Patients with focal drug-resistant epilepsy are potential candidates for surgery. Stereo-electroencephalograph (SEEG) is often considered as the “gold standard” to identify the epileptogenic zone (EZ) that accounts for the onset and propagation of epileptiform discharges. However, visual analysis of SEEG still prevails in clinical practice. In addition, epilepsy is increasingly understood to be the result of network disorder, but the specific organization of the epileptic network is still unclear. Therefore, it is necessary to quantitatively localize the EZ and investigate the nature of epileptogenic networks. In this study, intracranial recordings from 10 patients were analyzed through adaptive directed transfer function, and the out-degree of effective network was selected as the principal indicator to localize the epileptogenic area. Furthermore, a coupled neuronal population model was used to qualitatively simulate electrical activity in the brain. By removing individual populations, virtual surgery adjusting the network organization could be performed. Results suggested that the accuracy and detection rate of the EZ localization were 82.86 and 85.29%, respectively. In addition, the same stage shared a relatively stable connectivity pattern, while the patterns changed with transition to different processes. Meanwhile, eight cases of simulations indicated that networks in the ictal stage were more likely to generate rhythmic spikes. This indicated the existence of epileptogenic networks, which could enhance local excitability and facilitate synchronization. The removal of the EZ could correct these pathological networks and reduce the amount of spikes by at least 75%. This might be one reason why accurate resection could reduce or even suppress seizures. This study provides novel insights into epilepsy and surgical treatments from the network perspective. Focal epilepsy is characterized by paroxysmal abnormal discharge within local brain tissue, approximately 30% of which are medically refractory , 2. DrugPatients with refractory focal epilepsy require long-term electroencephalograph, functional imaging  and neuropsychological testing during the preoperative period . When daIn previous studies, ADTF was widely used to analyze the ictal discharge or the interictal spike data, while little attention was given to the interictal segments without spikes. Wilke et al. applied the interictal spike recordings to investigate the origin of abnormal discharge . Mierlo In addition, the connectivity pattern in the ictal process may qualitatively differ from others. After all, it is closely associated with seizures. To verify whether networks in the ictal stage are more likely to generate spikes, computational models are adopted. Previous studies were committed to building a bi-stable system, where fixed point represented resting state and limit cycle described the seizure-like dynamics , 17. HowIn this study, we apply the ADTF technique to SEEG recordings from 10 patients with focal drug-resistant epilepsy. Dynamic effective connectivity is constructed and out-degree is selected to identify the epileptogenic focus. The calculated results are compared with the clinical conclusions. Furthermore, we establish coupled neuronal population model to simulate electrical activity in various conditions . The outcomes of the virtual resection are then demonstrated, and the relationships between the EZ and pathological network are discussed.The SEEG dataset was obtained from 10 patients  at Sanbo Brain Hospital of Capital Medical University in Beijing. All were selected based on the following criteria: refractory focal epilepsy and neurosurgical resection rendering the patient seizure-free during at least 2 years. This study was carried out in accordance with the recommendations of the Ethics Committee of the Sanbo Brain Hospital of Capital Medical University with written informed consent from all subjects. The basic characteristics of these recruited patients are described in Table The locations of electrodes were decided on the findings of non-invasive investigations, and the implanting operation aided by an advanced robot ROSA was completed accurately. Each electrode had multiple contacts, each of which was regarded as a channel. In addition, the average monitoring period was 1 week, and the sampling frequency was set to 512 Hz.For each patient, we randomly selected two episodes (patient No. 8 had only one) containing epileptic seizures. These episodes were taken from 60 s before the pre-ictal stage to 30–60 s after seizure onset. It is worth mentioning that the pre-ictal period (IP) was divided due to the presence of high-frequency oscillations, also referred to as rapid discharges . If patiX(n) = T is a vector indicating the recordings of K channels at time in, A(n) is the K × K coefficient matrix for delay i relative to time n, E(n) is the prediction error assumed to be white noise, and p is the model order which can be estimated by Akaike information criteria  are unable to solve it unless time windows are adopted, which may spoil the data. Kalman filtering algorithm based on the state space model is entirely different and can estimate the coefficient matrix at any time. The details of algorithm implementation were described in Arnold et al. . An examn, the p matrices jointly determine the causality relationships between different channels, and ADTF is then developed to integrate these into the frequency domain  is the Fourier transformation of the coefficient matrix, and ijH  is the element of H , the inverse matrix of A .At time y domain (2)A, it may be more convenient to investigate the evolution with time if the information is integrated in a specific frequency band. Meanwhile, the ADTF is generally normalized with respect to the incoming flow, which leads to the spectrum-weighted ADTF (swADTF)  (4)swADTThe swADTF is calculated in the frequency band  to exclude the interference of background electrical activity and include the effect of low gamma rhythm simultaneously. Notably, the values of swADTF have a slight fluctuation even for stationary signals. Therefore, it is necessary and reasonable to take the average value every 0.5 s.K non-diagonal elements of the causality matrix are selected to determine the dynamic threshold th (n). The connection matrix L is defined as follows:ijL(n) = 1 indicates that there exists an edge from node j pointing to node i. The process of constructing dynamic effective connectivity is described in the left branch of Figure Effective connectivity is defined as the influence that one node exerts over another, In contrast to functional connectivity, it can describe the directionality of interactions and the path of information flow. The abnormal activity of the epileptogenic area can stimulate the connected neurons and cause sporadic or periodic spikes. The EZ often maintains robust causality relationships with neuronal populations. Therefore, the top The out-degree is the number of outgoing edges from one node. A large out-degree means that this node dominated the network at that time. Its role is extremely similar to the impact of the EZ on other neural ensembles in the production of synchronous discharges. Therefore, the out-degree is selected as the principal indicator of the epileptic area.fast circuit activity by GABAslow inhibitory postsynaptic currents  and the somatic-projecting inhibitory interneurons . Excitatory and inhibitory connections between subsets are depicted in Figure In 2002, Wendling et al. proposed an exquisite single neuronal population model on the basis of previous research results , 27, 28.currents . This moNeuronal populations are connected and cooperate to achieve specific functions. To account for this organization, a coupled neuronal population model is established Figure , and theK differential equations that describe the state of every population can be established:According to the block diagram, the following set of 12 × The parameters’ interpretation and standard values are listed in Table u is selected for removal, the coupling matrix W(n) can be changed into W′(n)It is also possible to perform virtual surgery and observe outcomes by removing the specified population from the model. For instance, if population Thus, it is easy to investigate the outcomes of different operation schemes, including the removal of the EZ based on theoretical calculation.The comparison between the out-degree calculation results and clinical conclusions is shown in Table The accuracy is the proportion of true positives to all calculated results. The detection rate measures corresponding percentage in clinical conclusions. They describe the influence of false positives and false negatives, respectively. According to Table Particularly, the out-degree of each channel with time is depicted for patient No. 2 in Figure It is shown that E04, the fourth contact of electrode E, governs the whole networks in both the interictal stage and the pre-IP. Similarly, J14 and M08 dominate activities in the ictal stage. The SEEG report verifies that intermittent spikes can be observed in E04 during the interictal period (IIP). Electrophysiologists deem that the abnormal discharges spread from E04 to electrode J and M. The calculated results are included within the resection area Figures C,D. FineSpikes are one of the most common biomarkers for indicating abnormal brain activity. In the simulation process, sporadic spikes are seen in the interictal stage and rhythmic spikes are observed during the IP Figure B, which However, two cases of simulations are not in accordance with the real situation. In Figure This study developed a framework to analyze long-term SEEG data and localize the epileptogenic area. The electrode contacts with the highest total out-degree corresponded well with epileptogenic region defined by electrophysiologists. The same stage shared a relatively stable connectivity pattern, while the patterns varied from stages. Coupled neuronal population model was established to investigate the presence of epileptogenic patterns. Simulations indicated that most networks during the IP were epileptogenic, and the nature of epileptogenic network was to enhance local excitability and facilitate synchronization. The fact that removing the EZ could significantly reduce the amount of spikes suggested that the EZ should be responsible for the formation of these pathological networks.Epilepsy is regarded as a network-level phenomenon . This tyIt is increasingly understood that seizures arise from epileptogenic networks, through which epileptiform discharges are produced and propagated . HoweverWe have known that the channel with the maximum out-degree varies from stages. However, the occurrence of seizures is not only due to the transfer of important nodes. Qualitative change of connectivity patterns must follow. Coupled neuronal population model was used to simulate the time course in different patterns. An increased number of spikes indicate that this connectivity pattern is more likely to generate ictal activity. The nature of epileptogenic network should be a specific organization which likely enhances local excitability and facilitates synchronization. Because the epileptogenic region affects other neural ensembles and enables them to synchronize, it should be responsible for the formation of these pathological networks. Virtual surgery also suggested that the removal of the EZ could significantly reduce the amount of spikes. Sinha et al. also concluded that the resection of these regions could reduce the overall likelihood of seizures after conducting simulations with a bi-stable model . Hence, The distribution of the out-degree could aid identification of the EZ and deepen our understanding of the difference between connectivity patterns. However, it failed to describe the qualitative changes of network organization. Coupled neuronal population model was used to demonstrate the network behavior and judge whether the network was epileptogenic or not. It was also convenient to observe the outcomes of different operation schemes. There are two reasons to carry out virtual surgery. First, contradiction may appear when the EZ overlaps with the brain function area. Hypothetical surgery provides new options for controlling seizures with regard to preserving major function. Second, virtual surgery is beneficial in terms of balancing the resection range and therapeutic effect , 39. It This study consists of the EZ localization and coupled neuronal population model. The former aims at identifying the electrode contacts triggering the abnormal discharge. However, seizures are often associated with abnormal changes in brain synchronization mechanisms. This model is used to find epileptogenic connectivity patterns and evaluate the role of the EZ on pathological network. The results suggested that the presence of the EZ made the network more easily synchronized. Accordingly, if the EZ is removed, pathological network can be corrected and seizures are reduced or suppressed. It is helpful to understand the relationship between the EZ and epileptogenic networks.It was shown that channels with the maximum out-degree corresponded to the epileptogenic area after applying the ADTF technique to SEEG recordings from 10 patients. All of these recordings from the interictal, pre-ictal and ictal stages were valuable for the EZ localization. Interestingly, we found that the same stage shared a relatively stable connectivity pattern, while the pattern evolved at different stages. In addition, some of these patterns were verified to be epileptogenic. Model simulations confirmed this and suggested that these pathological networks could be corrected if the EZ was removed. It indicated that the nature of the epileptogenic network should be a specific organization enhancing local excitability and facilitating synchronization.This study was carried out in accordance with the recommendations of the Ethics Committee of the Sanbo Brain Hospital of Capital Medical University with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Ethics Committee of the Sanbo Hospital of Capital Medical University.CY, GL, QW, ZL, FZ, and QYW collected and analyzed these clinical recordings to localize the epileptogenic zone and wrote the paper. CY and QYW built mathematical model and carried out a series of simulations.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks.In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs  during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions ( The current growth in Internet information and computational hardware development, such as Facebook and other well-known business websites, is enabling a wide range of researchers to utilize various advanced techniques, such as machine learning tools, to capture available and important information and analyze this processed information to provide better business decisions for the government and industry. A critical challenge in the development of advanced techniques and application of them to process more complicated problems is how to select more appropriate models that fit the specific data collected from real-world applications well. Although machine learning approaches has been successfully applied in a wide range of pattern recognition and prediction tasks, limitations of these techniques in processing large-scale and high-dimensional data still exist. Typically, in most pattern recognition tasks, the data is usually collected from a group of digital images or action videos, and each image of these data contains a high-dimensional pixel vector. Convolution machine learning approaches, such as support vector machine (SVM), have difficulty processing such data due to their shallow structures, which fail to capture the high-level abstract feature representation from the high-dimensional row data. The above difficulties and challenges have inspired most research groups and companies to find and develop more advanced techniques to address such issues. Deep learning techniques are among the most popular and representative methods of these advanced techniques. In recent years, deep learning techniques have been widely developed and applied in many real-world applications due to their excellent performance on large-scale and high-dimensional datasets. Deep learning techniques have been successfully applied in domains such as human action recognition –8, text Conventional machine learning algorithms usually have some limitations in their ability to address natural data due to the complex structure of their raw form. For decades, constructing a classification model or pattern recognition system usually required careful investigation and considerable background information regarding the optimization problem to design a feature representation tool that transformed the input data  into another data representation that is suitable for classifiers to process it and discover the classification pattern between the inputs and outputs. Feature reduction and feature learning are always important issues in the machine learning domain and are usually employed in combination with other machine learning algorithms, such as support vector machine, to provide a significant improvement in performance on recognition tasks compared to the standard machine learning algorithms. In addition, deep learning techniques can be seen as specific feature learning or feature reduction tools with multiple processing layers that abstract the high-level feature representation. A deep learning architecture usually contains one input layer, two or more processing layers, and one output layer. The degree of feature abstraction of the deep learning architecture is dependent on the depth of its neural network and the number of hidden neurons. However, although a more high-level network structure employed to construct a deep learning architecture may provide more powerful capabilities in feature representation, this is not suitable for only a few samples with low-dimensional attributes, and the choice of depth of neural networks is a trade-off between generalization capability and computational complexity.In many forms of machine learning, shallow or deep, supervised machine learning is the most popular and frequently used method to construct a classification model or pattern recognition system. The main idea of supervised learning is to update the adjustable network parameters, i.e., weights and biases, by receiving a huge number of real-world samples, such as a car, a pet, a house or a person. To train a network using the supervised learning method, the first step is to collect a large volume of data from appropriate real-world domains, and each sample (instance) must be labeled with its category. During the training phase, these collected data are employed as input data for the classifier and then transformed into outputs through the many non-linear modules that connect the multiple processing layers of a deep network. Generally, we want an instance or a sample that is fed into a classifier to yield the desired output. However, this usually does not occur before training. Training a network requires modifying its adjustable parameters (weights and biases) so that the error between the predicted outputs and the desired outputs is as small as possible. The objective function, which is also called the loss function and is used to measure the training error, usually adopts the square loss measure for regression problems and the log-likelihood loss measure for classification problems. In a typical higher-level neural network using the supervised learning approach, there may be many processing layers with hundreds of millions of these tuning parameters (weights and biases), which are trained with hundreds of millions of samples with genuine labels.Unsupervised learning is one type of machine learning method and can employ the “Deep Learn” concept to construct a higher-level neural network with more powerful capability for feature representation and abstraction. In many unsupervised learning methods, a deep belief network (DBN) is a more representative approach among these unsupervised learning. A DBN usually consists of many processing layers, and each processing layer contains multiple parameterized non-linear modules. DNB has a range of advantageous properties that can allow it to efficiently process large-scale and high-dimensional datasets. The main goal of a DBN is to learn a higher-level feature abstract representation that provides good classification performance. Actually, after network training is complete, the DBN eventually derives a set of vectors of parameters  that are employed as initialization values of the network parameters of the multilayer perceptron to train a final model. These optimal parameters determined by the DBN, employed to initialize a deeper network, may result in a training network via which the model has more opportunities for finding the global optimal solution and avoiding the case in which the algorithm procedure becomes trapped in a local minimum. In addition to the original DBN algorithm, some improved algorithms have been developed –38. The nd the number of hidden neurons. In addition, weight initialization is always given by a randomly generated real-number vector that is small enough around the zero to yield the largest gradients during the early training phase. The learning rate is typically a hot topic and has been discussed in the machine learning community. The main reason is because it plays a more important role in network training compared to other hyperparameters. To find a more appropriate learning rate, there is a simple solution for choosing a fixed learning rate, that is, we can simply use a grid search approach in combination with a network training procedure using steepest gradient descent algorithms to determine an optimal learning rate from several candidate log-spaced values , based on the training error. In addition, a classical MLP classifier using the traditional steepest gradient descent algorithms, such as a back-propagation algorithm for training a deep learning architecture, has several limitations, i.e., the algorithm cannot find the global optimum solutions and its search procedure is easily trapped in local optima. Therefore, to solve these difficulties in using steepest gradient algorithms to train a higher-level neural network, the Stochastic Gradient Descent (SGD) algorithm has recently been proposed . W denotes an inertia weight that was first proposed by Shi and Eberhart , [0.0001 0.0001], [0.1 0.9], and [0.1 0.9], respectively. Due to only two processing layers being employed to construct the deep learning architecture in the first experiment, the search ranges of the numbers of neurons of the two processing layers are set to the same range: . During the network training phase, the epochs for population evaluation of the PSO algorithm and training of the final model are set to a small number and a large number of epochs (5 and 20), respectively. A batch size of 100 is set for network training using mini-batch learning.In this section, we evaluate the classification performance and generalization ability of the proposed approach in solving the recognition of handwritten characters. In this experiment, two different methods were used to construct the final classification models. One approach is called “DNN-NONPSO” and randomly generates a group of network configurations (network structures and hyperparameter configurations) from the domains of the parameters and uses them to directly train a set of final deep learning architectures with their corresponding hyperparameter configurations on all of the training samples. The second approach is called “DNN-PSO” and first implements an optimization scheme in which the network configuration is decoded into a real-number vector and employed as a particle  of the PSO algorithm so that the algorithm search procedure can efficiently process these optimization parameters. In addition, after once updating the particle swarm information, a particle  of the PSO algorithm is converted into a network configuration and employed to initialize a deep learning architecture and train the network with a few steps of mini-batch learning to obtain the final training loss value as the score of the individual. Finally, the determined optimum solutions are employed to construct the final models. The detailed parameter configurations for the PSO algorithm and optimization scheme are presented as follows. The population size is set to 20, and the maximum number of iterations of PSO is set to 30. The acceleration coefficients To evaluate the performance of the proposed approach on the MNIST dataset, the k-fold cross-validation technique is used. The k-fold cross-validation technique is the most popular and frequently used method and is mainly employed to evaluate the performance of different algorithms in an unbiased manner. The main purpose of using k-fold cross-validation in this experiment is that the k-fold cross-validation technique has been widely applied in most studies and hence provides a standard benchmark for evaluating our approach. Additionally, it can also ensure that the experimental results that are not affected by other factors. The main idea of k-fold cross-validation is to randomly split the original dataset into k independent subsets. These k subsets share the same information with all categories and contain the same number of instances. The entire procedure of k-fold cross-validation requires k independent algorithm runs, and in each k-fold cross-validation run, one of the k subsets is selected as a test set for classifier evaluation, and the remaining k-1 independent subsets are employed as a training set to model a classifier. During all k runs of k-fold cross-validation, each subset has a chance to be selected as the validation set, with the remaining k-1 subsets being employed as the training set. After all k-fold cross-validation runs, the achieved results are averaged. In the handwritten recognition experiment, 5-fold cross-validation is adopted to randomly split the MNIST dataset, which originally consists of 6000 training samples and 10000 testing samples, into 5 independent subsets such that each subset is comprised of 14000 samples and shares information with ten categories. In each 5-fold cross-validation run, one of the 5 independent subsets is selected as the test set, and the remaining 4 independent subsets are employed as the training set. After all 5-fold cross-validation runs, we can obtain the average and standard deviation of the achieved results from the five instances of 5-fold cross-validation.pbest) and solution (gbest) of the PSO algorithm have achieved the best classification accuracies of 0.9849 and 0.9795, respectively. The above achieved results show that the ensemble model can provide better generalization capability than the individual DNN classifier. In addition, although the individual DNN classifier is constructed by the best solution (gbest) of the PSO algorithm, the classification accuracy achieved by the ensemble model combining 20 individual DNN classifiers is still superior to the best individual classifier. The main reason may be because combining evidence across multiple DNN classifiers of the ensemble model may result in the construction of a classification model in which some poorly performing DNN classifiers would be ignored by most of the well-performing DNN classifiers during the prediction phase. The more specific case can be seen in the ensemble without using the PSO-based optimization scheme. n trained DNN classifiers may derive a different ensemble model with different generalization capabilities when varying n from 2 to n. In addition, the prediction accuracy of the ensemble model is increased when the number of members (trained DNN classifiers) is increasing. The above results show that, the more DNN classifiers that are selected from the trained classifiers, employed to construct an ensemble model may provide better generalization capability than a few DNN classifiers combined. However, the table presented results that also show that the prediction accuracy of the ensemble model with a certain number of DNN classifiers has almost achieved the highest accuracy or has provided only a slight improvement when the number of DNN classifiers is increased. The computational times for training and testing of an ensemble model are considerable when choosing more DNN classifiers.gbest) of the PSO algorithm and then trained with the solution (gbest) corresponding to the hyperparameter configuration can produce a decreasing curve during the 20 epochs. In addition, the training loss values generated by the individual classifier initialized and trained by the solution (gbest) of the PSO algorithm not only achieved the lowest loss value at each epoch compared with the random solution but also obtained the lowest loss value on the latest training phase. The observed results also indicate that a more appropriate network structure with a better hyperparameter configuration can improve the performance in the network training phase and of the final achieved model, and the PSO algorithm in combination with the steepest gradient descent algorithm can utilize their global and local exploration capabilities to automatically discover the optimal network configuration without any prior knowledge.In this subsection, we evaluate the prediction performance of the proposed approach in solving the regression problem. To solve the regression problem, the prediction of biological activity is an important issue in computational biology fields and helps researchers to improve their work on drug discovery. In this experiment, KCD datasets are suitable for evaluating performance of various algorithms and investigating the effectiveness of the proposed approaches on predicting biological activity. Before performing any training procedures on KCD datasets, we need to preprocess the data. The details of the above process are illustrated as follows. The high-dimensional descriptors (features) that exist in each sample are difficult for training of a deep learning architecture, and hence this study uses the Principal Component Analysis (PCA) technique to reduce the number of descriptors to an appropriate amount so that the deep learning architecture can efficiently process them. All 15 targets of the KDD datasets were processed with PCA for feature reduction. After the above process, the remaining descriptors of 15 targets of the KCD datasets are scaled into the range of . For this regression experiment, all parameter domains were set as follows. The number of processing layers was two, and each processing layer’s neurons were initialized by a randomly generated number in the range of . The sigmoid function was employed to compute the activation value of the neuron. The number of epochs for population evaluation  was set to 30 and for final network training was set to 100. The batch size was set to 100. The domains of the learning rate, momentum, decay, dropout rate were set to the ranges , , , and , respectively. For evaluating the performance of the proposed approach on each biological dataset, a distribution ratio is used to randomly divide all of the samples of each dataset into two subsets, where 80% of the samples were employed to train a final network and the remaining samples were employed as a testing set for evaluation.pbest) of the PSO algorithm and random solutions for 15 datasets is presented in Tables The performance of the 20 independent DNN classifiers generated by the solutions (pbest) and (gbest) of the PSO algorithm and random approach, respectively, for 15 KCD datasets. The last two rows of the table present the average and standard deviation of accuracies generated by the two different approaches on 15 KCD datasets, respectively. We can observe that the ensemble model and the individual DN classifiers constructed by the PSO-based optimization scheme not only achieved better average prediction results but also yielded the lowest standard deviation of accuracy. In addition, using the solutions of the PSO algorithm to initialize the ensemble model and individual DNN classifier and then train these neural networks with their corresponding hyperparameter configurations requires less computational time to implement the final models compared to the random approach. Based on the above results, it is shown that the optimal network configurations can simultaneously improve the classification performance and training efficiency compared with randomly selected configurations.gbest) of the PSO algorithm and random configuration for 15 KCD datasets. As shown in these graphs, gbest is employed to initialize a DNN classifier and then train the classifier with its corresponding hyperparameter configuration, which generates lower training curves than the random configuration. In addition, not only did the individual classifier using the DNN-PSO approach generate the lowest training curves, but it also obtained the lowest final training error in the last training phase.Figs pbest) of the PSO algorithm and random approach, respectively, for five runs of 5-fold cross-validation on the MNIST dataset. The same phenomenon can be seen in this table: use of the optimal network configurations that were expressed by solutions of the PSO algorithm to construct DNN classifiers outperforms the classifiers trained using randomly generated configurations. The last two rows of the table show the average and standard deviation of these results within the table. To compare the classification accuracies generated by DNN classifiers with three and two hidden layers, the DNN classifiers perform better when using two hidden layers. This may be because a deeper-level neural network generally has a huge number of adjustable parameters (weights and biases), requiring more training time or computational resources to allow the model to fit the training data. In. The same training parameters (epochs and batch size) that have been employed in the training network with two hidden layers in the previous experiments are now used to train the network with three hidden layers. As a result, this training phase may be insufficient, and more epochs for network training may allow the deeper neural network to reach or exceed the classification performance of the shallow architecture.In this subsection, we investigate the influence of the use of the PSO-based optimization scheme for deeper neural networks and evaluate the effectiveness of these trained DNN classifiers on the MNIST dataset. For the experimental parameter setting, the search ranges for the optimization parameters, and the parameter configurations for the PSO algorithm and final network training adopt the same settings as in the previous experiment. In addition, we adopted three hidden layers to construct a deep neural network, and the number of hidden neurons of each hidden layer was searched for in the same domain. The five runs of the 5-fold cross-validation procedure with mini-batch learning are performed to train DNN classifiers and evaluate them. After five runs, we can obtain the final results by calculating the average and standard deviation of these achieved results. pbest) of the PSO algorithm provide significant improvements compared with the random approach, at each fold of the 5-fold cross-validation runs. The proposed approach provides a flexible manner by which the number of classifiers can be determined. In addition, constructing an ensemble model with a certain number of classifiers is directly choosing the optimal classifiers with better scores (low training errors) from all the trained classifiers. In this manner, the generalization capability can be maximized, while the computational time of the prediction phase can be reduced. Therefore, the choice of number of classifiers is a trade-off between performance and complexity. In the next subsection, we investigate the influence of the choice of number of classifiers and evaluate these ensemble models in terms of classification performance and computational time for the prediction phase.pbest of the PSO algorithm and the randomly generated solutions on the MNIST dataset, without training any new neural networks. When constructing an ensemble model, the choice of number of classifiers is an important issue that influences the generalization performance. How to choose a more appropriate number of classifiers is a challenging task because more classifiers may yield better generalization performance but also require more computational resources in network training and prediction. This subsection investigates the influence of the ensemble models that were constructed with different numbers of classifiers using the DNN-PSO and DNN-NONPSO approaches. In this experiment, all parameter configurations are set the same as before expect for the population size  of the PSO algorithm. In addition, the various ensemble models were generated by directly choosing the classifiers from the candidate classifiers trained by In this paper, a new automatic hyperparameter selection approach is proposed to find a more appropriate network structure and hyperparameter configuration for deep neural network training. The main ideal of the proposed approach is utilizing the advantages of global and local exploration capabilities from particle swarm optimization (PSO) and the steepest gradient descent algorithm and combining them into a hybrid search procedure. Because the performance of deep network classifiers extremely depends on their network structure and hyperparameter configurations, we aim to optimize these configurations through an efficient parameter optimization scheme using PSO in combination with the steepest gradient descent algorithm. After the procedure of the parameter optimization scheme, the final solutions (pbest) and (gbest) of the PSO algorithm having important network configuration information are used to initialize and train a final ensemble model and individual DNN classifier, respectively. In addition, the proposed approach also provides a flexible method that allows users to choose a certain threshold as the number of classifiers to construct a light ensemble model. In this manner, the trade-off between the generalization capability and the model complexity can be addressed, and this is discussed in the previous subsections in which several experiments have been performed with the ensemble models with different numbers of classifiers. We have constructed experimental studies to solve classification and regression problems by evaluating the performance of the proposed approach on the handwritten characters and biological activity prediction datasets, respectively. The experimental results demonstrated that the proposed approach can find a more appropriate network structure with a better hyperparameter configuration, which are then employed to initialize and train DNN classifiers, which achieve excellent performance in both the training phase and final models (after training). Therefore, the proposed approach can be regarded as an automatic hyperparameter optimization tool for deep learning architectures without requiring any prior knowledge.In our future work, we would like to extend our approach to optimize more deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), since the flexibility of the proposed approach in initializing and training a neural network means that the above implementation would require only a small modification. In addition, we would like to use other advanced evolutionary algorithms or develop a new algorithm to replace PSO in the proposed approach for network configuration optimization since it provides a flexible optimization scheme in which the algorithm is performed in a wrapped manner, and any population-based optimization algorithm can be employed.


Lophorina superba, is one of the most remarkable of all. Recent research by Lophorina is not a single species but is likely a complex of three allopatric species spanning the island of New Guinea: L. niedda in the Bird’s Head Peninsula of the west, L. superba throughout the central cordillera and L. minor in the Papuan Peninsula of the east. Of these, niedda is the most phenotypically divergent with plumage traits hypothesized to possibly produce differences in ornamental appearance during display. However, the whole courtship phenotype of niedda has not been documented and so the actual extent of differences in ornamental appearance during courtship remain unknown. Here we analyze the first audiovisual recordings of niedda and compare its courtship phenotype with superba to test the hypothesis of potential differences in ornamental appearance. Our main goals are to: (1) provide the first description of the courtship phenotype of niedda in the wild, (2) determine if and how the niedda courtship phenotype differs from superba and (3) evaluate any uncovered differences in light of niedda’s newly recognized species status. Our secondary goal is to provide a more thorough characterization of courtship phenotype diversity within the genus Lophorina to facilitate future comparative study within the genus and family. Results show that the niedda courtship phenotype differs substantially from superba in numerous aspects of ornamental appearance, display behavior and sound. We highlight six key differences and conclude that the new species status of niedda is corroborated by the distinctly differentiated ornamental features documented here. With full species status, niedda becomes the fourth endemic bird-of-paradise to the Bird’s Head region of Indonesian New Guinea , a fact that underscores the importance of this region as a center of endemic biodiversity worthy of enhanced conservation protection.The birds-of-paradise (Aves: Paradisaeidae) are a quintessential example of elaborate ornamental diversification among animals. Ornamental evolution in the birds-of-paradise is exemplified by the presence of a highly integrated courtship phenotype, which is the whole package of plumage ornaments, behaviors and sounds that each species uses during courtship. Characterizing a species’ courtship phenotype is therefore a key part of evolutionary and taxonomic investigation in the group. With its unprecedented transmogrification from bird-like form into something abstract and otherworldly, the courtship phenotype of the Superb Bird-of-Paradise,  A key ihenotype . A specihenotype . Characthenotype .Lophorina superba  Peninsula in the west, through all of the central cordillera and to the Papuan Peninsula in the east with isolated populations in the Adelbert Range and Huon Peninsula in the northeast  in the Bird’s Head (Vogelkop) Peninsula, L. superba  throughout the central cordillera, Adelbert Range and Huon Peninsula, and L. minor in the Papuan Peninsula  to provide the first description of the courtship phenotype of niedda in the wild, (2) test for potential differences in ornamental appearance by determining if and how the niedda courtship phenotype differs from superba and (3) evaluate any uncovered differences in light of niedda’s recently recognized species status. An additional goal is to provide a more thorough characterization of courtship phenotype diversity within the genus Lophorina to facilitate future comparative study within the genus and among species in the family.Here we analyze the first audiovisual recordings of courtship behavior in L. niedda were made near the village of Mingre in the Arfak Mountains of West Papua, Indonesia during June-November 2016 and 2017. Multiple simultaneous recordings were made with remotely and manually operated video cameras and audio recorders from within observation blinds. Video was recorded with RED Epic-W Helium and Canon 1DX Mark II cameras fitted with a range of Canon lenses. Audio was recorded using a Marantz PMD 661 and Sennheiser microphones. Focal length, frame size and frame rates of video recordings varied according to the particular camera-lens combination used and particular environmental conditions, but ranged between 50–800 mm, 4 k–8 k and 24–240 p respectively.Field observations and audiovisual recordings of two adult male http://www.macaulaylibrary.org) as accession number ACC4396. Vouchers are cited in the Results using Macaulay Library catalog numbers and can be viewed and/or requested online by searching the Macaulay Library database for catalog numbers ML487530 to ML487569 .For the acoustic analysis, archival quality audio files from the Macaulay Library  and one individual adult male superba latipennis (ML458001 –ML458003). All of the recordings with data used in the analysis are cited in the Results and can be viewed and/or requested online via the Macaulay Library database as described above.Video used for the analysis of  Library  of two isuperba, we also reviewed broadcast video footage obtained by film crews during the production of three documentary films: (1) Planet Earth (2006) produced and distributed by the BBC, (2) Nature: Birds of the Gods (2011) distributed by PBS, and (3) Designed to Dance: Birds of Paradise (2011) produced by NHK Wildlife. Through communications with the producers and/or with local informants at the filming locations, we know that all three the documentary sequences were filmed near the town of Tari in Hela Province, Papua New Guinea and therefore all represent the subspecies L. superba addenda. Clips containing the segments we reviewed and/or full episodes for each film can be viewed online through a variety of sources . Due to copyright restrictions, we could not include comparable images of addenda in our figures, but unless otherwise noted, the courtship phenotype of addenda was found to be identical to those of superba and latipennis documented here.To broaden our review of potential interspecific variation within niedda, which broadly correspond with those of superba. In the order in which they occur within the courtship sequence, they are: advertisement, horizontal and pointing displays. Each display is described in detail below. Our analysis did not identify substantial variation in the behaviors, postures and ornaments within the superba complex  and so we use latipennis as the frame of reference to describe and illustrate the superba complex (but see superba superba). Although the three types of initial display activity broadly correspond with those of superba, our analysis found that each display of niedda has numerous distinctive elements.Our analysis identified three distinct types of initial display activity in superbaML458000 from 0:44 to end; nieddaML487533). Upon arrival to the fallen log used for display, the male stands in an alert upright posture with head up, bill forward and tail down  while repeatedly flicking his cape forward in front of his head and calling  and ML458001 (from 0:27–0:34); nieddaML487558 from 0:00–0:30). The tail is also lifted so that the whole body forms a horizontal line from tip of the bill to tip of the tail  feathers of the breast shield stick out conspicuously to the sides and the central most breast feathers are sleeked against the body so that no color is visible, especially when viewed from above . The hops use both feet at the same time and are typically initiated with a quick wing-flick just prior to the first hop. Although less frequent than in advertisement display, the false wing-preens that cause frontal cape flicking are also given.The main movement of the horizontal display is ritualized hopping “run” along the log to change position and orientation . In superba, the body is held in an upright posture, with the upper body lifted from the horizontal to be erect as if the bird is “standing at attention”  to be flattened against forehead the while the hind crown feathers remain pushed forward (anteriorly). This result of this repositioning is that the highly reflective fore crown feathers become visible, changing the appearance from all-black to an iridescent electric blue  and 4. Athe head . In niedthe head .superba and niedda. In superba, the cape remains tightly folded over the back and is not noticeably spread , is nevertheless quite prominent with its reflective blue color visible  by the protruding “wings” of the cape—when viewed from the vantage point of an approaching female , the fore crown feathers may be pushed forward and back, which causes the eye-spots to disappear and reappear or to “flash” on and off. In that scenario, the breast shield of niedda is a leaf tossing behavior . For this behavior, the male picks up a leaf from the surface of the display log and tosses it into the air in such a way that it often lands on the log nearby. It is unclear if this behavior is a rudimentary form of court clearing behavior similar to that known from Parotia species or if it is some form of ritualized visual display like those of Parotia carolae, which uses leaves as props during certain displays . As with the initial display activities, we use s. latipennis as the standard frame of reference for the superba complex and details on s. superba are provided in niedda found it to have numerous distinctive elements, which are described below.Our analysis identified one high intensity display in both species, which we call the cape presentation display. Our analysis did not uncover substantial variation in the behaviors, postures and ornament forms among Lophorina . When performed to a female, the cape presentation display is typically initiated from the posture of the pointing display—i.e., superba and ML487557 the pointing display is omitted and the male appears to be caught off guard by the sudden arrival of the female).The visual form and movements of this display comprise the quintessential features of courtship in the genus superba and niedda, the cape-flapping of the pointing display (see above) increases in intensity. In niedda, the increase is greater as wings are lifted higher over the back and then quickly flicked open and shut in the exaggerated manner of superba during pointing .The visual effect of the exaggerated wing-flick/cape-flaps with the breast shield fully revealed is more pronounced in niedda, bouts of wing-flick/cape-flapping coincide with a distinctive very rapid side-to-side wobbling of the head. As viewed from the front  the head wobbling has the effect of making the eye-spots flicker or shimmer. It is possible that the wobbling head movement may also be present in superba, but is not discernible from the available data.In superba, the cape is lifted into its presentation position well before to female arrives on the log .Next comes the transition into the main part of cape presentation display—i.e., the presentation of the fully opened cape itself. In  the log . In L. nwith him . As the superba and niedda, the cape of both is lifted into the full display position in largely the same way. First, it is spread wide, fan-like, over the back and then it is raised (anteriorly) and pushed over the head until it creates the super-black .Once in position however, the form and appearance of the cape presentation phenotype differs dramatically between  is high  & S1F. Ieach end . In supepearance , which i superba . Correspsuperba and neidda. In superba, the breast shield is wider with thicker terminal ends that make a relatively flat surface with a fairly straight edge along the top of the ornament . This difference has the effect of making the eye-spots of niedda appear smaller with a straighter line along the top edge, which gives the “eyes” the appearance of having slightly droopy “eyelids” compared with the always larger and circular appearance of the “eyes” in superba . The repeated up and down movements coincide with a series of alternating shuffle–hop steps to the side, which create the distinct bouncing cadence: up–down shuffle–hop, first to one side and then the other. When a female is present, the male’s bouncing shuffle–hop movements revolve around the female in either a semi-circular or full circle path.Throughout the cape presentation display, the transformed oval-shaped cape presentation form of niedda, the overall appearance of the rotational movement during the cape presentation display is very different from that of superba. Instead of the characteristic bouncing of superba, the feet of niedda move in a faster, more pronounced, side-step motion without bouncing and with no visible up and down movement of the body. This creates the effect of niedda appearing to move in a quick but steady glide, rather than bounce, around the female in semi- or full circle .In superba) or smooth quick (niedda) rotational movement of the cape presentation form along the display log, the tail of both species is lifted to about 45 degrees above the plane of the back and rectrices are fanned. The secondary feathers of the wings are spread such that they become partially intertwined with the rectrices  snap-snap”  occurring with each downward bouncing stroke, “down-snap-up, down-snap-up, down-snap-up”. In both niedda and superba, the exact relationship between the wing movement and snapping sound production is unclear and warrants further investigation using high-speed videography.In superba and niedda, the cape presentation display ends when the female moves quickly away from the male such that he is unable to continue rotating around her, or when the female solicits copulation. At times the female moves along the log ahead of the male and at other times she will fly away from the log entirely. When females depart, males of both superba and niedda maintain the full cape presentation posture directed toward her and slowly lower it down over the back to readopt the pointing posture. In niedda, as the female departs, the male opens his bill to reveal the bright yellow gape once again. This end of display reveal of the gape was not observed in superba.In both ML487568 from 0:07–0:17), she beings fluttering her wings as the male rotates around her and then will tip forward with breast low and tail high. At this point, the male circles closely and mounts the female while maintaining the fully open cape posture. The cape is held in the open position during copulation and wing-snaps are given as part of, or just after, the wing flapping that occurs with copulation. After copulation, the male dismounts from the back of the female and resumes the cape presentation display directed at the female . If she departs the log, the male holds the opened cape posture for a few seconds and the slowly lowers it and often returns to the pointing display and sometimes even reverts backwards through the entire sequence from pointing, to horizontal to advertisement displays.If a female solicits copulation . While the number of notes is similar in both species, the acoustic quality of niedda is very different from the characteristic harsh screeching notes of superba , which sounds more like “yeep” instead of the up-and-down frequency of the “yiap” notes. Another common variant is slightly higher-pitched and distinctly descending to sound more like “iap” rather than “yiap”  and one complex high intensity display (cape presentation). In short, both superba and niedda share the same underlying courtship phenotype substructure, but differ in many details.Our results show that the niedda compared with superba are: (1) the protruding wing-like position of the ornamental cape during the horizontal and pointing displays  and with a constant (rather than doublet) wing-snap beat  a piercing whistle-like vocalization instead of the characteristic raspy screech of superba  theplay log ; (5) hownap beat ; and (6) superba .niedda and superba and therefore may not reflect the full scope of intraspecific variation. However, with the basic framework now in place, future studies will be able to assess this more critically as larger sample sizes are obtained. It should also be noted that while we did not find substantial differences within superba , sample sizes were limited, based on incomplete primary sources (for L. s. addenda) and included an outlying population from the Adelbert Mountains (L. s. latipennis) as the main frame of reference for all superba. To this last point, given the relatively small and geographically isolated population in the Adelbert Mountains, it is possible that further observations and larger sample sizes could reveal diagnostic differences between it (Adelbert) and cordillera populations. As noted in the Results, there are indications of differences in cadence of wing snaps and bouncing tempo of the cape presentation display among L. s. superba, L. s. addenda and L. s. latipennis. Quantifying these differences requires additional field observation and larger sample sizes.While the differences uncovered here are compelling, there are nevertheless several caveats to be considered. For example, our results are based on relatively low individual sample sizes for both niedda? We think the distinctive courtship phenotype confirms the conclusions of niedda. With the mounting genetic, external morphological and now full courtship phenotype evidence, there can now be little doubt that population of Lophorina inhabiting the Bird’s Head Peninsula of western New Guinea is a distinct evolutionary lineage that is both geographically and reproductively isolated from the nearest population of superba in the Kobowre (Weyland) Mountains of the western cordillera. Furthermore, because all the differences described here are directly related to mating preferences of females and have likely evolved through divergent aesthetic preferences of females, it seems probable that niedda and/or superba females would not find males of the other type as attractive as males of their own type. However, reciprocal exclusivity in female mating preferences is not required to meet the standards of species delineation in the birds-of-paradise. With the exception of the newly defined L. minor, for which there is not enough data to make a determination, bird-of-paradise species are defined by the degree of differences in their courtship phenotypes. In other words, courtship phenotype disparity is the key criterion for species diagnosis in the birds-of-paradise. By this yardstick, L. niedda is clearly a distinct species.What do the differences uncovered here mean with regard to the species status of niedda for the first time, our results also provide a more complete picture of the superba courtship phenotype. For the first time, we document that the initial display activity of superba is comprised of three distinct behaviors: the advertisement, horizontal and pointing displays  as a center of endemic biodiversity that deserves particular attention from the conservation community. Among birds-of-paradise alone, L. niedda now joins the three other montane species endemic to the region  and opens the door for additional systematic scrutiny of the entire avifauna of the Bird’s Head (Vogeklop) region, including the other birds-of-paradise at middle and upper elevations . Given that the Bird’s Head region was the first part of New Guinea to be ornithologically explored, and discovery of new species of birds-of-paradise was the main driver of exploration, it is surprising that the distinctive features of the niedda courtship phenotype, and therefore the species status of this unique population, have remained elusive for so long. Yet this fact underscores the need for continued exploration of New Guinea’s forests and further systematic investigation of all taxa, including those like the avifauna which are often considered to be relatively well known.The species validity 10.7717/peerj.4621/supp-1Figure S1superba superba and superba latipennis. Image credit/source: (A–D) Edwin Scholes/ML458167, (E) Kimberly Bostwick/ML487258 and (F) Kimberly Bostwick/ML487259.(A) Advertisement display and (B) vocalizing. (C) Horizontal display. (D) Pointing display. (E) Side view of the cape presentation display. (F) Frontal view of the cape presentation display. At the level of detail analyzed here, we found no discernible differences between the courtship phenotypes of Click here for additional data file.10.7717/peerj.4621/supp-2Table S1Descriptive metadata and source information for the audiovisual data used in the analysis.Click here for additional data file.


Background: Polyembolokoilamania refers to the practice of inserting foreign bodies (FBs) into natural orifices. A FB within the urethra is a relatively rare phenomenon with 646 cases recorded last year in the United Kingdom. Management of these patients presents technical challenges and complexities because of underlying psychiatric disorders that are often associated. This case illustrates a novel way of removing FBs from the genitourinary tract, requiring less resources, preventing hospital admission, and attempts to break the cycle of behavior, leading to recurrent attendance with polyembolokoilamania.Case Presentation: A 38-year-old Caucasian male prisoner, with psychiatric history presented to the emergency department (ED) with a history of inserting FBs into his urethra on 12 different occasions over a 6-week period. Of these 12 attendances, 3 resulted in admission and 2 required emergency intervention in theater under general anesthesia. After the third attendance in 5 days, it was decided to use Isiris™, a single-use flexible cystoscopy device with a built-in ureteral stent grasper, to remove the FBs and check the integrity of the urethra. The procedure was performed within the ED, without the need for admission to a ward bed or general anesthesia. Furthermore, only two members of staff were required to remove all of the urethral FBs.Conclusion: Isiris, although marketed as a stent removal device, enabled us to remove all the patient's FBs in one procedure. Isiris is an easy to use device, similar to a flexible cystoscope, that a specialist nurse or resident would be familiar using. It allows efficient and safe removal of lower urinary tract FBs, even out of hours. It requires minimal staffing support and can be done in the ED. It has the potential to reduce associated sequela of urethral polyembolokoilamania, saving resources while preserving the availability of the emergency theater. FBs in the urethra are a relatively rare phenomenon. Of the 5.6 million emergency hospital admissions recorded in England in a 1-year period between April 2014 and March 2015, 646 were because of FBs in the genitourinary (GU) tract with 89 resulting procedures recorded  for their removal.1PA large number of patients who insert FBs into their urethra may do so on multiple occasions, thus the management of these patients poses challenges of both a psychiatric and technical nature. When theater time and resources are required to remove FBs, the costs to the healthcare provider organization markedly increase. This case illustrates a novel method of removing FBs from the lower GU tract, which has the potential to save resources, prevent hospital admissions, and to break the cycle of behavior that leads to recurrent polyembolokoilamania.This case describes the management of a 38-year-old Caucasian male prisoner, with a previous psychiatric history of borderline personality disorder and deliberate self-harm, who presented to the emergency department (ED) with a history of inserting multiple FBs into his urethra on 12 different occasions in the space of a 6-week period. Of these 12 attendances to the ED, 3 resulted in admission and 2 required transfer to the emergency operating theater for their removal under general anesthesia. Representative plain radiographs demonstrating the FBs are shown in On one occasion, the patient developed acute urinary retention necessitating emergency insertion of a suprapubic catheter (SPC). Subsequently, the patient deliberately dismantled the SPC and reinserted it into the urethra. Thereafter, attendances to the ED became more frequent. After consultation with the prison and psychiatry services, it was decided that, whenever possible, hospital admission and urethral catheterization should be avoided. Further management plan at that stage was for a future elective admission for removal of the retained SPC tip and associated FBs from the bladder under general anesthesia.In the subsequent week, the patient presented to the ED three times after insertion of further FBs. On each attendance, the FBs were easily palpable in the urethra and their removal was effective using forceps. However, on each occasion despite there being no other palpable FBs in the urethra, the patient was adamant that he was unable to void, until a catheter was inserted.On his next attendance, the third in 5 days, it was decided to use Isiris™, a single-use flexible cystoscopy device with built-in stent grasper , to remoBecause of the rare nature of these cases and the added psychiatric component, a holistic approach to the management of polyembolokoilamania is required. A multidisciplinary approach was adopted with consultation of all key stakeholders , resulting in a safe and efficient intervention. This minimized both reinforcement of the patient's behavior and the impact upon the organization's capacity and resources. Such a response was required because of the spiraling situation, culminating in three attendances in 5 days with the patient becoming more demanding and manipulative so as to remain an inpatient in hospital.Although marketed as a stent removal device, Isiris enabled the removal of all the patient's FBs in one procedure in the ED. As Isiris is an all-in-one single-use system, there are multiple advantages of using it over rigid or flexible cystoscopy in such cases. First, the equipment can be kept in a 24-hour access storeroom and can be easily transported by a single person; it was taken from our storeroom to the patient and the procedure was performed in its entirety in the ED at the patient's bedside. This negated the need for admission of the patient and subsequent transfer to the emergency operating theater suite for conventional flexible or rigid cystoscopy.A single-use Isiris cystoscope plus monitor costs £258 ($333). Cost is not the only advantage of being able to remove FBs in the ED. There are a number of patient factors that would make immediate endoscopic removal of FBs in the ED favorable, in particular the ability to avoid a general anesthetic. The procedure itself is well tolerated and markedly reduces time in hospital, allowing a quicker return to normal activities. Furthermore, endoscopic removal of FBs using Isiris can be performed by a wide spectrum of clinicians who are competent to perform flexible cystoscopies, including more junior medical staff. This further reduces waiting times for patients. Removing the FBs immediately upon presentation may also offer the potential benefit of avoiding the complications associated with urethral polyembolokoilamania such as strictures, retention, periurethral abscess, and fistulation.4 By avoiding admission and dealing with the problem within the ED, there is the potential, along with the help of psychiatry/psychology, to begin to break these patterns of behavior and relieve some of the economic burden posed by malingering patients.There are many reasons people insert objects into natural orifices and there is a vast amount of literature surrounding polyembolokoilamania and the psychologic nature of the disease. Malingering refers to the act of deliberately feigning physical or psychologic symptoms to achieve a tangible secondary gain. In this case, the patient had a substantial and lengthy history of nonsuicidal, self-injurious malingering behavior. The phenomenon of urethral polyembolokoilamania by prisoners in a maximum-security hospital to control prison and hospital staff has been reported before and is difficult to manage.Although this scenario is admittedly rare, it provides a novel way of dealing with urethral polyembolokoilamania. Isiris is an easy-to-use device with which a urology specialist nurse or urology resident would be familiar. We have reported its utility for efficiently and safely removing urethral FBs, even out of hours. It requires minimal staffing support and can be done in the ED itself, thereby providing better and more expedient patient care. It has the potential to reduce the likelihood of complications associated with urethral polyembolokoilamania, while also saving resources in addition to preserving the availability of the operating theater for other surgical emergencies.