015) Biofilms and wounds: An overview of the evidence. Adv Wound Care New Rochelle 4: 373-381. https://doi.org/10.1089/wound.2014.0557 ![]() |
[13] |
Beaudoin T, Yau YCW, Stapleton PJ, et al. (2017) Staphylococcus aureus interaction with Pseudomonas aeruginosa biofilm enhances tobramycin resistance. NPJ Biofilms Microbiomes 3: 25. https://doi.org/10.1038/s41522-017-0035-0 ![]() |
[14] |
Windels EM, Michiels JE, Van den Bergh B, et al. (2019) Antibiotics: combatting tolerance to stop resistance. mBio 10. https://doi.org/10.1128/mbio.02095-19 ![]() |
[15] |
Tay WH, Chong KK, Kline KA (2016) Polymicrobial-host interactions during infection. J Mol Biol 428: 3355-3371. https://doi.org/10.1016/j.jmb.2016.05.006 ![]() |
[16] |
Wimmer MD, Friedrich MJ, Randau TM, et al. (2016) Polymicrobial infections reduce the cure rate in prosthetic joint infections: outcome analysis with two-stage exchange and follow-up ≥two years. Int Orthop 40: 1367-1373. https://doi.org/10.1007/s00264-015-2871-y ![]() |
[17] |
Dowd SE, Wolcott RD, Sun Y, et al. (2008) Polymicrobial nature of chronic diabetic foot ulcer biofilm infections determined using bacterial tag encoded FLX amplicon pyrosequencing (bTEFAP). PLoS One 3: e3326. https://doi.org/10.1371/journal.pone.0003326 ![]() |
[18] |
Solano C, Echeverz M, Lasa I (2014) Biofilm dispersion and quorum sensing. Curr Opin Microbiol 18: 96-104. https://doi.org/10.1016/j.mib.2014.02.008 ![]() |
[19] |
Hibbing ME, Fuqua C, Parsek MR, et al. (2010) Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol 8: 15-25. https://doi.org/10.1038/nrmicro2259 ![]() |
[20] |
Smith H (1982) The role of microbial interactions in infectious disease. Philos Trans R Soc Lond B Biol Sci 297: 551-561. https://doi.org/10.1098/rstb.1982.0060 ![]() |
[21] |
Hajishengallis G, Liang S, Payne MA, et al. (2011) Low-abundance biofilm species orchestrates inflammatory periodontal disease through the commensal microbiota and complement. Cell Host Microbe 10: 497-506. https://doi.org/10.1016/j.chom.2011.10.006 ![]() |
[22] |
Korgaonkar A, Trivedi U, Rumbaugh KP, et al. (2013) Community surveillance enhances Pseudomonas aeruginosa virulence during polymicrobial infection. Proc Natl Acad Sci USA 110: 1059-1064. https://doi.org/10.1073/pnas.1214550110 ![]() |
[23] |
Stacy A, McNally L, Darch SE, et al. (2016) The biogeography of polymicrobial infection. Nat Rev Microbiol 14: 93-105. https://doi.org/10.1038/nrmicro.2015.8 ![]() |
[24] |
Simón-Soro A, Mira A (2015) Solving the etiology of dental caries. Trends Microbiol 23: 76-82. https://doi.org/10.1016/j.tim.2014.10.010 ![]() |
[25] |
Yung DBY, Sircombe KJ, Pletzer D (2021) Friends or enemies? The complicated relationship between Pseudomonas aeruginosa and Staphylococcus aureus. Mol Microbiol 116: 1-15. https://doi.org/10.1111/mmi.14699 ![]() |
[26] |
DeLeon S, Clinton A, Fowler H, et al. (2014) Synergistic interactions of Pseudomonas aeruginosa and Staphylococcus aureus in an in vitro wound model. Infect Immun 82: 4718-4728. https://doi.org/10.1128/iai.02198-14 ![]() |
[27] |
Short FL, Murdoch SL, Ryan RP (2014) Polybacterial human disease: the ills of social networking. Trends Microbiol 22: 508-516. https://doi.org/10.1016/j.tim.2014.05.007 ![]() |
[28] |
Peleg AY, Hogan DA, Mylonakis E (2010) Medically important bacterial-fungal interactions. Nat Rev Microbiol 8: 340-349. https://doi.org/10.1038/nrmicro2313 ![]() |
[29] |
Murray JL, Connell JL, Stacy A, et al. (2014) Mechanisms of synergy in polymicrobial infections. J Microbiol 52: 188-199. https://doi.org/10.1007/s12275-014-4067-3 ![]() |
[30] |
Griffiths EC, Pedersen AB, Fenton A, et al. (2011) The nature and consequences of coinfection in humans. J Infect 63: 200-206. https://doi.org/10.1016/j.jinf.2011.06.005 ![]() |
[31] |
Hogan DA, Kolter R (2002) Pseudomonas-Candida interactions: an ecological role for virulence factors. Science 296: 2229-2232. https://doi.org/10.1126/science.1070784 ![]() |
[32] |
Trivedi U, Parameswaran S, Armstrong A, et al. (2014) Prevalence of multiple antibiotic resistant infections in Diabetic versus nondiabetic wounds. J Pathog 2014: 173053. https://doi.org/10.1155/2014/173053 ![]() |
[33] |
Costello EK, Lauber CL, Hamady M, et al. (2009) Bacterial community variation in human body habitats across space and time. Science 326: 1694-1697. https://doi.org/10.1126/science.1177486 ![]() |
[34] |
Nemergut DR, Schmidt SK, Fukami T, et al. (2013) Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev 77: 342-356. https://doi.org/10.1128/mmbr.00051-12 ![]() |
[35] |
Vellend M (2010) Conceptual synthesis in community ecology. Q Rev Biol 85: 183-206. https://doi.org/10.1086/652373 ![]() |
[36] |
Kolenbrander PE, Palmer RJ, Periasamy S, et al. (2010) Oral multispecies biofilm development and the key role of cell-cell distance. Nat Rev Microbiol 8: 471-480. https://doi.org/10.1038/nrmicro2381 ![]() |
[37] |
Valm AM, Mark Welch JL, Rieken CW, et al. (2011) Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging. Proc Natl Acad Sci U S A 108: 4152-4157. https://doi.org/10.1073/pnas.1101134108 ![]() |
[38] |
Egland PG, Palmer RJ, Kolenbrander PE (2004) Interspecies communication in Streptococcus gordonii-Veillonella atypica biofilms: signaling in flow conditions requires juxtaposition. Proc Natl Acad Sci USA 101: 16917-16922. https://doi.org/10.1073/pnas.0407457101 ![]() |
[39] |
Jakubovics NS, Gill SR, Iobst SE, et al. (2008) Regulation of gene expression in a mixed-genus community: stabilized arginine biosynthesis in Streptococcus gordonii by coaggregation with Actinomyces naeslundii. J Bacteriol 190: 3646-3657. https://doi.org/10.1128/jb.00088-08 ![]() |
[40] |
He X, McLean JS, Edlund A, et al. (2015) Cultivation of a human-associated TM7 phylotype reveals a reduced genome and epibiotic parasitic lifestyle. Proc Natl Acad Sci U S A 112: 244-249. https://doi.org/10.1073/pnas.1419038112 ![]() |
[41] |
Momeni B, Brileya KA, Fields MW, et al. (2013) Strong inter-population cooperation leads to partner intermixing in microbial communities. Elife 2: e00230. https://doi.org/10.7554/elife.00230 ![]() |
[42] |
Estrela S, Brown SP (2013) Metabolic and demographic feedbacks shape the emergent spatial structure and function of microbial communities. PLoS Comput Biol 9: e1003398. https://doi.org/10.1371/journal.pcbi.1003398 ![]() |
[43] |
Connell JL, Ritschdorff ET, Whiteley M, et al. (2013) 3D printing of microscopic bacterial communities. Proc Natl Acad Sci U S A 110: 18380-18385. https://doi.org/10.1073/pnas.1309729110 ![]() |
[44] |
Stacy A, Everett J, Jorth P, et al. (2014) Bacterial fight-and-flight responses enhance virulence in a polymicrobial infection. Proc Natl Acad Sci U S A 111: 7819-7824. https://doi.org/10.1073/pnas.1400586111 ![]() |
[45] |
Schillinger C, Petrich A, Lux R, et al. (2012) Co-localized or randomly distributed? Pair cross correlation of in vivo grown subgingival biofilm bacteria quantified by digital image analysis. PLoS One 7: e37583. https://doi.org/10.1371/journal.pone.0037583 ![]() |
[46] |
Settem RP, El-Hassan AT, Honma K, et al. (2012) Fusobacterium nucleatum and Tannerella forsythia induce synergistic alveolar bone loss in a mouse periodontitis model. Infect Immun 80: 2436-2443. https://doi.org/10.1128/iai.06276-11 ![]() |
[47] |
Liu X, Ramsey MM, Chen X, et al. (2011) Real-time mapping of a hydrogen peroxide concentration profile across a polymicrobial bacterial biofilm using scanning electrochemical microscopy. Proc Natl Acad Sci U S A 108: 2668-2673. https://doi.org/10.1073/pnas.1018391108 ![]() |
[48] |
Fazli M, Bjarnsholt T, Kirketerp-Møller K, et al. (2009) Nonrandom distribution of Pseudomonas aeruginosa and Staphylococcus aureus in chronic wounds. J Clin Microbiol 47: 4084-4089. https://doi.org/10.1128/jcm.01395-09 ![]() |
[49] | Kim W, Racimo F, Schluter J, et al. (2014) Importance of positioning for microbial evolution. Proc Natl Acad Sci U S A 111: E1639-47. https://doi.org/10.1073/pnas.1323632111 |
[50] |
Eberl L, Tümmler B (2004) Pseudomonas aeruginosa and Burkholderia cepacia in cystic fibrosis: genome evolution, interactions and adaptation. Int J Med Microbiol 294: 123-131. https://doi.org/10.1016/j.ijmm.2004.06.022 ![]() |
[51] |
Bragonzi A, Farulla I, Paroni M, et al. (2012) Modelling co-infection of the cystic fibrosis lung by Pseudomonas aeruginosa and Burkholderia cenocepacia reveals influences on biofilm formation and host response. PLoS One 7: e52330. https://doi.org/10.1371/journal.pone.0052330 ![]() |
[52] |
Markussen T, Marvig RL, Gómez-Lozano M, et al. (2014) Environmental heterogeneity drives within-host diversification and evolution of Pseudomonas aeruginosa. mBio 5: e01592-14. https://doi.org/10.1128/mbio.01592-14 ![]() |
[53] |
Huse HK, Kwon T, Zlosnik JE, et al. (2013) Pseudomonas aeruginosa enhances production of a non-alginate exopolysaccharide during long-term colonization of the cystic fibrosis lung. PLoS One 8: e82621. https://doi.org/10.1371/journal.pone.0082621 ![]() |
[54] |
Guilhen C, Forestier C, Balestrino D (2017) Biofilm dispersal: multiple elaborate strategies for dissemination of bacteria with unique properties. Mol Microbiol 105: 188-210. https://doi.org/10.1111/mmi.13698 ![]() |
[55] |
Baishya J, Wakeman CA (2019) Selective pressures during chronic infection drive microbial competition and cooperation. NPJ Biofilms Microbiomes 5: 16. https://doi.org/10.1038/s41522-019-0089-2 ![]() |
[56] | How KY, Song KP, Chan KG (2016) Porphyromonas gingivalis: An overview of periodontopathic pathogen below the gum line. Front Microbiol 7: 53. https://doi.org/10.3389/fmicb.2016.00053 |
[57] |
Swidsinski A, Weber J, Loening-Baucke V, et al. (2005) Spatial organization and composition of the mucosal flora in patients with inflammatory bowel disease. J Clin Microbiol 43: 3380-3389. https://doi.org/10.1128/jcm.43.7.3380-3389.2005 ![]() |
[58] |
Bezine E, Vignard J, Mirey G (2014) The cytolethal distending toxin effects on Mammalian cells: a DNA damage perspective. Cells 3: 592-615. https://doi.org/10.3390/cells3020592 ![]() |
[59] | Martin OCB, Frisan T (2020) Bacterial genotoxin-induced DNA damage and modulation of the host immune microenvironment. Toxins Basel 12. https://doi.org/10.3390/toxins12020063 |
[60] |
Weitzman MD, Weitzman JB (2014) What's the damage? The impact of pathogens on pathways that maintain host genome integrity. Cell Host Microbe 15: 283-294. https://doi.org/10.1016/j.chom.2014.02.010 ![]() |
[61] | Miller RA, Betteken MI, Guo X, et al. (2018) The typhoid toxin produced by the nontyphoidal Salmonella enterica serotype javiana is required for induction of a DNA damage response in vitro and systemic spread in vivo. mBio 9. https://doi.org/10.1128/mbio.00467-18 |
[62] |
Del Bel Belluz L, Guidi R, Pateras IS, et al. (2016) The typhoid toxin promotes host survival and the establishment of a persistent asymptomatic infection. PLoS Pathog 12: e1005528. https://doi.org/10.1371/journal.ppat.1005528 ![]() |
[63] |
Frisan T (2021) Co- and polymicrobial infections in the gut mucosa: The host-microbiota-pathogen perspective. Cell Microbiol 23: e13279. https://doi.org/10.1111/cmi.13279 ![]() |
[64] |
Wang B, Kohli J, Demaria M (2020) Senescent cells in cancer therapy: Friends or foes?. Trends Cancer 6: 838-857. https://doi.org/10.1016/j.trecan.2020.05.004 ![]() |
[65] |
Gorgoulis V, Adams PD, Alimonti A, et al. (2019) Cellular senescence: Defining a path forward. Cell 179: 813-827. https://doi.org/10.1016/j.cell.2019.10.005 ![]() |
[66] |
Ahn SH, Cho SH, Song JE, et al. (2017) Caveolin-1 serves as a negative effector in senescent human gingival fibroblasts during Fusobacterium nucleatum infection. Mol Oral Microbiol 32: 236-249. https://doi.org/10.1111/omi.12167 ![]() |
[67] |
Kim JA, Seong RK, Shin OS (2016) Enhanced viral replication by cellular replicative senescence. Immune Netw 16: 286-295. https://doi.org/10.4110/in.2016.16.5.286 ![]() |
[68] |
Shivshankar P, Boyd AR, Le Saux CJ, et al. (2011) Cellular senescence increases expression of bacterial ligands in the lungs and is positively correlated with increased susceptibility to pneumococcal pneumonia. Aging Cell 10: 798-806. https://doi.org/10.1111/j.1474-9726.2011.00720.x ![]() |
[69] | Murphy TF, Bakaletz LO, Smeesters PR (2009) Microbial interactions in the respiratory tract. Pediatr Infect J 28: S121-6. https://doi.org/10.1097/inf.0b013e3181b6d7ec |
[70] |
Preza D, Olsen I, Aas JA, et al. (2008) Bacterial profiles of root caries in elderly patients. J Clin Microbiol 46: 2015-2021. https://doi.org/10.1128/jcm.02411-07 ![]() |
[71] |
Becker MR, Paster BJ, Leys EJ, et al. (2002) Molecular analysis of bacterial species associated with childhood caries. J Clin Microbiol 40: 1001-1009. https://doi.org/10.1128/jcm.40.3.1001-1009.2002 ![]() |
[72] |
de Carvalho FG, Silva DS, Hebling J, et al. (2006) Presence of mutans streptococci and Candida spp. in dental plaque/dentine of carious teeth and early childhood caries. Arch Oral Biol 51: 1024-1028. https://doi.org/10.1016/j.archoralbio.2006.06.001 ![]() |
[73] | Baena-Monroy T, Moreno-Maldonado V, Franco-Martínez F, et al. (2005) Candida albicans, Staphylococcus aureus and Streptococcus mutans colonization in patients wearing dental prosthesis. Med Oral Patol Oral Cir Bucal 10 Suppl1: E27-39. |
[74] |
Armitage GC, Cullinan MP (2010) Comparison of the clinical features of chronic and aggressive periodontitis. Periodontol 2000 53: 12-27. https://doi.org/10.1111/j.1600-0757.2010.00353.x ![]() |
[75] |
RJ G (1996) Consensus report. Periodontal diseases: pathogenesis and microbial factors. Ann Periodontol 1: 926-932. https://doi.org/10.1902/annals.1996.1.1.926 ![]() |
[76] |
Saito A, Inagaki S, Kimizuka R, et al. (2008) Fusobacterium nucleatum enhances invasion of human gingival epithelial and aortic endothelial cells by Porphyromonas gingivalis. FEMS Immunol Med Microbiol 54: 349-355. https://doi.org/10.1111/j.1574-695x.2008.00481.x ![]() |
[77] |
O'May GA, Reynolds N, Smith AR, et al. (2005) Effect of pH and antibiotics on microbial overgrowth in the stomachs and duodena of patients undergoing percutaneous endoscopic gastrostomy feeding. J Clin Microbiol 43: 3059-3065. https://doi.org/10.1128/jcm.43.7.3059-3065.2005 ![]() |
[78] |
Jacques I, Derelle J, Weber M, et al. (1998) Pulmonary evolution of cystic fibrosis patients colonized by Pseudomonas aeruginosa and/or Burkholderia cepacia. Eur J Pediatr 157: 427-431. https://doi.org/10.1007/s004310050844 ![]() |
[79] |
Liou TG, Adler FR, Fitzsimmons SC, et al. (2001) Predictive 5-year survivorship model of cystic fibrosis. Am J Epidemiol 153: 345-352. https://doi.org/10.1093/aje/153.4.345 ![]() |
[80] |
Duan K, Dammel C, Stein J, et al. (2003) Modulation of Pseudomonas aeruginosa gene expression by host microflora through interspecies communication. Mol Microbiol 50: 1477-1491. https://doi.org/10.1046/j.1365-2958.2003.03803.x ![]() |
[81] |
Brand A, Barnes JD, Mackenzie KS, et al. (2008) Cell wall glycans and soluble factors determine the interactions between the hyphae of Candida albicans and Pseudomonas aeruginosa. FEMS Microbiol Lett 287: 48-55. https://doi.org/10.1111/j.1574-6968.2008.01301.x ![]() |
[82] |
Chotirmall SH, McElvaney NG (2014) Fungi in the cystic fibrosis lung: bystanders or pathogens?. Int J Biochem Cell Biol 52: 161-173. https://doi.org/10.1016/j.biocel.2014.03.001 ![]() |
[83] |
Van Ewijk BE, Wolfs TF, Aerts PC, et al. (2007) RSV mediates Pseudomonas aeruginosa binding to cystic fibrosis and normal epithelial cells. Pediatr Res 61: 398-403. https://doi.org/10.1203/pdr.0b013e3180332d1c ![]() |
[84] |
Sibley CD, Rabin H, Surette MG (2006) Cystic fibrosis: a polymicrobial infectious disease. Future Microbiol 1: 53-61. https://doi.org/10.2217/17460913.1.1.53 ![]() |
[85] |
Tunney MM, Field TR, Moriarty TF, et al. (2008) Detection of anaerobic bacteria in high numbers in sputum from patients with cystic fibrosis. Am J Respir Crit Care Med 177: 995-1001. https://doi.org/10.1164/rccm.200708-1151oc ![]() |
[86] |
Dejea CM, Fathi P, Craig JM, et al. (2018) Patients with familial adenomatous polyposis harbor colonic biofilms containing tumorigenic bacteria. Science 359: 592-597. https://doi.org/10.1126/science.aah3648 ![]() |
[87] |
Drewes JL, White JR, Dejea CM, et al. (2017) High-resolution bacterial 16S rRNA gene profile meta-analysis and biofilm status reveal common colorectal cancer consortia. NPJ Biofilms Microbiomes 3: 34. https://doi.org/10.1038/s41522-017-0040-3 ![]() |
[88] |
Palusiak A (2022) Proteus mirabilis and Klebsiella pneumoniae as pathogens capable of causing co-infections and exhibiting similarities in their virulence factors. Front Cell Infect Microbiol 12: 991657. https://doi.org/10.3389/fcimb.2022.991657 ![]() |
[89] | Singh N, Mishra S, Mondal A, et al. (2022) Potential of desert medicinal plants for combating resistant biofilms in urinary tract infections. Appl Biochem Biotechnol . https://doi.org/10.1007/s12010-022-03950-4 |
[90] |
James GA, Swogger E, Wolcott R, et al. (2008) Biofilms in chronic wounds. Wound Repair Regen 16: 37-44. https://doi.org/10.1111/j.1524-475x.2007.00321.x ![]() |
[91] |
Citron DM, Goldstein EJ, Merriam CV, et al. (2007) Bacteriology of moderate-to-severe diabetic foot infections and in vitro activity of antimicrobial agents. J Clin Microbiol 45: 2819-2828. https://doi.org/10.1128/jcm.00551-07 ![]() |
[92] |
Segal N, Leibovitz E, Dagan R, et al. (2005) Acute otitis media-diagnosis and treatment in the era of antibiotic resistant organisms: updated clinical practice guidelines. Int J Pediatr Otorhinolaryngol 69: 1311-1319. https://doi.org/10.1016/j.ijporl.2005.05.003 ![]() |
[93] | Klein JO (2000) The burden of otitis media. Vaccine 19 Suppl 1: S2-8. https://doi.org/10.1016/s0264-410x(00)00271-1 |
[94] |
Faden H, Duffy L, Wasielewski R, et al. (1997) Relationship between nasopharyngeal colonization and the development of otitis media in children. Tonawanda/Williamsville Pediatrics. J Infect Dis 175: 1440-1445. https://doi.org/10.1086/516477 ![]() |
[95] |
Hament JM, Kimpen JL, Fleer A, et al. (1999) Respiratory viral infection predisposing for bacterial disease: a concise review. FEMS Immunol Med Microbiol 26: 189-195. https://doi.org/10.1111/j.1574-695x.1999.tb01389.x ![]() |
[96] |
Abramson JS, Wheeler JG (1994) Virus-induced neutrophil dysfunction: role in the pathogenesis of bacterial infections. Pediatr Infect J 13: 643-652. ![]() |
[97] |
Laufer AS, Metlay JP, Gent JF, et al. (2011) Microbial communities of the upper respiratory tract and otitis media in children. mBio 2: e00245-10. https://doi.org/10.1128/mbio.00245-10 ![]() |
[98] |
Muñoz-Elías EJ, Marcano J, Camilli A (2008) Isolation of Streptococcus pneumoniae biofilm mutants and their characterization during nasopharyngeal colonization. Infect Immun 76: 5049-5061. https://doi.org/10.1128/iai.00425-08 ![]() |
[99] |
Vidal JE, Ludewick HP, Kunkel RM, et al. (2011) The LuxS-dependent quorum-sensing system regulates early biofilm formation by Streptococcus pneumoniae strain D39. Infect Immun 79: 4050-4060. https://doi.org/10.1128/iai.05186-11 ![]() |
[100] |
Shak JR, Vidal JE, Klugman KP (2013) Influence of bacterial interactions on pneumococcal colonization of the nasopharynx. Trends Microbiol 21: 129-135. https://doi.org/10.1016/j.tim.2012.11.005 ![]() |
[101] |
Guiral S, Mitchell TJ, Martin B, et al. (2005) Competence-programmed predation of noncompetent cells in the human pathogen Streptococcus pneumoniae: genetic requirements. Proc Natl Acad Sci U S A 102: 8710-8715. https://doi.org/10.1073/pnas.0500879102 ![]() |
[102] |
Johnsborg O, Håvarstein LS (2009) Regulation of natural genetic transformation and acquisition of transforming DNA in Streptococcus pneumoniae. FEMS Microbiol Rev 33: 627-642. https://doi.org/10.1111/j.1574-6976.2009.00167.x ![]() |
[103] |
Weimer KE, Juneau RA, Murrah KA, et al. (2011) Divergent mechanisms for passive pneumococcal resistance to β-lactam antibiotics in the presence of Haemophilus influenzae. J Infect Dis 203: 549-555. https://doi.org/10.1093/infdis/jiq087 ![]() |
[104] |
Pereira CS, Thompson JA, Xavier KB (2013) AI-2-mediated signalling in bacteria. FEMS Microbiol Rev 37: 156-181. https://doi.org/10.1111/j.1574-6976.2012.00345.x ![]() |
[105] |
Dejea CM, Wick EC, Hechenbleikner EM, et al. (2014) Microbiota organization is a distinct feature of proximal colorectal cancers. Proc Natl Acad Sci U S A 111: 18321-18326. https://doi.org/10.1073/pnas.1406199111 ![]() |
[106] |
Wick EC, Rabizadeh S, Albesiano E, et al. (2014) Stat3 activation in murine colitis induced by enterotoxigenic Bacteroides fragilis. Inflamm Bowel Dis 20: 821-834. https://doi.org/10.1097/mib.0000000000000019 ![]() |
[107] |
Wild S, Roglic G, Green A, et al. (2004) Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27: 1047-1053. https://doi.org/10.2337/diacare.27.5.1047 ![]() |
[108] |
Armstrong DG, Boulton AJM, Bus SA (2017) Diabetic foot ulcers and their recurrence. N Engl J Med 376: 2367-2375. https://doi.org/10.1056/nejmra1615439 ![]() |
[109] |
Adler AI, Boyko EJ, Ahroni JH, et al. (1999) Lower-extremity amputation in diabetes. The independent effects of peripheral vascular disease, sensory neuropathy, and foot ulcers. Diabetes Care 22: 1029-1035. https://doi.org/10.2337/diacare.22.7.1029 ![]() |
[110] |
Oates A, Bowling FL, Boulton AJ, et al. (2012) Molecular and culture-based assessment of the microbial diversity of diabetic chronic foot wounds and contralateral skin sites. J Clin Microbiol 50: 2263-2271. https://doi.org/10.1128/jcm.06599-11 ![]() |
[111] |
Gontcharova V, Youn E, Sun Y, et al. (2010) A comparison of bacterial composition in diabetic ulcers and contralateral intact skin. Open Microbiol J 4: 8-19. https://doi.org/10.2174/1874285801004010008 ![]() |
[112] |
Percival SL, Malone M, Mayer D, et al. (2018) Role of anaerobes in polymicrobial communities and biofilms complicating diabetic foot ulcers. Int Wound J 15: 776-782. https://doi.org/10.1111/iwj.12926 ![]() |
[113] |
Wolcott RD, Hanson JD, Rees EJ, et al. (2016) Analysis of the chronic wound microbiota of 2,963 patients by 16S rDNA pyrosequencing. Wound Repair Regen 24: 163-174. https://doi.org/10.1111/wrr.12370 ![]() |
[114] |
Loesche M, Gardner SE, Kalan L, et al. (2017) Temporal stability in chronic wound microbiota is associated with poor healing. J Invest Dermatol 137: 237-244. https://doi.org/10.1016/j.jid.2016.08.009 ![]() |
[115] |
Ndosi M, Wright-Hughes A, Brown S, et al. (2018) Prognosis of the infected diabetic foot ulcer: a 12-month prospective observational study. Diabet Med 35: 78-88. https://doi.org/10.1111/dme.13537 ![]() |
[116] |
Malone M, Johani K, Jensen SO, et al. (2017) Next generation DNA sequencing of tissues from infected diabetic foot ulcers. EBioMedicine 21: 142-149. https://doi.org/10.1016/j.ebiom.2017.06.026 ![]() |
[117] | Shanmugam P, Jeva M, Susan SL (2013) The bacteriology of diabetic foot ulcers, with a special reference to multidrug resistant strains. J Clin Diagn Res 7: 441-445. https://doi.org/10.7860/jcdr/2013/5091.2794 |
[118] |
Gardner SE, Hillis SL, Heilmann K, et al. (2013) The neuropathic diabetic foot ulcer microbiome is associated with clinical factors. Diabetes 62: 923-930. https://doi.org/10.2337/db12-0771 ![]() |
[119] |
Jneid J, Lavigne JP, La Scola B, et al. (2017) The diabetic foot microbiota: A review. Hum Microbiome J 5–6: 1-6. https://doi.org/10.1016/j.humic.2017.09.002 ![]() |
[120] |
Cogen AL, Nizet V, Gallo RL (2008) Skin microbiota: a source of disease or defence?. Br J Dermatol 158: 442-455. https://doi.org/10.1111/j.1365-2133.2008.08437.x ![]() |
[121] | Kalan L, Loesche M, Hodkinson BP, et al. (2016) Redefining the chronic-wound microbiome: fungal communities are prevalent, dynamic, and associated with delayed healing. mBio 7. https://doi.org/10.1128/mbio.01058-16 |
[122] |
Mottola C, Mendes JJ, Cristino JM, et al. (2016) Polymicrobial biofilms by diabetic foot clinical isolates. Folia Microbiol (Praha) 61: 35-43. https://doi.org/10.1007/s12223-015-0401-3 ![]() |
[123] |
Davies JC, Alton EW, Bush A (2007) Cystic fibrosis. Bmj 335: 1255-1259. https://doi.org/10.1136/bmj.39391.713229.ad ![]() |
[124] |
Anderson MP, Gregory RJ, Thompson S, et al. (1991) Demonstration that CFTR is a chloride channel by alteration of its anion selectivity. Science 253: 202-205. https://doi.org/10.1126/science.1712984 ![]() |
[125] |
Mortensen J, Hansen A, Falk M, et al. (1993) Reduced effect of inhaled beta 2-adrenergic agonists on lung mucociliary clearance in patients with cystic fibrosis. Chest 103: 805-811. https://doi.org/10.1378/chest.103.3.805 ![]() |
[126] |
Arias SL, Brito IL (2021) Biophysical determinants of biofilm formation in the gut. Curr Opin Biomed Eng 18: 100275. https://doi.org/10.1016/j.cobme.2021.100275 ![]() |
[127] |
Schwarz-Linek J, Winkler A, Wilson LG, et al. (2010) Polymer-induced phase separation in Escherichia coli suspensions. Soft Matter 6: 4540-4549. https://doi.org/10.1039/C0SM00214C ![]() |
[128] |
Secor PR, Michaels LA, Ratjen A, et al. (2018) Entropically driven aggregation of bacteria by host polymers promotes antibiotic tolerance in Pseudomonas aeruginosa. Proc Natl Acad Sci U S A 115: 10780-10785. https://doi.org/10.1073/pnas.1806005115 ![]() |
[129] |
Hubert D, Réglier-Poupet H, Sermet-Gaudelus I, et al. (2013) Association between Staphylococcus aureus alone or combined with Pseudomonas aeruginosa and the clinical condition of patients with cystic fibrosis. J Cyst Fibros 12: 497-503. https://doi.org/10.1016/j.jcf.2012.12.003 ![]() |
[130] |
Razvi S, Quittell L, Sewall A, et al. (2009) Respiratory microbiology of patients with cystic fibrosis in the United States, 1995 to 2005. Chest 136: 1554-1560. https://doi.org/10.1378/chest.09-0132 ![]() |
[131] |
Folkesson A, Jelsbak L, Yang L, et al. (2012) Adaptation of Pseudomonas aeruginosa to the cystic fibrosis airway: an evolutionary perspective. Nat Rev Microbiol 10: 841-851. https://doi.org/10.1038/nrmicro2907 ![]() |
[132] |
Harrison F (2007) Microbial ecology of the cystic fibrosis lung. Microbiol Read 153: 917-923. https://doi.org/10.1099/mic.0.2006/004077-0 ![]() |
[133] |
Clark SE (2020) Commensal bacteria in the upper respiratory tract regulate susceptibility to infection. Curr Opin Immunol 66: 42-49. https://doi.org/10.1016/j.coi.2020.03.010 ![]() |
[134] |
Davies JC (2002) Pseudomonas aeruginosa in cystic fibrosis: pathogenesis and persistence. Paediatr Respir Rev 3: 128-134. https://doi.org/10.1016/s1526-0550(02)00003-3 ![]() |
[135] |
O'Brien S, Fothergill JL (2017) The role of multispecies social interactions in shaping Pseudomonas aeruginosa pathogenicity in the cystic fibrosis lung. FEMS Microbiol Lett 364. https://doi.org/10.1093/femsle/fnx128 ![]() |
[136] |
Valenza G, Tappe D, Turnwald D, et al. (2008) Prevalence and antimicrobial susceptibility of microorganisms isolated from sputa of patients with cystic fibrosis. J Cyst Fibros 7: 123-127. https://doi.org/10.1016/j.jcf.2007.06.006 ![]() |
[137] |
Briaud P, Camus L, Bastien S, et al. (2019) Coexistence with Pseudomonas aeruginosa alters Staphylococcus aureus transcriptome, antibiotic resistance and internalization into epithelial cells. Sci Rep 9: 16564. https://doi.org/10.1038/s41598-019-52975-z ![]() |
[138] |
Fischer AJ, Singh SB, LaMarche MM, et al. (2021) Sustained coinfections with Staphylococcus aureus and Pseudomonas aeruginosa in cystic fibrosis. Am J Respir Crit Care Med 203: 328-338. https://doi.org/10.1164/rccm.202004-1322oc ![]() |
[139] |
Briaud P, Bastien S, Camus L, et al. (2020) Impact of coexistence phenotype between Staphylococcus aureus and Pseudomonas aeruginosa isolates on clinical outcomes among cystic fibrosis patients. Front Cell Infect Microbiol 10: 266. https://doi.org/10.3389/fcimb.2020.00266 ![]() |
[140] |
Emerson J, Rosenfeld M, McNamara S, et al. (2002) Pseudomonas aeruginosa and other predictors of mortality and morbidity in young children with cystic fibrosis. Pediatr Pulmonol 34: 91-100. https://doi.org/10.1002/ppul.10127 ![]() |
[141] |
Sajjan U, Thanassoulis G, Cherapanov V, et al. (2001) Enhanced susceptibility to pulmonary infection with Burkholderia cepacia in Cftr(-/-) mice. Infect Immun 69: 5138-5150. https://doi.org/10.1128/iai.69.8.5138-5150.2001 ![]() |
[142] |
De Soyza A, McDowell A, Archer L, et al. (2001) Burkholderia cepacia complex genomovars and pulmonary transplantation outcomes in patients with cystic fibrosis. Lancet 358: 1780-1781. https://doi.org/10.1016/s0140-6736(01)06808-8 ![]() |
[143] |
Chotirmall SH, O'Donoghue E, Bennett K, et al. (2010) Sputum Candida albicans presages FEV1 decline and hospital-treated exacerbations in cystic fibrosis. Chest 138: 1186-1195. https://doi.org/10.1378/chest.09-2996 ![]() |
[144] |
Pihet M, Carrere J, Cimon B, et al. (2009) Occurrence and relevance of filamentous fungi in respiratory secretions of patients with cystic fibrosis–a review. Med Mycol 47: 387-397. https://doi.org/10.1080/13693780802609604 ![]() |
[145] |
Hogan DA, Vik A, Kolter R (2004) A Pseudomonas aeruginosa quorum-sensing molecule influences Candida albicans morphology. Mol Microbiol 54: 1212-1223. https://doi.org/10.1111/j.1365-2958.2004.04349.x ![]() |
[146] |
Reece E, Segurado R, Jackson A, et al. (2017) Co-colonisation with Aspergillus fumigatus and Pseudomonas aeruginosa is associated with poorer health in cystic fibrosis patients: an Irish registry analysis. BMC Pulm Med 17: 70. https://doi.org/10.1186/s12890-017-0416-4 ![]() |
[147] | Coman I, Bilodeau L, Lavoie A, et al. (2017) Ralstonia mannitolilytica in cystic fibrosis: A new predictor of worse outcomes. Respir Med Case Rep 20: 48-50. https://doi.org/10.1016/j.rmcr.2016.11.014 |
[148] |
Lim YW, Evangelista JS, Schmieder R, et al. (2014) Clinical insights from metagenomic analysis of sputum samples from patients with cystic fibrosis. J Clin Microbiol 52: 425-437. https://doi.org/10.1128/jcm.02204-13 ![]() |
[149] |
Billard L, Le Berre R, Pilorgé L, et al. (2017) Viruses in cystic fibrosis patients' airways. Crit Rev Microbiol 43: 690-708. https://doi.org/10.1080/1040841x.2017.1297763 ![]() |
[150] |
Lopes SP, Ceri H, Azevedo NF, et al. (2012) Antibiotic resistance of mixed biofilms in cystic fibrosis: impact of emerging microorganisms on treatment of infection. Int J Antimicrob Agents 40: 260-263. https://doi.org/10.1016/j.ijantimicag.2012.04.020 ![]() |
[151] |
Verdial C, Serrano I, Tavares L, et al. (2023) Mechanisms of antibiotic and biocide resistance that contribute to Pseudomonas aeruginosa persistence in the hospital environment. Biomedicines 11. https://doi.org/10.3390/biomedicines11041221 ![]() |
[152] |
Aloke C, Achilonu I (2023) Coping with the ESKAPE pathogens: Evolving strategies, challenges and future prospects. Microb Pathog 175: 105963. https://doi.org/10.1016/j.micpath.2022.105963 ![]() |
[153] |
Ciofu O, Tolker-Nielsen T (2019) Tolerance and resistance of Pseudomonas aeruginosa biofilms to antimicrobial agents-how P. aeruginosa can escape antibiotics. Front Microbiol 10: 913. https://doi.org/10.3389/fmicb.2019.00913 ![]() |
[154] |
Akita S, Tanaka K, Hirano A (2006) Lower extremity reconstruction after necrotising fasciitis and necrotic skin lesions using a porcine-derived skin substitute. J Plast Reconstr Aesthet Surg 59: 759-763. https://doi.org/10.1016/j.bjps.2005.11.021 ![]() |
[155] |
Levine EG, Manders SM (2005) Life-threatening necrotizing fasciitis. Clin Dermatol 23: 144-147. https://doi.org/10.1016/j.clindermatol.2004.06.014 ![]() |
[156] |
Pastar I, Nusbaum AG, Gil J, et al. (2013) Interactions of methicillin resistant Staphylococcus aureus USA300 and Pseudomonas aeruginosa in polymicrobial wound infection. PLoS One 8: e56846. https://doi.org/10.1371/journal.pone.0056846 ![]() |
[157] |
Filkins LM, O'Toole GA (2015) Cystic fibrosis lung infections: polymicrobial, complex, and hard to treat. PLoS Pathog 11: e1005258. https://doi.org/10.1371/journal.ppat.1005258 ![]() |
[158] |
Kirketerp-Møller K, Jensen P, Fazli M, et al. (2008) Distribution, organization, and ecology of bacteria in chronic wounds. J Clin Microbiol 46: 2717-2722. https://doi.org/10.1128/jcm.00501-08 ![]() |
[159] |
Trizna EY, Yarullina MN, Baidamshina DR, et al. (2020) Bidirectional alterations in antibiotics susceptibility in Staphylococcus aureus-Pseudomonas aeruginosa dual-species biofilm. Sci Rep 10: 14849. https://doi.org/10.1038/s41598-020-71834-w ![]() |
[160] |
Biswas L, Götz F (2021) Molecular mechanisms of Staphylococcus and Pseudomonas interactions in cystic fibrosis. Front Cell Infect Microbiol 11: 824042. https://doi.org/10.3389/fcimb.2021.824042 ![]() |
[161] |
Kessler E, Safrin M, Olson JC, et al. (1993) Secreted LasA of Pseudomonas aeruginosa is a staphylolytic protease. J Biol Chem 268: 7503-7508. https://doi.org/10.1016/S0021-9258(18)53203-8 ![]() |
[162] |
Hotterbeekx A, Kumar-Singh S, Goossens H, et al. (2017) In vivo and In vitro interactions between Pseudomonas aeruginosa and Staphylococcus spp. Front Cell Infect Microbiol 7: 106. https://doi.org/10.3389/fcimb.2017.00106 ![]() |
[163] |
Wood TL, Gong T, Zhu L, et al. (2018) Rhamnolipids from Pseudomonas aeruginosa disperse the biofilms of sulfate-reducing bacteria. NPJ Biofilms Microbiomes 4: 22. https://doi.org/10.1038/s41522-018-0066-1 ![]() |
[164] |
Marques CN, Morozov A, Planzos P, et al. (2014) The fatty acid signaling molecule cis-2-decenoic acid increases metabolic activity and reverts persister cells to an antimicrobial-susceptible state. Appl Env Microbiol 80: 6976-6991. https://doi.org/10.1128/aem.01576-14 ![]() |
[165] |
Qazi S, Middleton B, Muharram SH, et al. (2006) N-acylhomoserine lactones antagonize virulence gene expression and quorum sensing in Staphylococcus aureus. Infect Immun 74: 910-919. https://doi.org/10.1128/iai.74.2.910-919.2006 ![]() |
[166] |
Voggu L, Schlag S, Biswas R, et al. (2006) Microevolution of cytochrome bd oxidase in Staphylococci and its implication in resistance to respiratory toxins released by Pseudomonas. J Bacteriol 188: 8079-8086. https://doi.org/10.1128/jb.00858-06 ![]() |
[167] | Noto MJ, Burns WJ, Beavers WN, et al. (2017) Mechanisms of pyocyanin toxicity and genetic determinants of resistance in Staphylococcus aureus. J Bacteriol 199. https://doi.org/10.1128/jb.00221-17 |
[168] |
Machan ZA, Taylor GW, Pitt TL, et al. (1992) 2-Heptyl-4-hydroxyquinoline N-oxide, an antistaphylococcal agent produced by Pseudomonas aeruginosa. J Antimicrob Chemother 30: 615-623. https://doi.org/10.1093/jac/30.5.615 ![]() |
[169] |
Szamosvári D, Böttcher T (2017) An unsaturated quinolone N-Oxide of Pseudomonas aeruginosa modulates growth and virulence of Staphylococcus aureus. Angew Chem Int Ed Engl 56: 7271-7275. https://doi.org/10.1002/anie.201702944 ![]() |
[170] |
Hoffman LR, Déziel E, D'Argenio DA, et al. (2006) Selection for Staphylococcus aureus small-colony variants due to growth in the presence of Pseudomonas aeruginosa. Proc Natl Acad Sci U S A 103: 19890-19895. https://doi.org/10.1073/pnas.0606756104 ![]() |
[171] |
Falcon MA, Mansito TB, Carnicero A, et al. (1989) L-form-like colonies of Staphylococcus aureus induced by an extracellular lytic enzyme from Pseudomonas aeruginosa. J Clin Microbiol 27: 1650-1654. https://doi.org/10.1128/jcm.27.7.1650-1654.1989 ![]() |
[172] |
Pallett R, Leslie LJ, Lambert PA, et al. (2019) Anaerobiosis influences virulence properties of Pseudomonas aeruginosa cystic fibrosis isolates and the interaction with Staphylococcus aureus. Sci Rep 9: 6748. https://doi.org/10.1038/s41598-019-42952-x ![]() |
[173] |
Armbruster CR, Wolter DJ, Mishra M, et al. (2016) Staphylococcus aureus protein A mediates interspecies interactions at the cell surface of Pseudomonas aeruginosa. mBio 7. https://doi.org/10.1128/mbio.00538-16 ![]() |
[174] |
Price CE, Brown DG, Limoli DH, et al. (2020) Exogenous alginate protects Staphylococcus aureus from killing by Pseudomonas aeruginosa. J Bacteriol 202. https://doi.org/10.1128/jb.00559-19 ![]() |
[175] |
Limoli DH, Whitfield GB, Kitao T, et al. (2017) Pseudomonas aeruginosa alginate overproduction promotes coexistence with Staphylococcus aureus in a model of cystic fibrosis respiratory infection. mBio 8. https://doi.org/10.1128/mbio.00186-17 ![]() |
[176] |
Orazi G, Ruoff KL, O'Toole GA (2019) Pseudomonas aeruginosa increases the sensitivity of biofilm-grown Staphylococcus aureus to membrane-targeting antiseptics and antibiotics. mBio 10. https://doi.org/10.1128/mbio.01501-19 ![]() |
[177] |
Orazi G, O'Toole GA (2017) Pseudomonas aeruginosa alters Staphylococcus aureus sensitivity to vancomycin in a biofilm model of cystic fibrosis infection. mBio 8. https://doi.org/10.1128/mbio.00873-17 ![]() |
[178] |
Baldan R, Cigana C, Testa F, et al. (2014) Adaptation of Pseudomonas aeruginosa in Cystic Fibrosis airways influences virulence of Staphylococcus aureus in vitro and murine models of co-infection. PLoS One 9: e89614. https://doi.org/10.1371/journal.pone.0089614 ![]() |
[179] |
Zhao K, Du L, Lin J, et al. (2018) Pseudomonas aeruginosa quorum-sensing and type VI secretion system can direct interspecific coexistence during evolution. Front Microbiol 9: 2287. https://doi.org/10.3389/fmicb.2018.02287 ![]() |
[180] |
Soares A, Alexandre K, Etienne M (2020) Tolerance and persistence of Pseudomonas aeruginosa in biofilms exposed to antibiotics: Molecular mechanisms, antibiotic strategies and therapeutic perspectives. Front Microbiol 11: 2057. https://doi.org/10.3389/fmicb.2020.02057 ![]() |
[181] |
Fisher RA, Gollan B, Helaine S (2017) Persistent bacterial infections and persister cells. Nat Rev Microbiol 15: 453-464. https://doi.org/10.1038/nrmicro.2017.42 ![]() |
[182] |
Grassi L, Di Luca M, Maisetta G, et al. (2017) Generation of persister cells of Pseudomonas aeruginosa and Staphylococcus aureus by chemical treatment and evaluation of their susceptibility to membrane-targeting agents. Front Microbiol 8: 1917. https://doi.org/10.3389/fmicb.2017.01917 ![]() |
[183] |
Mulcahy LR, Burns JL, Lory S, et al. (2010) Emergence of Pseudomonas aeruginosa strains producing high levels of persister cells in patients with cystic fibrosis. J Bacteriol 192: 6191-6199. https://doi.org/10.1128/jb.01651-09 ![]() |
[184] |
Tamber S, Cheung AL (2009) SarZ promotes the expression of virulence factors and represses biofilm formation by modulating SarA and agr in Staphylococcus aureus. Infect Immun 77: 419-428. https://doi.org/10.1128/iai.00859-08 ![]() |
[185] |
Melter O, Radojevič B (2010) Small colony variants of Staphylococcus aureus–review. Folia Microbiol Praha 55: 548-558. https://doi.org/10.1007/s12223-010-0089-3 ![]() |
[186] |
Pagels M, Fuchs S, Pané-Farré J, et al. (2010) Redox sensing by a Rex-family repressor is involved in the regulation of anaerobic gene expression in Staphylococcus aureus. Mol Microbiol 76: 1142-1161. https://doi.org/10.1111/j.1365-2958.2010.07105.x ![]() |
[187] |
Tuchscherr L, Löffler B, Proctor RA (2020) Persistence of Staphylococcus aureus: Multiple metabolic pathways impact the expression of virulence factors in small-colony variants (SCVs). Front Microbiol 11: 1028. https://doi.org/10.3389/fmicb.2020.01028 ![]() |
[188] |
Besier S, Smaczny C, von Mallinckrodt C, et al. (2007) Prevalence and clinical significance of Staphylococcus aureus small-colony variants in cystic fibrosis lung disease. J Clin Microbiol 45: 168-172. https://doi.org/10.1128/jcm.01510-06 ![]() |
[189] |
Painter KL, Strange E, Parkhill J, et al. (2015) Staphylococcus aureus adapts to oxidative stress by producing H2O2-resistant small-colony variants via the SOS response. Infect Immun 83: 1830-1844. https://doi.org/10.1128/iai.03016-14 ![]() |
[190] |
Wolter DJ, Emerson JC, McNamara S, et al. (2013) Staphylococcus aureus small-colony variants are independently associated with worse lung disease in children with cystic fibrosis. Clin Infect Dis 57: 384-391. https://doi.org/10.1093/cid/cit270 ![]() |
[191] |
Xu Y, Zhang B, Wang L, et al. (2020) Unusual features and molecular pathways of Staphylococcus aureus L-form bacteria. Microb Pathog 140: 103970. https://doi.org/10.1016/j.micpath.2020.103970 ![]() |
[192] |
Michailova L, Kussovsky V, Radoucheva T, et al. (2007) Persistence of Staphylococcus aureus L-form during experimental lung infection in rats. FEMS Microbiol Lett 268: 88-97. https://doi.org/10.1111/j.1574-6968.2006.00567.x ![]() |
[193] |
Malhotra S, Limoli DH, English AE, et al. (2018) Mixed communities of mucoid and nonmucoid Pseudomonas aeruginosa exhibit enhanced resistance to host antimicrobials. mBio 9. https://doi.org/10.1128/mbio.00275-18 ![]() |
[194] |
Yang N, Cao Q, Hu S, et al. (2020) Alteration of protein homeostasis mediates the interaction of Pseudomonas aeruginosa with Staphylococcus aureus. Mol Microbiol 114: 423-442. https://doi.org/10.1111/mmi.14519 ![]() |
[195] |
Mashburn LM, Jett AM, Akins DR, et al. (2005) Staphylococcus aureus serves as an iron source for Pseudomonas aeruginosa during in vivo coculture. J Bacteriol 187: 554-566. https://doi.org/10.1128/jb.187.2.554-566.2005 ![]() |
[196] |
Samad T, Billings N, Birjiniuk A, et al. (2017) Swimming bacteria promote dispersal of non-motile staphylococcal species. Isme J 11: 1933-1937. https://doi.org/10.1038/ismej.2017.23 ![]() |
[197] |
Pernet E, Guillemot L, Burgel PR, et al. (2014) Pseudomonas aeruginosa eradicates Staphylococcus aureus by manipulating the host immunity. Nat Commun 5: 5105. https://doi.org/10.1038/ncomms6105 ![]() |
[198] |
Nevalainen TJ, Graham GG, Scott KF (2008) Antibacterial actions of secreted phospholipases A2. Review. Biochim Biophys Acta 1781: 1-9. https://doi.org/10.1016/j.bbalip.2007.12.001 ![]() |
[199] |
Mottola C, Semedo-Lemsaddek T, Mendes JJ, et al. (2016) Molecular typing, virulence traits and antimicrobial resistance of diabetic foot staphylococci. J Biomed Sci 23: 33. https://doi.org/10.1186/s12929-016-0250-7 ![]() |
[200] | WHO Regional Office for Europe/European Centre for Disease Prevention and ControlAntimicrobial resistance surveillance in Europe 2022–2020 data, Copenhagen, DN, WHO Regional Office for Europe (2022). |
[201] |
Pletzer D, Hancock RE (2016) Antibiofilm peptides: potential as broad-spectrum agents. J Bacteriol 198: 2572-2578. https://doi.org/10.1128/jb.00017-16 ![]() |
[202] |
Wu H, Moser C, Wang HZ, et al. (2015) Strategies for combating bacterial biofilm infections. Int J Oral Sci 7: 1-7. https://doi.org/10.1038/ijos.2014.65 ![]() |
[203] | Yin W, Wang Y, Liu L, et al. (2019) Biofilms: The microbial ‘protective clothing’ in extreme environments. Int J Mol Sci 20. https://doi.org/10.3390/ijms20143423 |
[204] | Truong-Bolduc QC, Khan NS, Vyas JM, et al. (2017) Tet38 efflux pump affects Staphylococcus aureus internalization by epithelial cells through interaction with CD36 and contributes to bacterial escape from acidic and nonacidic phagolysosomes. Infect Immun 85. https://doi.org/10.1128/iai.00862-16 |
[205] |
Tognon M, Köhler T, Gdaniec BG, et al. (2017) Co-evolution with Staphylococcus aureus leads to lipopolysaccharide alterations in Pseudomonas aeruginosa. Isme J 11: 2233-2243. https://doi.org/10.1038/ismej.2017.83 ![]() |
[206] |
Edwards AM (2012) Phenotype switching is a natural consequence of Staphylococcus aureus replication. J Bacteriol 194: 5404-5412. https://doi.org/10.1128/jb.00948-12 ![]() |
[207] |
Depoorter E, Bull MJ, Peeters C, et al. (2016) Burkholderia: an update on taxonomy and biotechnological potential as antibiotic producers. Appl Microbiol Biotechnol 100: 5215-5229. https://doi.org/10.1007/s00253-016-7520-x ![]() |
[208] |
Mahenthiralingam E, Urban TA, Goldberg JB (2005) The multifarious, multireplicon Burkholderia cepacia complex. Nat Rev Microbiol 3: 144-156. https://doi.org/10.1038/nrmicro1085 ![]() |
[209] |
Schwab U, Abdullah LH, Perlmutt OS, et al. (2014) Localization of Burkholderia cepacia complex bacteria in cystic fibrosis lungs and interactions with Pseudomonas aeruginosa in hypoxic mucus. Infect Immun 82: 4729-4745. https://doi.org/10.1128/iai.01876-14 ![]() |
[210] |
Costello A, Reen FJ, O'Gara F, et al. (2014) Inhibition of co-colonizing cystic fibrosis-associated pathogens by Pseudomonas aeruginosa and Burkholderia multivorans. Microbiol Read 160: 1474-1487. https://doi.org/10.1099/mic.0.074203-0 ![]() |
[211] |
Bakkal S, Robinson SM, Ordonez CL, et al. (2010) Role of bacteriocins in mediating interactions of bacterial isolates taken from cystic fibrosis patients. Microbiol Read 156: 2058-2067. https://doi.org/10.1099/mic.0.036848-0 ![]() |
[212] |
Tomlin KL, Coll OP, Ceri H (2001) Interspecies biofilms of Pseudomonas aeruginosa and Burkholderia cepacia. Can J Microbiol 47: 949-954. https://doi.org/10.1139/w01-095 ![]() |
[213] |
McKenney D, Brown KE, Allison DG (1995) Influence of Pseudomonas aeruginosa exoproducts on virulence factor production in Burkholderia cepacia: evidence of interspecies communication. J Bacteriol 177: 6989-6992. https://doi.org/10.1128/jb.177.23.6989-6992.1995 ![]() |
[214] |
Riedel K, Hentzer M, Geisenberger O, et al. (2001) N-acylhomoserine-lactone-mediated communication between Pseudomonas aeruginosa and Burkholderia cepacia in mixed biofilms. Microbiol Read 147: 3249-3262. https://doi.org/10.1099/00221287-147-12-3249 ![]() |
[215] |
Lewenza S, Visser MB, Sokol PA (2002) Interspecies communication between Burkholderia cepacia and Pseudomonas aeruginosa. Can J Microbiol 48: 707-716. https://doi.org/10.1139/w02-068 ![]() |
[216] |
Chattoraj SS, Murthy R, Ganesan S, et al. (2010) Pseudomonas aeruginosa alginate promotes Burkholderia cenocepacia persistence in cystic fibrosis transmembrane conductance regulator knockout mice. Infect Immun 78: 984-993. https://doi.org/10.1128/iai.01192-09 ![]() |
[217] | Kaplan NM, Khader YS, Ghabashineh DM (2022) Laboratory diagnosis, antimicrobial susceptibility and genuine clinical spectrum of Streptococcus anginosus group; our experience at a university hospital. Med Arch Sarajevo Bosnia Herzeg 76: 252-258. https://doi.org/10.5455/medarh.2022.76.252-258 |
[218] |
Pilarczyk-Zurek M, Sitkiewicz I, Koziel J (2022) The clinical view on Streptococcus anginosus group-opportunistic pathogens coming out of hiding. Front Microbiol 13: 956677. https://doi.org/10.3389/fmicb.2022.956677 ![]() |
[219] |
Sibley CD, Grinwis ME, Field TR, et al. (2010) McKay agar enables routine quantification of the ‘Streptococcus milleri’ group in cystic fibrosis patients. J Med Microbiol 59: 534-540. https://doi.org/10.1099/jmm.0.016592-0 ![]() |
[220] |
Agarwal R, Chakrabarti A, Shah A, et al. (2013) Allergic bronchopulmonary aspergillosis: review of literature and proposal of new diagnostic and classification criteria. Clin Exp Allergy 43: 850-873. https://doi.org/10.1111/cea.12141 ![]() |
[221] |
Amin R, Dupuis A, Aaron SD, et al. (2010) The effect of chronic infection with Aspergillus fumigatus on lung function and hospitalization in patients with cystic fibrosis. Chest 137: 171-176. https://doi.org/10.1378/chest.09-1103 ![]() |
[222] |
Moree WJ, Phelan VV, Wu CH, et al. (2012) Interkingdom metabolic transformations captured by microbial imaging mass spectrometry. Proc Natl Acad Sci U S A 109: 13811-13816. https://doi.org/10.1073/pnas.1206855109 ![]() |
[223] |
Briard B, Bomme P, Lechner BE, et al. (2015) Pseudomonas aeruginosa manipulates redox and iron homeostasis of its microbiota partner Aspergillus fumigatus via phenazines. Sci Rep 5: 8220. https://doi.org/10.1038/srep08220 ![]() |
[224] |
Wang Y, Wilks JC, Danhorn T, et al. (2011) Phenazine-1-carboxylic acid promotes bacterial biofilm development via ferrous iron acquisition. J Bacteriol 193: 3606-3617. https://doi.org/10.1128/jb.00396-11 ![]() |
[225] |
Marvig RL, Damkiær S, Khademi SM, et al. (2014) Within-host evolution of Pseudomonas aeruginosa reveals adaptation toward iron acquisition from hemoglobin. mBio 5: e00966-14. https://doi.org/10.1128/mbio.00966-14 ![]() |
[226] | Talapko J, Juzbašić M, Matijević T, et al. (2021) Candida albicans-The virulence factors and clinical manifestations of infection. J Fungi Basel 7. https://doi.org/10.3390/jof7020079 |
[227] |
Jayatilake JA, Samaranayake YH, Samaranayake LP (2008) A comparative study of candidal invasion in rabbit tongue mucosal explants and reconstituted human oral epithelium. Mycopathologia 165: 373-380. https://doi.org/10.1007/s11046-008-9096-1 ![]() |
[228] |
Richard ML, Nobile CJ, Bruno VM, et al. (2005) Candida albicans biofilm-defective mutants. Eukaryot Cell 4: 1493-1502. https://doi.org/10.1128/ec.4.8.1493-1502.2005 ![]() |
[229] |
Bjarnsholt T, Jensen P, Jakobsen TH, et al. (2010) Quorum sensing and virulence of Pseudomonas aeruginosa during lung infection of cystic fibrosis patients. PLoS One 5: e10115. https://doi.org/10.1371/journal.pone.0010115 ![]() |
[230] |
McAlester G, O'Gara F, Morrissey JP (2008) Signal-mediated interactions between Pseudomonas aeruginosa and Candida albicans. J Med Microbiol 57: 563-569. https://doi.org/10.1099/jmm.0.47705-0 ![]() |
[231] |
Morales DK, Grahl N, Okegbe C, et al. (2013) Control of Candida albicans metabolism and biofilm formation by Pseudomonas aeruginosa phenazines. mBio 4: e00526-12. https://doi.org/10.1128/mbio.00526-12 ![]() |
[232] |
DeVault JD, Kimbara K, Chakrabarty AM (1990) Pulmonary dehydration and infection in cystic fibrosis: evidence that ethanol activates alginate gene expression and induction of mucoidy in Pseudomonas aeruginosa. Mol Microbiol 4: 737-745. https://doi.org/10.1111/j.1365-2958.1990.tb00644.x ![]() |
[233] |
Greenberg SS, Zhao X, Hua L, et al. (1999) Ethanol inhibits lung clearance of Pseudomonas aeruginosa by a neutrophil and nitric oxide-dependent mechanism, in vivo. Alcohol Clin Exp Res 23: 735-744. https://doi.org/10.1111/j.1530-0277.1999.tb04177.x ![]() |
[234] |
Goral J, Karavitis J, Kovacs EJ (2008) Exposure-dependent effects of ethanol on the innate immune system. Alcohol 42: 237-247. https://doi.org/10.1016/j.alcohol.2008.02.003 ![]() |
[235] |
Korem M, Gov Y, Rosenberg M (2010) Global gene expression in Staphylococcus aureus following exposure to alcohol. Microb Pathog 48: 74-84. https://doi.org/10.1016/j.micpath.2009.11.002 ![]() |
[236] |
Nwugo CC, Arivett BA, Zimbler DL, et al. (2012) Effect of ethanol on differential protein production and expression of potential virulence functions in the opportunistic pathogen Acinetobacter baumannii. PLoS One 7: e51936. https://doi.org/10.1371/journal.pone.0051936 ![]() |
[237] |
Cugini C, Calfee MW, Farrow JM, et al. (2007) Farnesol, a common sesquiterpene, inhibits PQS production in Pseudomonas aeruginosa. Mol Microbiol 65: 896-906. https://doi.org/10.1111/j.1365-2958.2007.05840.x ![]() |
[238] |
Kerr JR, Taylor GW, Rutman A, et al. (1999) Pseudomonas aeruginosa pyocyanin and 1-hydroxyphenazine inhibit fungal growth. J Clin Pathol 52: 385-387. https://doi.org/10.1136/jcp.52.5.385 ![]() |
[239] |
Lopez-Medina E, Fan D, Coughlin LA, et al. (2015) Candida albicans inhibits Pseudomonas aeruginosa virulence through suppression of pyochelin and pyoverdine biosynthesis. PLoS Pathog 11: e1005129. https://doi.org/10.1371/journal.ppat.1005129 ![]() |
[240] |
Wat D, Gelder C, Hibbitts S, et al. (2008) The role of respiratory viruses in cystic fibrosis. J Cyst Fibros 7: 320-328. https://doi.org/10.1016/j.jcf.2007.12.002 ![]() |
[241] |
de Vrankrijker AM, Wolfs TF, Ciofu O, et al. (2009) Respiratory syncytial virus infection facilitates acute colonization of Pseudomonas aeruginosa in mice. J Med Virol 81: 2096-2103. https://doi.org/10.1002/jmv.21623 ![]() |
[242] |
Oliver BG, Lim S, Wark P, et al. (2008) Rhinovirus exposure impairs immune responses to bacterial products in human alveolar macrophages. Thorax 63: 519-525. https://doi.org/10.1136/thx.2007.081752 ![]() |
1. | Marco Kemmerling, Daniel Lütticke, Robert H. Schmitt, Beyond games: a systematic review of neural Monte Carlo tree search applications, 2024, 54, 0924-669X, 1020, 10.1007/s10489-023-05240-w | |
2. | weijin mao, Wenzhen Wu, Pierluigi Siano, Hazlie Mokhlis, 2024, A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM, 9781510679795, 394, 10.1117/12.3024734 | |
3. | Yixin Zhuo, Ling Li, Jian Tang, Wenchuan Meng, Zhanhong Huang, Kui Huang, Jiaqiu Hu, Yiming Qin, Houjian Zhan, Zhencheng Liang, Optimal real-time power dispatch of power grid with wind energy forecasting under extreme weather, 2023, 20, 1551-0018, 14353, 10.3934/mbe.2023642 |
Algorithm 1: Detailed algorithm Tree_UCT |
Input: The load state LSi; The root node RN_s0; The tree node RN_s; The reward re_v; Time threshold TN; Number of visited node Ns; The estimated value Qs. |
Output: Tree_UCT |
1 //Create Tree_UCT sequence LSi and create the root node. |
2 TreeUCTsequence[LS_i]=0; |
3 RN_s0=LS0; |
4 //Within the calculated time limit. |
5 for tn=1 to TN do |
![]() |
Algorithm 2: MCTS-CI Algorithm |
Input: Load power series [P1,P2,…,P(t−1)]; Preprocessed data series [P′1,P′2,…,P′(t−1)]; |
Time threshold Tleft; Create the root node RS_node0 of the state Ls0; Initial state Ls0 |
1. Output: The RL-MCTS model RM. |
2 //Training RL-MCTS model. |
3 //Construct MCTSsearch Ls0. |
4 if: t≤Tleft then |
![]() |
Load type | No. 1 house | No. 2 house | No. 3 house | No. 4 house | No. 5 house | No. 6 house |
REF | 107 | 106 | 107 | 107 | 107 | 107 |
MWO | 106 | 106 | 107 | 105 | 107 | 106 |
IC | 105 | 105 | 107 | 105 | 105 | 105 |
WH | 107 | 107 | 107 | 106 | 107 | 106 |
Methods | Datasets | |||
No. 1 house | No. 2 house | No. 3 house | No. 4 house | |
FHMM | 67.13 ± 0.36 | 69.83 ± 1.71 | 54.56 ± 0.98 | 63.68 ± 4.32 |
KNN | 70.34 ± 1.23 | 73.56 ± 0.89 | 62.78 ± 0.65 | 74.69 ± 2.31 |
CNN | 78.24 ± 1.12 | 75.43 ± 0.62 | 77.23 ± 4.12 | 80.68 ± 0.89 |
Bi-LSTM | 87.78 ± 4.13 | 79.56 ± 2.92 | 85.57 ± 2.54 | 85.97 ± 5.18 |
MCTS-CI | 95.56 ± 0.19 | 89.91 ± 0.47 | 90.95 ± 0.42 | 92.22 ± 0.34 |
Criteria | Methods | Datasets | |||
No. 1 house | No. 2 house | No. 3 house | No. 4 house | ||
H−loss | FHMM | 0.021 ± 0.002 | 0.019 ± 0.001 | 0.017 ± 0.002 | 0.020 ± 0.001 |
KNN | 0.018 ± 0.001 | 0.016 ± 0.002 | 0.014 ± 0.001 | 0.015 ± 0.004 | |
CNN | 0.014 ± 0.003 | 0.012 ± 0.004 | 0.011 ± 0.003 | 0.013 ± 0.002 | |
Bi-LSTM | 0.009 ± 0.002 | 0.008 ± 0.001 | 0.006 ± 0.001 | 0.009 ± 0.003 | |
MCTS-CI | 0.003 ± 0.001 | 0.003 ± 0.001 | 0.002 ± 0.001 | 0.004 ± 0.002 | |
Micro-F1 | FHMM | 0.691 ± 0.019 | 0.636 ± 0.012 | 0.627 ± 0.021 | 0.624 ± 0.017 |
KNN | 0.724 ± 0.003 | 0.652 ± 0.014 | 0.719 ± 0.016 | 0.698 ± 0.030 | |
CNN | 0.821 ± 0.013 | 0.794 ± 0.017 | 0.814 ± 0.008 | 0.755 ± 0.023 | |
Bi-LSTM | 0.857 ± 0.019 | 0.873 ± 0.040 | 0.854 ± 0.013 | 0.877 ± 0.017 | |
MCTS-CI | 0.964 ± 0.004 | 0.947 ± 0.008 | 0.946 ± 0.011 | 0.935 ± 0.006 | |
Macro-F1 | FHMM | 0.562 ± 0.029 | 0.637 ± 0.010 | 0.587 ± 0.014 | 0.613 ± 0.011 |
KNN | 0.695 ± 0.019 | 0.642 ± 0.013 | 0.693 ± 0.012 | 0.661 ± 0.008 | |
CNN | 0.795 ± 0.015 | 0.788 ± 0.015 | 0.758 ± 0.005 | 0.714 ± 0.003 | |
Bi-LSTM | 0.828 ± 0.012 | 0.854 ± 0.010 | 0.831 ± 0.004 | 0.856 ± 0.012 | |
MCTS-CI | 0.924 ± 0.008 | 0.937 ± 0.012 | 0.912 ± 0.010 | 0.928 ± 0.014 | |
MAE | FHMM | 0.321 ± 0.018 | 0.351 ± 0.015 | 0.371 ± 0.002 | 0.251 ± 0.008 |
KNN | 0.275 ± 0.011 | 0.247 ± 0.012 | 0.284 ± 0.003 | 0.212 ± 0.013 | |
CNN | 0.172 ± 0.009 | 0.201 ± 0.022 | 0.171 ± 0.017 | 0.149 ± 0.008 | |
Bi-LSTM | 0.154 ± 0.007 | 0.132 ± 0.011 | 0.154 ± 0.012 | 0.135 ± 0.014 | |
MCTS-CI | 0.081 ± 0.013 | 0.062 ± 0.008 | 0.091 ± 0.012 | 0.061 ± 0.006 |
Algorithm 1: Detailed algorithm Tree_UCT |
Input: The load state LSi; The root node RN_s0; The tree node RN_s; The reward re_v; Time threshold TN; Number of visited node Ns; The estimated value Qs. |
Output: Tree_UCT |
1 //Create Tree_UCT sequence LSi and create the root node. |
2 TreeUCTsequence[LS_i]=0; |
3 RN_s0=LS0; |
4 //Within the calculated time limit. |
5 for tn=1 to TN do |
![]() |
Algorithm 2: MCTS-CI Algorithm |
Input: Load power series [P1,P2,…,P(t−1)]; Preprocessed data series [P′1,P′2,…,P′(t−1)]; |
Time threshold Tleft; Create the root node RS_node0 of the state Ls0; Initial state Ls0 |
1. Output: The RL-MCTS model RM. |
2 //Training RL-MCTS model. |
3 //Construct MCTSsearch Ls0. |
4 if: t≤Tleft then |
![]() |
Load type | No. 1 house | No. 2 house | No. 3 house | No. 4 house | No. 5 house | No. 6 house |
REF | 107 | 106 | 107 | 107 | 107 | 107 |
MWO | 106 | 106 | 107 | 105 | 107 | 106 |
IC | 105 | 105 | 107 | 105 | 105 | 105 |
WH | 107 | 107 | 107 | 106 | 107 | 106 |
Methods | Datasets | |||
No. 1 house | No. 2 house | No. 3 house | No. 4 house | |
FHMM | 67.13 ± 0.36 | 69.83 ± 1.71 | 54.56 ± 0.98 | 63.68 ± 4.32 |
KNN | 70.34 ± 1.23 | 73.56 ± 0.89 | 62.78 ± 0.65 | 74.69 ± 2.31 |
CNN | 78.24 ± 1.12 | 75.43 ± 0.62 | 77.23 ± 4.12 | 80.68 ± 0.89 |
Bi-LSTM | 87.78 ± 4.13 | 79.56 ± 2.92 | 85.57 ± 2.54 | 85.97 ± 5.18 |
MCTS-CI | 95.56 ± 0.19 | 89.91 ± 0.47 | 90.95 ± 0.42 | 92.22 ± 0.34 |
Criteria | Methods | Datasets | |||
No. 1 house | No. 2 house | No. 3 house | No. 4 house | ||
H−loss | FHMM | 0.021 ± 0.002 | 0.019 ± 0.001 | 0.017 ± 0.002 | 0.020 ± 0.001 |
KNN | 0.018 ± 0.001 | 0.016 ± 0.002 | 0.014 ± 0.001 | 0.015 ± 0.004 | |
CNN | 0.014 ± 0.003 | 0.012 ± 0.004 | 0.011 ± 0.003 | 0.013 ± 0.002 | |
Bi-LSTM | 0.009 ± 0.002 | 0.008 ± 0.001 | 0.006 ± 0.001 | 0.009 ± 0.003 | |
MCTS-CI | 0.003 ± 0.001 | 0.003 ± 0.001 | 0.002 ± 0.001 | 0.004 ± 0.002 | |
Micro-F1 | FHMM | 0.691 ± 0.019 | 0.636 ± 0.012 | 0.627 ± 0.021 | 0.624 ± 0.017 |
KNN | 0.724 ± 0.003 | 0.652 ± 0.014 | 0.719 ± 0.016 | 0.698 ± 0.030 | |
CNN | 0.821 ± 0.013 | 0.794 ± 0.017 | 0.814 ± 0.008 | 0.755 ± 0.023 | |
Bi-LSTM | 0.857 ± 0.019 | 0.873 ± 0.040 | 0.854 ± 0.013 | 0.877 ± 0.017 | |
MCTS-CI | 0.964 ± 0.004 | 0.947 ± 0.008 | 0.946 ± 0.011 | 0.935 ± 0.006 | |
Macro-F1 | FHMM | 0.562 ± 0.029 | 0.637 ± 0.010 | 0.587 ± 0.014 | 0.613 ± 0.011 |
KNN | 0.695 ± 0.019 | 0.642 ± 0.013 | 0.693 ± 0.012 | 0.661 ± 0.008 | |
CNN | 0.795 ± 0.015 | 0.788 ± 0.015 | 0.758 ± 0.005 | 0.714 ± 0.003 | |
Bi-LSTM | 0.828 ± 0.012 | 0.854 ± 0.010 | 0.831 ± 0.004 | 0.856 ± 0.012 | |
MCTS-CI | 0.924 ± 0.008 | 0.937 ± 0.012 | 0.912 ± 0.010 | 0.928 ± 0.014 | |
MAE | FHMM | 0.321 ± 0.018 | 0.351 ± 0.015 | 0.371 ± 0.002 | 0.251 ± 0.008 |
KNN | 0.275 ± 0.011 | 0.247 ± 0.012 | 0.284 ± 0.003 | 0.212 ± 0.013 | |
CNN | 0.172 ± 0.009 | 0.201 ± 0.022 | 0.171 ± 0.017 | 0.149 ± 0.008 | |
Bi-LSTM | 0.154 ± 0.007 | 0.132 ± 0.011 | 0.154 ± 0.012 | 0.135 ± 0.014 | |
MCTS-CI | 0.081 ± 0.013 | 0.062 ± 0.008 | 0.091 ± 0.012 | 0.061 ± 0.006 |