Granular multiparticle ensembles are of interest from fundamental statistical viewpoints as well as for the understanding of collective processes in industry and in nature. Extraction of physical data from optical observations of three-dimensional (3D) granular ensembles poses considerable problems. Particle-based tracking is possible only at low volume fractions, not in clusters. We apply shadow-based and feature-tracking methods to analyze the dynamics of granular gases in a container with vibrating side walls under microgravity. In order to validate the reliability of these optical analysis methods, we perform numerical simulations of ensembles similar to the experiment. The simulation output is graphically rendered to mimic the experimentally obtained images. We validate the output of the optical analysis methods on the basis of this ground truth information. This approach provides insight in two interconnected problems: the confirmation of the accuracy of the simulations and the test of the applicability of the visual analysis. The proposed approach can be used for further investigations of dynamical properties of such media, including the granular Leidenfrost effect, granular cooling, and gas-clustering transitions. Ensembles of macroscopic, hard particles that interact by inelastic collisions are of considerable academic interest as a test for analytical and numerical treatments of multiparticle systems. Moreover, they are also of fundamental importance, e.g. for the understanding of fluidization of granular materials, of granular flow and behaviour under shear. Dilute ensembles, commonly referred to as granular gases, are relevant for the understanding of the interactions of cosmic dust particles that lead to their self-organization to high-density clusters, planetesimals, large orbs and planetary structures. Similar ensembles of hard macroscopic particles are present in terrestrial natural phenomena as well, e.g. in the form of whirlwinds (dust devils) and avalanches, or in technological processes, e.g. in agriculture, pharmaceutical or construction industry.1. We will focus on such systems here, but in principle many of the ideas detailed here may also be exploited for the study of much denser, fluidized granular beds.The dissipative nature of the particle-particle interactions distinguishes granular gases qualitatively from atomic or molecular gases. A comprehensive introduction to granular gases is given, e.g. in Ref.2, has attracted considerable theoretical efforts, while experimental studies remained comparably scarce. Experiments were so far largely restricted to small ensembles  particles in the central part of the observed volume, which is flanked by two regions with ’hot’ (high kinetic energy) particles of lower particle number densities towards the excitation pistons. Eshuis et al.37 described a similar phenomenon in shaken granular matter in normal gravity as a granular analogue of the Leidenfrost effect, where a liquid droplet levitates above a hot plate on a vapor layer. A similar situation occurs when the pistons move in phase, i.e. conserving the volume available to the grains32. Menendez et al.47 studied this ’granular Leidenfrost effect’ in microgravity using X-ray imaging. They established a characteristic state diagram with the excitation strength and the filling fraction as its essential parameters. Qualitatively similar results were reported under (non volume-conserving) vibrational in-phase excitation28. Most of these experiments were performed with spherical particles, with the advantage that numerical simulations of collisions are particularly simple. Often, they are even approximated by using a constant normal restitution only.One of the activities to study granular gases under strong excitation is the 26. Thus, one finds more inter-particle collisions in a given ensemble but approximately the same number of particle-wall collisions. In low filling fraction experiments, this is advantageous since the latter disturb the bulk dynamics and tamper the behaviour of the whole ensemble. With elongated cylinders, one can perform experiments with the same number of particle-particle collisions at lower particle number densities, which is favorable for the optical observation of the ensemble (less coverage of particles in the back). An equally interesting aspect is that the cylinders add a rich variety of effects to the collision processes50. For example, rotational degrees of freedom can be excited without frictional contacts between the cylinders. Collisions with plane container walls are more complex than for spheres51. It is much easier than for spheres to identify the rotational degrees of freedom of the particles and to evaluate rotational kinetic energies.Here, we extend this study to cylindrical particles. There are strong arguments to use cylinders instead of spheres: The number of particle-particle collisions at a given particle number density and particle volume is considerably larger for elongated cylinders than for spheres52. This demanding task can be tackled manually25 or using Machine Learning algorithms53, but even 2D tracking becomes complicated when the filling fraction of the observed volume is more than a few percent, because particles in front screen background particles. Particle assignment in subsequent videos is not always trivial. Shadows cast by the grains or reflections on the particle surfaces make a homogeneous illumination practically impossible. Sophisticated tracking mechanisms for clouds of spherical particles at low filling fractions were recently presented with application to dusty plasmas54. Some information on the evolution of a sufficiently dilute granular ensemble, the local filling fraction and general trends of the direction of motion can be obtained by averaging image sequences26. Even accurate information on particle distributions may be derived from reflection patterns in a granular gas of spheres6, a method not straightforwardly extendable to elongated or irregular grains. Particularly in local regions of high filling fractions, methods evaluating the optical density of the granular ensemble from a 2D perspective suffer from particle overlaps. They essentially reflect the behavior of the front particles, while little information is available on the grains behind. Similar challenges of 3D and 2D tracking of multiple particles are often encountered in the studies of complex (dusty) plasmas55.A key problem of the quantitative analysis is the determination of particle positions, velocities and orientations from optical video footage. In general, this includes the problem of 3D tracking of particles on the basis of two stereoscopic images, which is particularly challenging because of particle overlaps, see, e.g., the experimental data in Refs.45. The method was successfully applied in Refs.56. For elongated grain shapes, however, the situation is much more complex and KS-tests tend to fail. Several conditions for this method are violated: Because of the multiple possible 3D orientations, elongated particles cannot be separated from each other by dividing the container into equally thick slices as it can be done for spheres. Moreover, the number of independent slices (classes) needed to perform a reliable KS-test cannot be attained. This problem increases with higher aspect ratios of the grains.For the spherical grains, Kolmogorov-Smirnov (KS) tests provide a solid criterion to distinguish between a gaseous state and a dynamic cluster of particles The aim of this study is to develop methods to extract local velocity, kinetic energy, and particle number density data from densely packed, periodically excited granular ensembles of cylindrical rods. We apply this approach to synthetic data to test the correctness and reliability of the mathematical analysis, and further apply the method to experimental data from parabolic flights. The approach is equally suited to solve the simpler problem of ensembles of spherical particles.43. The parabolic flight equipment was built by DTM Technologies . Here, only few aspects are important: the type and parameters of excitation, the shape, size and amount of granular material, and the imaging method. Figure Experiments were performed with the Vip-Gran instrument during two ESA parabolic flight campaigns . A detailed description of the setup and experiments is found in Ref.N monodisperse steel rods with diameter In each run of the experiments, an ensemble of Mikrotron EoSens 4CXP) at a resolution of Bottom and front sides of the container are illuminated by two LED panels. The grain ensemble seen from these perpendicular perspectives is recorded at a frame rate of 900 pictures per second by two synchronized high speed cameras  of density profiles obtained from experimental data roughly corresponds to the threshold found by studying the caging threshold in the simulated data.An image analysis for spheres providing local particle number densities in similar experiments was successfully employed earlier. The introduction of the analysis methods is given for beads  in Refs.Here, we perform a more thorough analysis of the shadow density profile for elongated particles. Furthermore, we suggest a method based on feature tracking to extract the mean velocity profile from image sequences.57 for the experiment from Ref.24 using similar code to what is employed here.Our approach is based on the following methodology: Numerical simulation provides particle positions and orientations as well as velocities for a given ensemble, using realistic simulation parameters. ’Synthetic’ images are created from these ensemble data. These images are analyzed with the proposed evaluation tools and compared with data extracted with the same tools from experimental images. While the individual particle arrangements will naturally differ in experiment and simulation, we take agreement between the statistical ensemble data as the decisive criterion to test both the reliability of the simulation and the accuracy of the evaluation approach. We can presuppose that the possibility of both methods giving a similar but incorrect profile which mimics the observed images naturally exists, but is minimal. This approach allows us to infer a reasonable degree of reliability both to the simulation technique and to the visual analysis method. We do not aim at a detailed agreement between experiment and simulation in all physical statistical quantities here, such a first study is found in Ref.58. This numerical tool was adapted to simulate confined systems with moving walls. The model considers the evolving dynamics of a monodisperse ensemble of spherocylinders, with length r, i.e. aspect ratio r. For the particle-wall collisions, we used the same model but assuming the interaction of a spherocylinder with an infinite moving plane. The force i by the particle j reads as: 59, depending on the overlap distance We used a hybrid GPU-CPU implementation of discrete element modelling (DEM)60. A simulation time step was chosen equal to 61.The simulated container was defined with the same geometry as in the experiment, in the same coordinate system. Similar to the experiment, an amplitude We concentrate here mainly on a part of the data set (of the 71. and 72. ESA PFC) with cylindrical rods of aspect ratio 3. This choice of short rods minimizes the effects of particle rotations on the evaluation approach (see below), which may require further refinement of the analysis. Note that every method successful for short cylindrical rods will also be suitable for spheres. The influence of the rotational degrees of freedom would vanish in ensembles of spherical grains.We performed simulations for 183, 367 and 733 spherocylindrical particles of 3 mm length and 1 mm diameter with parameters similar to the experiment. Namely, we were aiming to simulate the 1st, 6th and 16th parabolas of 1st day, ESA PFC71. First, 10,000 simulated frames with the same frame rate as in the experiment were created (corresponding to entclass1pt{minima28) for both experimental and simulated data. First, a brightness threshold was applied to the image to separate shadowed areas from illuminated ones. Then, the image was separated into slices with the length of an effective particle diameter 56, the local density ground truth), from the shadow density of corresponding simulated images, and from the shadow density of experimental video frames. Both perspective views (front and bottom) were used for shadow density analysis. The density profiles were averaged over 100 periods for simulation and 50 periods for the experiment . Here we use video footage from frame 3800 to frame 6800. The plot includes error bars for the standard deviation.We then extracted the density profiles along the horizontal axis  in the x-direction which reaches values of 0.005 g for this particular time frame of the PFC71 parabolic flight. It is worthwhile to analyse if such a shape of the profile can be reconstructed with the shadow density method. We have performed additional simulations, where a slight variation between left and right wall collision parameters was artificially introduced. This resulted in an asymmetry of the simulated density profile that resembles the one observed in the experiment. It turns out that such a profile asymmetry is accurately reproduced by the shadow profile image analysis, see Fig. Additionally, a slight asymmetry of the density profile was observed in the visual analysis of the experimental data, see Figs. An extensive effort will be required to improve the accuracy of the shadow density analysis for different numbers of particles (filling fractions) and other parameters. However, for the present purpose, we notice that the shapes of the density profile are satisfactorily similar for both simulation and experimental data. A more detailed analysis of experimental data will be possible when more data sets for different parabolas will be available and can be compared with simulations.45. A procedure which at least indirectly allows to infer mean velocity profiles from the time evolution of density profiles was recently suggested47. However, this method does not provide information on particle dynamics.The density profiles yield some insight to the particle distribution in space, but do not lead to a deeper understanding of their underlying motion. The extraction of mean velocity profiles from experimental data could provide greater insight into the particle dynamics, particularly in regions of different local particle number densities, and will help to detect dynamic clusters62 and applied to the extraction of data in a number of publications64. Our system is, naturally, very different from the original application scenarios of PyTrx. The visual velocities of glacial profiles are usually comparably slow and variations between the consecutive video frames are very subtle . In our case, however, the periodic nature of the experiment allows to average data across many cycles to obtain smoother results. We found that the general approach is feasible after averaging 100–200 cycles, depending on the number of particles in the system. There are two velocity detection procedures in PyTrx, dense  and sparse . In the dense mode, frames are separated into small rectangular regions (windows) and a displacement vector is obtained for every window by cross-correlation between corresponding windows in consecutive video frames. In the sparse mode, the Lucas-Kanade optical flow algorithm65 is applied using the OpenCV function calcOpticalFlowPyrLK66 to find the pairs of corresponding points in a couple of consecutive video frames. For a relatively dilute system like the granular gas, where neighboring particles often move in different directions, the sparse algorithm should perform better. To confirm this, we applied both dense and sparse velocity detection procedures and obtained much more accurate results in the sparse mode.In what follows, we describe a method to extract particle velocity profiles which is suitable for the case when most of the individual particles cannot be tracked. We adopted a variation of a PIV (Particle Image Velocimetry) method developed for the glaciological analysis of visual data (estimation of surface ice velocity from time-lapse images). Those methods were realized in the Python software package PyTrxwindow size, which governs the maximal search distance between similar features in two frames. The second parameter is the backtracking threshold. It works as follows: The features are tracked from the original image to the next. Then the order of the frames is reversed and the features from the second frame are tracked back to the original. Only the points which appear closer to their origin than a threshold value are selected. There is as well a Boolean switch which regulates if image histogram equalization should be performed before performing edge detection or not. We found that to obtain a more accurate reconstruction of the velocity profiles, histogram equalization should be omitted: Turning it on generally allows to detect more feature displacements between subsequent frames, but introduces a lot of noise in extracted velocity data.There are two important parameters in the calcSparseVelocity procedure of the PyTrx package which extracts the velocities from a couple of consecutive frames. One parameter is the We performed a comprehensive search for the best combination of the two parameters mentioned above (window size and backtracking threshold) that provides the most accurate reproduction of the ground truth mean velocity data for two different simulated packing fractions (number of particles). We found that the best results correspond to a window size equal to 30 pixels and a backtracking threshold of 10 pixels. The value set for the window size parameter roughly corresponds to the optical translation of the fastest particles between consecutive frames. Due to the relatively high velocities of particles (in comparison to the frame rate of the video) and visual noise, only few features which travel not too far between subsequent frames and which are not obscured can be accurately backtracked. Consequently, the value for the backtracking threshold has to be relatively large in order to account for particles with higher velocity. Figure The lines in Fig. One obtains sufficiently good reconstruction results both for the lower and the higher packing fractions of the short 45 as well as qualitative experimental results from47. A comprehensive study of mean velocity profiles for various microgravity experiments is part of ongoing research.After performing the calibration procedure with synthetic data, we demonstrate how the method can be applied to reconstruct mean kinetic energy profiles from the experimental video footage. Figure les Fig. a with thles Fig. b and witles Fig. c. All thsquared velocities in Figs. At this point, it is appropriate and necessary to discuss the averaging process and the meaning of reconstructed velocity and energy data from the previous section: Important parameters for the description of the granular gas are the local granular temperature and the local material transport. In our evaluation, we have employed a synchronous averaging over fixed phases of a periodically excited system, assuming that the state of the granular gas is also periodic. We have presented the averaged The primary purpose of this study is to propose, test and calibrate methods for the characterization of grain ensembles in situations where individual particle tracking is impossible. Its focus is the extraction of information on local packing fractions and mean velocities on the basis of 2D optical images. Even though we have access to two stereoscopic projections of the ensemble, we did not make attempts to extract 3D particle positions or velocity data. This requires the necessity to collate individual particles in both perspectives, which is practically impossible in view of multiple overlaps.For the present type of granular ensembles, no other evaluation method is available. Thus, we had to design a calibration strategy that uses numerically simulated data. The test procedure is based upon the composition of synthetic frame sequences from simulated particle ensembles, where all information regarding the particles is known for each frame, followed by a subsequent comparison of particle position and velocity data from image analysis with the original data (ground truth).28 yields satisfactory reconstructions of the number density profiles, but it is unsuitable for an extraction of velocities and kinetic energies as key figures of merit in cluster formation. For this purpose, we applied a software (PyTrx) developed for geophysical research62 that is based on a feature tracking algorithm. We demonstrated by comparison of velocity data extracted from synthetic images with the original data that this method yields very good results for elongated cylinders or spherocylinders with sufficiently short aspect ratios  in order to get more and better features from the images. Coloring particles individually will improve the feature detection and tracking process enormously. The detection and analysis of features is certainly not limited to optical images in the visible spectrum. One can also adapt the method to the analysis of X-ray radiography provided that the particles themselves are resolved. Then, the problem of overlaps becomes less restrictive since features from background particles are also accessible.A further improvement can be envisaged in the application of methods of machine learning  to the images, or intermediate visual analysis data for a reconstruction of smoother and more reliable profiles.


The acute stress response is a natural and fundamental reaction that balances the physiological conditions of the brain. To maintain homeostasis in the brain, the response is based on changes over time in hormones and neurotransmitters, which are related to resilience and can adapt successfully to acute stress. This increases the need for dynamic analysis over time, and new approaches to examine the relationship between metabolites have emerged. This study investigates whether the constructed metabolic network is a realistic or a random network and is affected by acute stress. While the metabolic network in the control group met the criteria for small-worldness at all time points, the metabolic network in the stress group did not at some time points, and the small-worldness had resilience after the fifth time point. The backbone metabolic network only met the criteria for small-worldness in the control group. Additionally, creatine had lower local efficiency in the stress group than the control group, and for the backbone metabolic network, creatine and glutamate were lower and higher in the stress group than the control group, respectively. These findings provide evidence of metabolic imbalance that may be a pre-stage of alterations to brain structure due to acute stress. It is important to understand the stress response because it is a fundamental natural mechanism for survival that protects the immune system and improves functional performance from external stimuli3. The stress response, which involves some, neurotransmitters, and neuropeptides, occurs over time, ranging from tens of minutes to hours5. The effects of the stress response manifest themselves in two ways: short-term and long-term. Time-dependent variation of neurochemicals, including the stress hormones, necessitates investigation of the metabolic changes over time2. If the stress response is maladaptive over time, it causes structural and functional changes in the brain7.Acute stress is a physiologically or biologically adaptive response to one extreme stressor that threatens homeostasis8. Since the hippocampus is involved in the regulation of the hypothalamic-pituitary-adrenal (HPA) axis, which regulates stress hormones  and neurotransmitters by negative feedback, the hippocampus is an important stress-related region and vulnerable to acute stress10. Because of the difference in the function and balance of stress hormones and the interaction of neurotransmitters, the brain’s ability to regulate and successfully adapt to acute stress, called stress resilience, varies between individuals4. Stress resilience is important and is necessary for a variety of situations that can cause acute stress with the following associations: 1) quick activation and successful termination of the stress response11 and 2) the ability to restrain the increase of stress hormones and neurotransmitters due to acute stress through a sophisticated negative feedback12. If not, the brain is vulnerable to acute stress, which induces a reorganization of brain architecture, which may be associated with the onset and exacerbation of several neuropsychiatric disorders13. Considering these findings, it can be concluded that studies on time-dependent responses to acute stress are necessary, and they can be analyzed through temporal changes in neurotransmitters as opposed to in previous studies15 that compared the changes of neurotransmitters with a focus on consequential changes, which can cause bias.The brain is a central mediator that regulates the stress response9. Using metabolic variation and changes in the concentration of metabolites over time, the relation between the metabolites, which may be used as a measurement of the similarity of metabolite conditions, profiles, and biological information, can be determined with analyzable values. It is important to analyze the alteration of metabolic relationship because it can provide insights for understanding the dynamics of metabolic response to acute stress16. Since the conventional To investigate the changes in neurotransmitters over time, a proton magnetic resonance spectroscopy  and edges  between pairs of nodes in the present study. In the network analysis, the small-worldness (SW) proposed by Watts and Strogatz23 may be employed to evaluate whether the network is a realistic network relative to a random network24 and whether the network balances global integration and local segregation for efficient information processing25. This study presumes that network analysis can provide effective and comprehensive information for the process of metabolic response to acute stress.A real-world complex system in a multidisciplinary research area can be modeled as a network27. McDermott et al. reported that the connectivity related to the left hippocampus was significantly correlated with both chronic stress and acute stress26. In addition, Rahman et al. found that the stress affected the left hippocampus volume much earlier than the right hippocampus volume27. Therefore, we choose the left hippocampus as voxel of interest (VOI) to investigate the dynamic change in the characteristics of the metabolic network after exposure to acute stress.Previous studies provided evidence that the left hippocampus is more vulnerable to stress than the right hippocampusin vivo, Our study aims to provide a new approach for investigating the metabolism association between metabolites using mentclass2pt{minimDuring metabolite quantification through LCModel, two mice were excluded from analysis because they did not meet the criteria of Cramer-Rao lower bound (CRLB). Eleven mice were used in each control group and stress group for analysis.For the metabolic network analysis at each time point, the metabolic networks in the control group met the criterion of SW across a wide range of correlation coefficient thresholds  for all time points Fig. . This fiFor the metabolic backbone network analysis, while the backbone network in the control group met a criterion of SW across a wide range of thresholds , the backbone network in the acute stress group did not meet the criterion. In addition, the acute stress group showed lower of SW than the control group . This suggests that the metabolic link may not be maintained constantly over time after exposure to acute stress , and glutamine (Gln) in the backbone metabolic network of the control group but not in the backbone metabolic network of the stress group across all thresholds. There were Glu-related connections in the backbone metabolic network of the stress group but not in the backbone metabolic network of the control group across all threshold. In the backbone metabolic network of both the control and stress group, there was no alanine (Ala)- and phosphorylcholine (PCh)-related connections across all threshold.28. These metabolites do not function independently but interact together with functional connections in the brain’s metabolism. To investigate the relationship between metabolites based on the 30. Since the metabolic network met the criteria of SW across a wide range of thresholds, the metabolic network can newly be considered as one of the real networks. Second, the stress group had lower local efficiency , glutathione (GSH), and Cr. The change in concentration of these metabolites after exposure to stress can be explained by the previous findings which reported that the glucocorticoids such as corticosterone (CORT) by the hypothalamic-pituitary-adrenal (HPA) axis activity due to acute stress, induced the increase of the concentration in Gln, which was a precursor of the neurotransmitter by transporting one from the glial cell to the glutamatergic presynaptic neuron35, acute stress can accelerate Cr release. Rapid release of Cr may break metabolic synchronization between Cr and other metabolites. It might cause reduced Low 22. In the backbone metabolic network analysis, a cluster consisted of Gln, Cr, and phosphocreatine (PCr) in the control group, but there was no cluster in the stress group across a wide range of thresholds. Therefore, we focused on interpreting this cluster to understand the change in metabolic association due to acute stress responses. The Cr and PCr are directly related through the creatine kinase (CK) . The PCr is decomposed into Cr and inorganic phosphate for adenosine triphosphate (ATP) formation through the CK. Since ATP hydrolysis results in products of adenosine diphosphate (ADP) and inorganic phosphate, it is re-synthesized into PCr through the CK36. The Cr and Gln are indirectly related through the Glu-Gln cycling. The Cr enhances the energy-consuming  conversion of Glu to Gln through Glu-Gln cycling37. According to this phenomenon, there is a cluster that consists of metabolic connections between Cr and PCr, Gln and Cr, and Gln and Cr in the control group. Since the Cr has a sedative-hypnotic effect in acute stress response35, acute stress can accelerate Cr release. In addition, the Glu from the presynaptic neuron is released upon the HPA axis activity after exposure to stress38. Because the released Glu has neurotoxicity, it travels to nearby astrocytes and is converted to the Gln by consuming energy39. The converted Gln is back transported to the presynaptic neuron. Consequently, the acute stress caused alteration in the concentration of Cr, PCr and Gln that may have led to a break in the Gln-Cr-PCr cluster after exposure to acute stress. Furthermore, although the metabolic network in the stress group was not a SW network, remarkable Glu-related connections emerged in the present study. The acute stress activates the HPAaxis10. The hypothalamus releases the corticotropin-releasing hormone, which stimulates the pituitary secretion of the adrenocorticotropic hormone. It activates CORT release from the adrenal glands41. CORT enters the hippocampus via the bloodstream and combines with the glucocorticoid receptors to release an excitatory neurotransmitter, Glu, from the presynaptic membrane of the neuron39. In particular, Glu-Gln cycling in the hippocampus of the mouse brain can be described as the transport of Glu acting as the excitatory neurotransmitter and Gln forming through the amidation of Glu in the tripartite synapses43. Thus, Gln production and Glu decomposition occur in astrocytes, and the opposite effects occur in neurons via Glu-Gln cycling44. These reasons explain that the connection between Gln-Cr and Gln-PCr broke after exposure to acute stress. There is evidence for forming Glu-related connections in the stress group. Many kinds of stress affect the metabolites in the glutamatergic and aspartate systems through the tricarboxylic acid (TCA)-cycle45. The Glu interacts with 47. It can be caused by N-acetylaspartate (NAA) produced from the aspartate and acetate, and it is synthesized with adenosine triphosphate and converted to acetyl-coenzyme A. Finally, this synthesis affects the aminotransferase, which reacts with [aspartate + 48. This reaction, which results in increasing Glu concentration, may induce an increase in NAA concentration, and in consequence, Glu is statistically connected with NAA.In a network, a specialized function and segregated processing are based on the concept of cluster or connected triangle49. Interestingly, one study proposed that stress causes the change in metabolism, including glucocorticoids and serotonin acting through excitatory amino acids, which may be a mediator of hippocampus atrophy50, and that glucocorticoids can influence neurotransmission such as Glu and 51. Analysis of the metabolic network can provide evidence of metabolic imbalance, which may be a pre-stage of alterations to the brain structure due to stress, and additional research is required to investigate the stress response in the hippocampus.In the present study, our findings propose that acute stress induces an imbalance between metabolite variations, which forms random metabolic connections in the left hippocampus, and it needs a period to return to the pre-stress level. Although it is a natural stress response, previous studies have reported changes in the metabolism and brain structure of the hippocampus due to stress. The acute stress induces increases in extracellular Glu, and it may thus underlie the dendritic remodeling in the hippocampus52. Isoflurane-based inhalation anesthesia was applied to minimize the accumulation of anesthetic metabolites and the harmful effects for long-term use of the anesthetic, and it makes inhalation anesthesia with isoflurane possible for 8 hours since no practical difference occurs53. Since all experimental conditions were made the same, the comparison between groups can be considered as an analysis for the effect of restraint stress. Although this is beyond the scope of the current study, it remains an important line of inquiry for future research. Despite these limitations, the present study provided a non-invasive measurement of dynamic change in the connection between metabolites after exposure to acute stress.The main strengths of this study were 1) the mouse age, gender and strain homogeneity, which reduced associated confounding with the metabolites and stress response that could change depending on the above conditions and 2) In summary, we focused on the correlation coefficient between the metabolite concentrations  after exposure to acute stress. Although it remains unclear how an individual-level metabolic network might be constructed directly from 54. In the light phase (08:00-14:00), the changes in corticosterone of control animal are insignificant55 as well as the level of corticosterone that is increased by restraint stress is much higher than that by changes in the circadian rhythm56. Control animals were kept in the cages without removal of food during the period of restraint stress because food deprivation can be a stressor. The mice were randomly divided into a control group (n = 12) and a stress group (n = 12).Male C57BL/6N mice  with body weight of 18-25 g at the age of four weeks were used as experimental animals. The mice were housed three per transparent plastic cage and were allowed to acclimate for two weeks before https://arriveguidelines.org)57.All experiments were approved by the Institutional Animal Care and Use Committee and conducted at Lee Gil Ya Cancer and Diabetes Institute accredited in Association for Assessment and Accreditation for Laboratory Animal Care International (AAALAC), which is the international standard for animal care and use globally. All procedures for experiments complied with the Center of Animal Care and Use (CACU) for Animal Research . In addition, the study was carried out following the ARRIVE guidelines . The holes of the plastic tube toward the body (3 mm in diameter) and mouth (5 mm in diameter) of a mouse were made to promote airflow for ventilation and kept intact with the background noise in the air-conditioned room for the restraint procedure  was used for data acquisition. To localize a voxel of interest (VOI) in a mouse brain, T2-weighted MR images were acquired using rapid acquisition with relaxation enhancement and the following acquisition parameters: TR = 5000 ms, TEmentclass2pt{minimin vivoTo investigate dynamic metabolic changes over time, in vivo spectra to be analyzed later. In addition, since it is specific to MRI  and region , the model must be recreated if the conditions of the in vivo spectrum to be analyzed change. The second is to acquire unsuppressed water signals for absolute quantification of metabolites from in vivo spectra. Third, it is possible to estimate the intensity of each metabolite by fitting it to the in vivo spectrum using the constrained nonlinear least-squares algorithm based on the basis set and the zero- and first-order phase correction62.The method for determining the intensity of the spectral peaks by LCModel used for metabolite quantification in the present study is as follows: First, it is a model that includes not only the baseline (including macromolecules) but also the spectra obtained from the phantom solution of each metabolite to be included as a basis set known as prior knowledge such as concentration and chemical shift. This model should contain spectra of very high quality and should have the same protocol as the 64. These corrections were repeated once for each To correct the frequency and phase shift caused by a long scan time and acquire a series of 65. Of the 17 metabolites included in the simulated basis-set, the 11 metabolites that met the Cramer-Rao lower bound (CRLB) of < 35% at all time points were included in the analysis: alanine (Ala), creatine (Cr), phosphocreatine (PCr), 66.The concentrations of metabolites were calculated by fitting the experimentally obtained mentclass2pt{minim67. Usually, Pearson’s correlation analysis is conducted when the assumptions of this test including normality distribution and both variables measurement are met68. However, our data did not meet the normality distribution and small sample size. Thus, to characterize the relationships between metabolites, we employed Spearman’s rank correlation analysis which is another common statistical correlation method that could be adopted if Pearson’s correlation analysis assumptions are not met68. This study first defined a metabolic connection as statistical associations measured by computing Spearman’s rank correlation coefficient of mice between the concentrations of every pair of metabolites   Fig. . Both co69. Thus, it may not be a meaningful global measure for the sparse network. An alternative measurement can be global efficiency  the global efficiency  normalized global efficiency [72. To test the null hypothesis that the observed group differences could occur by chance, this study randomly reallocated a set of metabolite concentrations in each mouse to the control group or stress group, recomputed the correlation matrix for each randomized group, and then obtained the corresponding binarized matrix using the same correlation threshold. Next, this study calculated the SW for each randomized group and obtained the differences between the randomized groups. This randomization procedure was repeated 1000 times at each correlation threshold value. The 95 percentile points of each distribution were used as the critical values for a non-parametric permutation test of the null hypothesis with a probability of type I error of 0.05. The p-values were adjusted for multiple comparisons using false discovery rate (FDR) correction. Group differences in the areas under curves (AUCs) were assessed at all correlation thresholds.A non-parametric permutation test was applied to determine the statistical significance of the differences in the SW between groups


Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies , as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively. In outpatient care, back pain, and knee joint osteoarthritis are among the 30 most common individual diagnoses, with approximately 20% of 18–79-year-olds having physician-diagnosed osteoarthritis, whereby knee and hip joints are most commonly affected , for exaArtificial intelligence (AI) and machine learning approaches are gaining increasing interest in the field of biomechanical clinical data analysis and have obtained promising results  deviations. Unsupervised outlier detection methods may have the potential to perform this. A previous work  used a oOverall, there is a lack of research regarding those classifiers and their interpretation. According to current practice, they are treated as black boxes. This does not comply with GDPR and strongly limits applications in the clinical context. Furthermore, it is questionable whether XAI tools are suitable for interpretation in this context. Therefore, the aim of this research is to design and evaluate a pathology-independent classifier that provides explanations of classification decisions using XAI. Using clinical spinal posture data of the clinically highly relevant pathologies back pain, spinal fusion, and osteoarthritis, we wanted to check whether it is possible to adequately explain pathologic differences compared to healthy controls with a pathology-independent classifier and to generate clinically relevant insights. The classification results are compared with those obtained using commonly used binary classification approaches.Across four studies, we collected data of 151 subjects. Depending on the study design of the respective study see , for eveFifty-five static parameters provided by the system were used (pelvic obliquity (°), pelvic torsion (dimples) (°), pelvic inclination (dimples) (°), pelvic rotation (°), and orientation of VP, T1–12, and L1–L4 in all planes (°)) for modeling. For a detailed description of the parameters, see ,39 and An = 24 subjects) of known separation to the healthy reference subjects were created. The use of the synthetic data is intended to ensure that groups of different class separations are present to check the performance of the proposed approach in settings with well separated groups as well as settings with little separated groups. To maintain similar spinal characteristics compared to real-world data for the synthetic posture data, data creation was based on the principal components (PCs) of the healthy subjects. After removing the mean and scaling to unit variance, principal component analysis (PCA) was applied. Four different synthetic classes were created, each of which was based on a modification of one PC. Therefore, separately for the first four PCs, data were created by adding random samples from a normal Gaussian distribution. The center of the distribution was set according to the desired cluster separation  with a standard deviation equal to 1. Afterward, the synthetic PCA data were transformed back into the original space.For evaluation of the classification performance in terms of the dependence of the class separation, synthetic subject data  with five folds  was used for model evaluation to check if the model was able to generalize to new subjects. Therefore, the data were split considering the subjects to ensure that the samples of each subject were not present in both the training and the testing data. The data of each training fold were split into data for actual training and data for validation  to allow hyperparameter searching and probability calibration/calculation without introducing a bias. Standardization was performed based on each respective training set by removing the mean and scaling to unit variance.OCSVM  was usedThe classification output was transformed into a probability distribution over the classes according to Platt´s method . TherefoFor comparing the results of the proposed methodology with those obtained using a classical binary classification method, the random forest classifier (RF)  was usedThe model-agnostic interpretation tool Local Interpretable Model-Agnostic Explanations (LIME)  was usedUncertainties were analyzed using classification probabilities. Due to partly imbalanced classes, predicted probabilities were evaluated using the Brier Skill Score . ClassiThe results for the OCSVM, as well as the binary classification approach, are presented in For the synthetic data, the binary classifier performed better than the OCSVM (except S2). The difference was especially visible for the data with the lowest separation from the group of healthy subjects . Surprisingly, OCSVM performance was reduced for S1 compared to S2, and even the class separation measured by means of the silhouette score increased for S1.The local results for the pathology with the highest classification performance  for the OCSVM, as well as the binary classifier, are presented in Using the mean probability values of the measurements of each subject, 27 and 7 subjects were correctly and falsely classified, respectively, out of the 34 subjects with spinal fusion. For 8 of the 27 correctly classified subjects, the class probability difference was below 0.2. Using the RF approach, 9 and 25 subjects were falsely and correctly classified, respectively. For the majority of the misclassified subjects, the results for the OCSVM and the binary classification approach were congruent.The point plots in For the discussion of validity/plausibility of the found effects, as well as the proposed pathology-independent classifier, five aspects were addressed in the following: (a) classification performance of the pathology-independent and binary classification approaches, (b) previous research results and biomechanical characteristics in relation to classification performance, (c) LIME effects between the pathology-independent and binary classification approaches, (d) LIME effects in relation to the location of the spinal fusion, and (e) expert knowledge-based interpretation of the exemplary subject results.(a) Looking at the real data, the best performance was achieved using the pathology-independent classifier for the spinal fusion data (MCC = 0.57 ± 0.23). These results also matched the use of the binary classifier using the RF algorithm trained on both classes (MCC = 0.45 ± 0.25). OCSVM performed better using the spinal fusion data. Therefore, the current study indicated prevalent spinal differences between healthy subjects and subjects after spinal fusion that were suitable for classification. The classification performance of the subjects with osteoarthritis might have indicated potential spinal differences with a limited ability for classification. For the osteoarthritis data, the predicted and actual classes were only weakly correlated when using the stance data . The results indicated that subjects with osteoarthritis seemed to stand differently compared to healthy subjects. However, the differences seemed relatively small, which resulted in a low discriminative power of the features. An interpretation of the results should, therefore, be carried out with caution. For the back pain data, no good model solution was found for either approach .The BSS as a probability scoring metric also quantified the poor distinguishability of subjects with back pain from healthy subjects. Due to the poor results for both the pathology-independent, as well as the binary classifier, it can be ruled out that the proposed approach alone was the cause of the poor results. This was in line with a previous work that demonstrated that even the use of different complex classifiers and metric learning approaches is not able to lead to a significant improvement in the classification performance for subjects with back pain .For the synthetic data, the binary classification approach seemed to show superior performance when the class separation was reduced. This may highlight the importance of highly discriminant data for optimal performance of the pathology-independent approach. Surprisingly, the OCSVM approach showed a lower performance on S1 compared to S2, which was contrary to this statement. It cannot be fully clarified at this point whether this was a random event and, consequently, if further research is necessary.There were diverging indications that, on the one hand, suggested no clear, systematic performance difference and, on the other hand, that binary classifiers performed better when the class separation was low. This study was intended as an initial investigation, but in order to clarify the mentioned aspects, further research is necessary. However, overall, the results seemed relatively congruent for the OCSVM, as well as for the binary RF approach.(b) It can be assumed that non-specific back pain and osteoarthritis affect the spine more in terms of dynamic function than in terms of posture. Therefore, these pathologies are less detectable in static measurements. Back pain often originates in the muscular system . Due to (c) The following discussion of the LIME effects focuses on the results for the spinal fusion data due to the best classification performance. The reason is that, on the one hand, local LIME effects may indicate characteristics of a healthy or pathologic subject for the respective vertebras and, on the other hand, it is also possible that the model learned a wrong relationship between the feature value and the class membership. For the identification of subject-specific characteristics, it is, therefore, important to reduce model mistakes.Looking at the LIME values of the OCSVM and the binary RF approaches with the highest effects, showed that partly congruent results were present for the features with the highest effects for both approaches. However, there were also diverging results; for example, the pelvis more often had a higher relevance for the OCSVM. In general, only a little research addresses the agreement of XAI results for different classifiers, different XAI approaches , and difRegarding the partly different/instable XAI results for both approaches, the aggregation of the results of different XAI approaches and different models on the same data might be an interesting approach to increase the robustness of XAI interpretations. In the context of feature selection, the ensemble method has already proven useful in generating more robust results ,54. The Looking at the LIME effects, the boxplots showed little variation of the LIME values between the 10 measurements of each subject most of the time. Therefore, in general, the same effects were identified for the measurements of each subject, which also speaks for the quality of the interpretation. These results can also be justified by the fact that previous works showed a high accordance between multiple static and dynamic measurements of the same subjects .(d) The LIME values did not always show high effects for the vertebras related to the direct location of the spinal fusion. A possible reason for this might be that spinal fusion causes a higher pathologic deviation in other regions of the spine. This could also be explained by the connection instability that often develops after surgery ,57. Howe(e) Looking at the exemplarily results for the correctly classified subject 3459598 showed that the LIME values with the highest effect  indicated an effect toward an outlier. The effects were similar for both approaches. In line with the above-mentioned aspects, the features with the highest effects did not directly map the vertebras where the spinal fusion was located (T10–L2). For the misclassified subject 8232865, the LIME values  indicated an effect for the class of healthy subjects. However, the subject belonged to the class of spinal fusion. The misclassified subject showed very different patterns compared to the other subjects of the spinal fusion group. In addition to the still obvious vertebral body position data deviating from physiological posture, the very long fusion  also suggested that subject 8232865 suffered from severe scoliosis. In general, the mean values for the flexion extensions T3, T4, and T5 of the group of spinal fusion were above the mean values for the healthy. For the misclassified sample, this was the other way around. This might be the reason why the binary classifier failed to correctly classify the subject, because it learned an oversimplified relationship of the feature values to the class membership. The training data for the respective pathology should, therefore, be expanded, especially for subjects that show similar characteristics to the misclassified subject, so that the model is able to map the respective characteristics during the training phase. Further feature engineering through an automated feature extraction  or the iOverall, the results highlighted the usefulness of the proposed XAI approach for explaining the predictions of the pathology-independent classifier. With the proposed XAI approach, it was possible to understand why subjects were classified (including why they were misclassified) and to reduce the black box character of the machine learning model. Therefore, the current study formed an important step for making OCSVM classifiers more applicable in clinical contexts.Common inference-based statistical analysis methods often aim to find global effects between different groups of subjects. However, global interpretations are misleading in the case of highly individual subject characteristics, which are potentially relevant for class memberships. For spinal data, a previous work showed that highly individual patterns are present, which even enables the recognition of subjects . For exaOverall, the present results showed a high potential for the proposed pathology-independent classifier and no clear superiority of commonly used binary classification approaches. Limitations are to be mentioned in connection with the validity of the measurements while standing. Although a meta-analysis was able to confirm an overall reliable and valid measurement method for the assessment of spinal posture, this was particularly the case for global parameters such as thoracic kyphosis or lumbar lordosis. Pelvic parameters, such as obliquity or torsion, were less reliable and showed higher reliability in scoliosis patients than in healthy individuals, but one reason for this may be the smaller sample size . HoweverThe sample of healthy subjects for training the pathology-independent classifier was relatively small. The discovered differences could, therefore, also be due to the sample and not due to actual differences in the subjects with pathologies. An expansion of the sample is, therefore, necessary for future studies. There were limitations regarding the sample of subjects and the large age difference between the groups. At this point, it cannot be excluded that the corresponding effects influenced the classification task. In following studies, the analysis should be repeated with matched groups of subjects. Further, the present study evaluated the posture while standing. Other positions , as demonstrated in , may infInteracting features might influence the dependency between the feature and, thus, the LIME values. Hence, possible interactions should be considered and analyzed in future works. For different contexts, different interpretation levels can be provided through selecting a maximum number of features to be displayed by LIME. However, there is no ground truth for the evaluation of the interpretation results. It is, therefore, difficult to evaluate if they were meaningful and appropriately mapped posture characteristics. Furthermore, according to the current state of research, there were no objective criteria to evaluate interpretability . For theThe present study used static spinal data for the application of the methods. However, in the context of biologic gender classification, for example, dynamic data have proven superior for classification compared to the use of static data . FurtherAs a possible field of application, except of the use in the clinical analysis of spinal data, the proposed approach might also be interesting in the field of ergonomics. Trunk posture classifiers are an emerging application for estimating spinal loads during manual lifting tasks that may lead to low back pain . Many ofThe results suggested the potential suitability of the proposed pathology-independent approach. In the present study, no clear superiority of the commonly used binary classifiers compared to the proposed approach could be demonstrated. Static data did not seem to be optimally discriminant for mapping differences between the currently studied patient groups  and the healthy subjects. As a next step, spinal movement data should be used for classification to check if the dynamic data better map group differences and lead to increased classification performance. Moreover, metric learning approaches should be evaluated.The proposed pathology-independent data-driven approach could be helpful for providing clinicians and therapists an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively. Overall, the approach might be beneficial for finding and addressing individual spinal characteristics. In the context of personalized medicine, the most relevant characteristics for each subject classification might be useful as an objective orientation and for an individual adaptation of therapy measures.


Preventing network intrusion is the essential requirement of network security. In recent years, people have conducted a lot of research on network intrusion detection systems. However, with the increasing number of advanced threat attacks, traditional intrusion detection mechanisms have defects and it is still indispensable to design a powerful intrusion detection system. This paper researches the NSL-KDD data set and analyzes the latest developments and existing problems in the field of intrusion detection technology. For unbalanced distribution and feature redundancy of the data set used for training, some training samples are under-sampling and feature selection processing. To improve the detection effect, a Deep Stacking Network model is proposed, which combines the classification results of multiple basic classifiers to improve the classification accuracy. In the experiment, we screened and compared the performance of various mainstream classifiers and found that the four models of the decision tree, k-nearest neighbors, deep neural network and random forests have outstanding detection performance and meet the needs of different classification effects. Among them, the classification accuracy of the decision tree reaches 86.1%. The classification effect of the Deeping Stacking Network, a fusion model composed of four classifiers, has been further improved and the accuracy reaches 86.8%. Compared with the intrusion detection system of other research papers, the proposed model effectively improves the detection performance and has made significant improvements in network intrusion detection. With the development of the Internet of Things (IoT), device embedding and connection have generated more and more network data traffic . The incHowever, according to the ‘no free lunch’ theorem, we cannot find the best algorithm . Each alAn ensemble learning system DSN is proposed, consisting of the decision tree, k-nearest neighbor, random forest and deep neural network. DSN improves the accuracy of intrusion detection technology and provides a new research direction for intrusion detection.The proposed DSN combines the predictions of multiple basic classifier models, fusing decision information and improving the generalization and robustness of the detection model.We use a real NSL-KDD dataset to evaluate our proposed system. The experimental results show that DSN has better performance than traditional methods and most current algorithms. We consider that the proposed system has good application prospects for IDS.This paper proposes a deep stacking network model. This model belongs to an ensemble learning model, which integrates the advantages of various machine learning algorithms and improves the detection rate of various attack categories in network intrusion detection. The main contributions of this paper are summarized as follows:The rest of the paper organizes as follows: In recent years, many scholars have tried to use machine learning algorithms to study new intrusion detection methods . SeveralDeep learning has achieved many successes in speech detection, image recognition, data analysis and other fields, becoming the preferred solution to many problems. Many scholars have also begun to use deep learning to solve intrusion detection problems. Wu et al.  designedAlthough machine learning and deep learning have certain advantages in intrusion detection, the disadvantages are also obvious. A single algorithm tends to have a high detection rate for certain attack categories while ignoring the detection effect of other attack categories. In order to solve this problem, many scholars try to use the idea of integrated learning to solve the problem of intrusion detection. Rahman et al.  proposedThe development of the above-mentioned intrusion detection technologies is encouraging, but these classification technologies still have detection deficiencies, such as being insensitive to unknown attacks and a low detection rate when detecting a few attacks. In order to overcome these problems, this paper uses preprocessing technology to deal with the dataset and selects the basic classifier of ensemble learning selected to construct the ensemble learning model DSN. Finally, the system DSN solves the above-mentioned problems by learning the advantages of each classifier.The famous public KDDCUP’ 99 is the most widely used data set for the intrusion detection system . HoweverMany scholars have carried out a series of studies on NSL-KDD and analysis shows that the NSL-KDD data set is suitable for evaluating different intrusion detection models . TherefoDecision tree (DT) is a commonly used machine learning method to complete classification and regression tasks. The decision tree model has a tree structure, starting from the root node and branching using the essential features of the data. Each branch represents the output of a feature and each child node represents a category. The classification decision tree is a kind of supervised learning and the required classification model can be obtained by giving sample training. The input data finally completes the classification task through the judgment of each node. According to the criteria for judging branch characteristics, decision trees can be divided into ID3, C4.5 and CART. ID3 uses a greedy strategy and uses information gain based on information entropy as a branch criterion.In the classification problem, take a data set Choose feature The greater the information entropy, the higher the uncertainty of the sample set. The essence of the classification learning process is the reduction of sample uncertainty . The greater the change of the information gain, the better the classification effect of the feature on the sample set. Therefore, the feature split with the largest information gain should be selected.Deep neural network (DNN) is a deep learning algorithm widely recognized by scholars. In a multi-class network, ReLU is usually used as an activation function, the formula is as follows:The loss function measures the output loss of training samples and calculates the back propagation of the network through the loss function to optimize the network structure. In the classification task, we usually choose cross-entropy as the loss function, the formula is as follows:Individual machine learning algorithms usually have shortcomings and cannot complete complex task requirements. Therefore, we try to combine many different machine learning algorithms to form a learning system. We call this learning system ensemble learning and the algorithms that make up the learning system are called individual learners. Ensemble learning can be divided into two categories. One type is serialization methods that have strong dependencies between individual learners and must be generated serially, such as boosting and AdaBoost. The other is parallelization methods that can be generated at the same time without strong dependencies on individual learners, such as bagging and random forest (RF).Stacking is a combination strategy that combines the calculation results of individual learners. Wolpert  put forwDSN usually consists of two modules. The first module is the classifier module, composed of classifiers with different classification performances and performs preliminary prediction processing on the input data. ‘Stacking’ refers to concatenating all output predictions with the original input vector to form a new input vector for the next module. The second module is the prediction fusion module. By training the new combined input data obtained from the previous layer, a new network can be obtained. The network can effectively use the output data obtained from the previous layer for further processing. The prediction result output by the network is more accurate and closer to the true value.Input the original NSL-KDD training data set. The pre-processing module discretizes the string information in the data set, filters important feature selection, handles imbalanced data and normalizes the data.Use 10-fold cross-validation to divide the pre-processed dataset and then input the data into various classifiers for training.After using training data to conduct cross-validation training for all algorithms, select algorithms with better detection accuracy and operational performance as the basic classifier. Then discretize the classification results.Input the predicted classification result of the training set and the original category as the training set, initialize the parameter weights of the neural network, train the network parameters and finally generate the Deep Stacking Network model.Input the NSL-KDD testing data set. The pre-processing module discretizes the character information in the data set, selects essential features and normalizes the data.Use the trained basic classifier to initially predict the classification results and discretize the results.Input the preliminary predicted classification results into the trained neural network to obtain the prediction results of the Deep Stacking Network model.In this paper, a deep stacking network model is designed which selects commonly used machine learning algorithms, such as support vector machines (SVM), decision trees, random forests, k-nearest neighbors (KNN), AdaBoost, deep neural networks (DNN), etc., as the basic classifiers. Through comparative testing, we select four machine learning methods as the basic classifiers. Through data preprocessing and deep neural network tuning, the best detection effect is finally obtained. Data pre-processing is a necessary step for data analysis, and it is also an essential part of an intrusion detection system. The preprocessing stage mainly includes four units: one-hot encoding, feature selection, data standardization, imbalance handling and normalization.protocol_type has a total of three attributes: tcp, udp and icmp. By one-hot-encoding, tcp is encoded into , udp is encoded into  and icmp is encoded into . Overall, the three character type features protocol_type, service, flag are mapped into 84-dimensional binary values. Otherwise, the num_outbound_cmds feature value is 0, so this feature is removed. Therefore, the original 41-dimensional NSL-KDD can be transformed into a new 121-dimensional data set.There are 41 features in the NSL-KDD dataset, including 3 string features and 38 continuous value features. In machine learning, character type information cannot be used directly and needs the encoding methods to convert it. One-hot-encoding is one of the most commonly used methods to deal with the numeralization of categorical features . It convFeature selection (FS) is a commonly used method of data aggregation. In some ways, we can select important features and remove the remaining redundant features to alleviate the problem of dimensionality disaster. Similarly, removing irrelevant features can reduce the difficulty of machine learning tasks and increase the efficiency of storage space utilization. In some machine learning algorithms, FS can help the algorithm improve detection performance, especially decision tree algorithms .It can be clearly seen from the table that the training samples are imbalanced on the NSL-KDD data set. Unbalanced training samples will cause the trained model to be biased to recognize most sample categories, resulting in the degradation of the model’s detection performance. Therefore, we choose to process the training samples. Commonly used methods for processing unbalanced data sets include under-sampling and over-sampling. The model in this paper uses undersampling to process the training samples of the data set. From a security perspective, the intrusion detection system should identify the attack type as much as possible and can appropriately reduce the normal traffic data in the training sample when inputting the training sample, so that the focus of model training is to identify the attack type. We use the non-replacement method to sample the normal flow data randomly. Different dimensions of input data usually have different dimensions and orders of magnitude. When using machine learning, data normalization is a very necessary measure. The transformed NSL-KDD has 121-dimensional features and there are big differences between the features, so we use data normalization to reduce the differences for improved performance . In thisThe min-max normalization scales the data to the interval  through a linear transformation. The formula is as follows:The classifier module reads the preprocessed data and uses the ten-fold cross-validation method to process the training data. In the 10-fold cross-validation method, the entire training set is randomly divided into 10 folds, of which 9 folds work as sub-training data and the remaining 1 fold works as sub-validation data. The read data is used for model training. We first choose KNN, RF, SVM, DT, LR, DNN to process the data. Two standardization methods are mentioned in src_bytes is the root node of the decision tree and the decision tree model is shown in The classification decision tree in this article uses the ID3 algorithm as the way to build the tree model. First, perform feature selection, reducing the number of features of the input data from 121 to 56. Then use the data after feature selection to input decision tree training. Each time the feature with the largest information gain is selected as the bifurcation node, each child node connects two branches to build a binary decision tree. In the decision tree, choosing the bifurcation point is the key to affecting the classification performance of the decision tree. Take the 4-layer decision tree trained on NSL-KDD data as an example, where The settings of different DNN network structures usually affect the results of training, so this paper designs a DNN network structure with five hidden layers. According to the proportion of the number of samples in the training sample, we reset the proportion of each category class_weight = {1:2:3:4:5} to increase the effect of small samples in network training, thereby improving the overall detection of the DNN network Effect. Finally, use the softmax function to output the final category prediction results. The Deep Stacking Network (DSN) is divided into two layers. In the first layer, the classifier module, each classifier has 10 different model parameter structures based on 10-fold cross-validation. Each model performs a result prediction on its corresponding verification set and can get the prediction results of 10 verification sets. The set of 10 validation sets corresponds to a complete training set. We superimposed the prediction results of the 10 validation sets to obtain the prediction results of a complete training set. This prediction result on the training set can help us evaluate the performance of the classifier and use it as the new training set input to the next layer. At the same time, each model inputs the data that need to be predicted and the mode of the predicted values of these 10 models are taken as the prediction result of the classifier, which is used as the new test set to input by the next layer. In the algorithm proposed in this paper, 4 classifiers are selected as the basic classifiers of Deep Stacking Network. Each classifier corresponds to a new training set and a new test set. Then use one-hot encoding to convert the prediction results from character variables to discrete variables and the prediction results from 1-dimensional features to 5-dimensional features. For example, the prediction result is that Probe becomes . Therefore, a total of 20-dimensional training set features and 20-dimensional test set features can be obtained. Combining the original classification label of the training set with the features of the new training set is the input of the new training set. The features in this training set can also be called appearance features. These appearance features can help us train to get the influence relationship of each basic classifier in the network. In the second layer, the prediction fusion module, we use a simple neural network for decision fusion. Since decision fusion does not need to explore the deep relationship between features and labels, we use a hidden layer of neural network for model training. The neural network structure is shown in In this article, nine indicators commonly are used in intrusion detection to evaluate the performance of the intrusion detection system, including four confusion matrix indicators of true positive (TP), true negative (TN), false positive (FP), false negative (FN) and five evaluation indicators of accuracy (ACC), precision, recall rate, F1-score, multi-class accuracy (MACC). True Positive (TP): Attack records that are correctly detected as attack ones.False Positive (FP): Normal records that are incorrectly detected as attack ones.True Negative (TN): Normal records that are correctly detected as normal ones.False Negative (FN): Attack records that are incorrectly detected as normal ones.The four confusion matrix indicators are defined as follows:The six evaluation indicators are defined as follows:The ACC is usually an indicator of traditional binary classification tasks. According to the standard of multi-attack classification, multi-class accuracy (MACC) is proposed, which can help us better compare the performance of classifiers.The proposed system is performed by a laboratory computer with Intel(R) Core(TM) i7-9750H CPU@ 2.60 GHz and 16.00 GB of RAM using Python on system Windows 10. All experiments are performed on the preprocessed NSL-KDD dataset. Firstly, select the appropriate basic classifier by screening the appropriate machine learning algorithm. After selecting the basic classifier, we conduct experiments on the complete system to evaluate the performance of the model.When the learning rate of the network is too high, the loss function of networks will oscillate without convergence. If the learning rate is too low, the slow convergence rate will hinder the update of networks. Therefore, choosing an appropriate learning rate is very important for network performance optimization. In this experiment, a set of learning rates  is selected as the candidate parameters of the two networks and the accuracy of the network on the verification set is used as the measurement standard. Similarly, the number of iterations is also critical to the optimization of the network. A large number of epochs will cause the network to waste time cost and it is easy to cause the network to overfit. The small number of epochs will result in insufficient network convergence and poor model learning performance. This experiment finds the appropriate number of iterations from the changing law of the loss function value during network training.In order to find the right parameters, we use the 10-fold cross-validation method mentioned in In the feature selection of DT, try to select a different number of features to test the classification effect. As shown in the In order to establish a good ensemble learning model, it is first necessary to screen the basic classifiers with excellent performance. In the experiment, a 10-fold cross-validation method was used to evaluate the performance of the six selected algorithms. We consider the effect of the algorithm from the perspective of the predicted success rate of each attack type so that the characteristics of each classifier can be analyzed, which will help us choose a good basic classifier to improve the performance of the entire intrusion detection system. From the table, it is appreciated that three algorithms of KNN, DT and RF have outstanding performance in detection accuracy. Among them, RF has the best performance in detecting Normal categories, DT has the best performance in detecting Probe and R2L categories and DNN has the best performance in detecting DoS and U2R categories. In terms of time spent, DT used the shortest time and SVM used the longest due to slow modeling. Stacked generalization requires us to choose the classifiers to be good and different, so we choose KNN, DT, RF and DNN that have outstanding performance in various aspects as the basic classifier of the DSN network.In order to better demonstrate the performance of this system in intrusion detection, we will compare the proposed model with the intrusion detection algorithms proposed by seven scholars, including DNN, RNN, Ensemble Voting and SAAE-DNN. This paper proposes a novel intrusion detection approach called DSN that integrates the advantage of four machine learning methods. For the real network dataset NSL-KDD, we use Pre-processing to normalize data. In the experiment, four of the six machine learning methods were selected as the basic classifiers for ensemble learning. The integrated learning model DSN gathers the advantages of four different classifiers and improves the performance of the algorithm. Compared with other researches, it is proved that our ensemble model effectively improves the detection accuracy. The DSN proposed in this article has a good application prospect, which is worthy of further exploration.The data used in the experiment is NSL-KDD, which is an unbalanced data set. Therefore, the use of this data set for training will inevitably lead to the learning result biased towards the majority of samples. How to use limited training samples to improve the adaptability of multi-classification is the key to solving the problem. Ensemble learning is an excellent method that can improve the performance of the model in a short time. However, it is not advisable to use ensemble learning methods blindly. Different algorithms are suitable for different classification situations. It is necessary to select the correct algorithm as the basic classifier to fundamentally improve the overall effect of the model. From the condition of model optimization, the most important thing is to optimize the data, followed by the optimization algorithm and finally, the parameters of the optimization algorithm.Future work will focus on improving IDS performance. I consider the detection cost of each algorithm for different attack categories as a measurement standard and design a new IDS. At the same time, I will choose other intrusion detection data sets collected from reality to experiment with the intrusion detection performance of the algorithm. Designing an intrusion detection system capable of parallel processing and learning is our next step of work.


Background: Myositis ossificans is an uncommon complication of trauma and surgery, defined as ossifying changes in a non-osseous tissue such as muscles. It happens after tissue injury, with or without fractures. When myositis ossificans occurs around a joint, it can cause ankylosis, leading to complete dysfunction of the joint. Though it has been described in most parts of the body, bridging myositis ossificans involving the elbow joint were scarcely reported.Case Presentation: We report a severe case of myositis ossificans after a supracondylar humerus fracture in a 9-year-old child. In this case a palpable painless mass appeared following the fracture and surgical trauma. Ultrasound or X-ray is of significant diagnostic value. The brachialis was completely ossified and formed a bony bridge around the elbow, causing complete ankylosis. The bone mass was surgically removed through a bilateral less-invasive approach with less surgical trauma 9 months after initial presentation. we applied bone wax to the fresh bone wounds to prevent the formation of hematocele. Indomethacin, a non-steroidal anti-inflammatory drug, was administered after the operation to suppress bone proliferation in our case. Our patient had the best possible functional status and no recurrence at 2 years' follow-up.Conclusion: Elbow myositis ossificans in children may mainly affects the brachialis. A bilateral less-invasive approach is sufficient to remove the bone mass with less surgical trauma. This case also provides a new reference for the treatment of myositis ossificans after the elbow injuries. Myositis ossificans is characterized by heterotopic calcification and ossification of muscular tissue that mainly affects teenagers, but it has also been described in younger children. The disease is often limited to a single muscle and can occur throughout the body. It is associated with multiple etiologies, such as injury, genetic pre-disposition, post-infection, or undetermined causes, whereas injury is an important factor in its pathogenesis . The maiThis study was approved by the Research and Ethics Committee of our institution, and written informed consent was obtained from the patient's family.Figure 4A).A 9-year-old child presented to the rural hospital complaining of left elbow pain after a fall. X-ray showed supracondylar fracture of the humerus classified as Gartland type III . An openThe patient was then transferred to a territory pediatric orthopedic center 9 months after the primary injury. Laboratory tests, magnetic resonance imaging (MRI)  and compThen a 5-cm longitudinal incision at the antero-medial part of the forearm was made. Develop the incision through the aponeurosis of the biceps. Expose the volar side of the proximal ulna through the interval between the pronator teres and the neurovascular bundle (the median nerve and the ulnar artery). The ulnar end of the bony bridge was shown and stripped from the ulna with osteotome. By this way the bone bridge was completely dissected from the bases and extracted from the humeral incision after thorough blunt dissection of the middle part . The masMyositis ossificans is an uncommon complication of upper limb trauma . Its patMyositis ossificans after supracondylar humerus fracture were rare . To the Another case was reported by Kanthimathi B et al. . A 13-yeThe patient in our case received open reduction and crossed pin fixation, while the two cases in literature were treated with external fixation only. Our case was initially treated by the orthopedic surgeon for adult with unprofessional rehabilitation in fear of elbow stiffness. On the contrary, this might significantly increase the risk of developing myositis ossificans . AlthougOur intraoperative findings confirmed the involvement of the entire brachialis. The brachialis arises from the anterior inferior part of the humerus and inserts at the ulnar tuberosity and coronal process, which was exactly the starting and ending point of the bone bridge in our case. The brachial artery travels anterolaterally to the brachialis. The ossified tissue in other two cases had similar shape, so it can be inferred that the site of ossifying myositis is also the brachialis. As inferred from our case, the distal fragment normally displaces posteriorly in relation to the proximal fragment in humeral supracondylar fractures (the extension type), so that the brachialis would easily be injured by the sharp end of the proximal fragment. This injury pre-disposes the brachialis to ectopic ossification. Besides, the brachialis might be extensively stretched during rehabilitation, adding to the risk of further injury. Therefore, we suggest that anterior myositis ossificans of the elbow with brachial involvement may be a common form of myositis ossificans in children.Based on the anatomy of brachialis and the location of the ectopic bone mass, we proposed a modified bilaterally co-approach for the removal of the bone bridge. As the middle part of the mass was not connected with the humerus or ulna, the starting and ending points of the bone bridge can be determined according to the pre-operative and intraoperative imaging examination. A longitudinal incision at both ends of the mass was made to expose the ends of the mass, and the bone bridge was dissected at the base of the conjunctions with the humerus and the ulna. After careful blunt dissection of the entire bone tissue next to the bundle of artery and the median nerve, it can be extracted from the humeral incision freely . This moIn conclusion, we reported a rare case of myositis ossificans after supracondylar fracture of humerus. For these similar three cases, myositis ossificans occurs 3 weeks−3 months after injury. It could occur after conservative fixation with a simple plaster cast or even after open reduction. By comparing with similar cases in literature, we confirmed that elbow myositis ossificans in children mainly affected the brachialis. Based on the anatomic feature of the brachialis, we designed a bilateral less-invasive approach to remove the bone mass, aiming at reducing surgical trauma. The efficacy of intraoperative bone wax and post-operative NSAIDs need further evaluation.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by the Research and Ethics Committee of China Medical University. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. Written informed consent was obtained from the individual(s), and minor(s)' legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.EW, TL, QL, and JC participated in diagnosing and treating the patient and acquisition of data. JC and QL wrote the case report including performing the literature review. EW approved the final version of the manuscript. All authors participated in every revision and improvement of the manuscript and read and approved the final manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.