This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents. A permanent magnet synchronous motor (PMSM) is a type of synchronous machine with symmetrical three-phase stator windings in which the conventional rotor field windings are replaced with special-shaped rare-earth permanent magnets . PMSMs aWhile there are several sources that can cause the failure of electrical machines, the failures themselves can be classified into two main categories of internal or external depending on the location of the fault source. The causes of internal source failures are manufacturing errors and material deterioration, whereas external faults are caused by interaction with the operational environment, power supply, and load. The internal and external source failures can be roughly split into subcategories such as mechanical, electrical, or environmental, and then into more specific faults types such as rotor strikes, dielectric failure, eccentricity, unbalanced voltage, and poor mounting . The diaSince PMSMs operate in various complex systems, their failure can lead to degradation of manufacturing quality and extensive damage of the plant, causing significant financial loss and danger to the life and health of the operating personnel. A 45% occurrence of bearing faults in the electric motor compels us to admit the importance of electric motor bearing fault diagnosis study; therefore, the focus of this research work is on electric motor bearing fault diagnosis.During the history of the development of bearing fault diagnosis techniques, to avoid dangerous accidents related to electric motor faulty operation, breakdown maintenance techniques were replaced with time-based preventive maintenance approaches, which were performed according to working time periods regardless of the true status of the machine. However, nowadays, the application of time-based preventive techniques for modern complex systems is expensive since, to perform the maintenance, the electric motor or even the whole system it is operated in should be stopped; and the expensive system which came up against the maintenance deadline may still have had a significant remaining useful lifetime, but without regard to that, the maintenance would be performed. Therefore, to reduce the cost by lessening the amount of unnecessary scheduled preventive maintenance operations, non-invasive condition-based maintenance approaches are currently considered more efficient; thus, condition monitoring (CM) and precise fault detection and diagnosis systems have become essential for the industry . Bearing fault diagnosis using the condition monitoring approach can be successfully solved by either model-based or data-driven techniques. Model-based techniques rely on an accurate model of a system, the development of which is based on a fundamental understanding of the physics behind the process and is expressed in terms of mathematical functional relationships between the inputs and the outputs of the system ,10,11. UThe bearing arrangement inside the electric motor makes it possible to use different types of source data for fault diagnosis. For instance, Motor Current Signature Analysis (MCSA), traditionally used for detecting and diagnosing electrical types of faults such as stator winding faults, broken rotor bar faults, rotor asymmetry, and abnormal air-gap eccentricity, is now applied for the diagnosis of bearing faults in many works ,15,16,17In recent times, data-based bearing fault diagnosis with applications of Machine Learning (ML) has been rejuvenated with a multitude of research. The analysis of those works shows that generally, the bearing condition monitoring approaches consist of two main steps: (1) fault feature extraction and selection and (2) fault classification . FeatureThe features of signals extracted using the methods described above are used for the classification of system state. The classification is generally performed by ML techniques. The state-of-the-art supervised ML approaches have gained strong popularity in fault diagnosis at present. In recent times, Deep Learning (DL) techniques have gained in popularity too. As state-of-the-art methods, supervised DL techniques are used for bearing fault diagnosis are Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks ,33,34,35In the research by Rauber et al. , the autHowever, there is another issue that arises before the feature extraction and selection step. The problem of which portion of the time-based signal that should be analyzed for reliable fault diagnosis is pointed out in research work by Kang et al. . In thatIn this paper, the broadened GMM-window-based signal processing approach is proposed to avoid hidden misclassifications that arise from the presence of discriminant information in the data, which is not necessarily related to the actual bearing fault state. Here, for every type of bearing fault, GMM-windows are set for the first ten fault frequency harmonics to reduce the negative effect of possible interference and masking effect inside the selected frequency bands and to suppress the high frequency components. This method allows for extracting reliable features from the selected frequency bands and performs classification with higher accuracy and lighter utilization of computational resources. The rest of the paper is organized as follows. The nature of bearing faults and bearing fault frequencies are explained in the remaining Introduction section. Then, the experimental testbed, data collection process, and dataset used in this paper are described in Electric motors have two sets of bearings at either end of the rotor to support the rotating shaft. These bearings are needed to reduce friction and to enable the rotor to rotate smoothly. Bearings consist of four basic working parts: the outer race, inner race, rolling elements, and separator . The main factors causing damage to bearings are excessive load, improper lubrication, material fatigue, lubrication contamination, corrosion, and incorrect installation. Bearing damage is visually represented by inner or outer race indentations, spalls, discoloration, or abnormal ball wear path as presented in shS is a shaft speed expressed in revolutions per minute (RPM), bd is the diameter of the rolling element, and pD states for the pitch diameter, bN is the number of rolling elements, and theta is the angle of the load from the radial plane.During the operation of a bearing with a race fault, rolling elements pass the damaged areas on the race surface. This process generates periodical impulses with a certain rate known as the fundamental defect frequency. These four fault frequencies can be distinguished as the ball spin frequency (BSF), fundamental train frequency (FTF), bearing ball-pass frequency of the outer race (BFPO), and bearing ball-pass frequency of the inner race (BPDI). These characteristic frequencies depend on the geometric parameters of the bearing such as the diameter of the rolling element, the cage diameter, and the pitch diameter, as well as on the number of rolling elements and the angular velocity of the shaft. Equations (1)–(4) for bearing characteristic frequency calculations are given below ,39:(1)BSThere are four stages of bearing fault evolution. Every stage is defined by a frequency range and contents. In In Stage 1, invisible subsurface microcracks are present at ultrasonic frequencies in range D from 20 kHz to 350 kHz. The bearing should not be replaced at this moment. As the wear progresses, the bearing defects start to ring the natural frequency of the bearing components. As a result, in this stage spectral contents start to appear at 500–2000 Hz frequency range C along with higher frequency information in range D. In stage 3, bearing defects become visible. At this stage, bearing fundamental defect frequencies start to appear in range B accompanied by well-formed sidebands and then it is possible to diagnose the damaged bearing component. At stage 4, mainly rotor-related frequencies are present in range A. The bearing damages come to a point where they cause increased rotor vibration. Due to this bearing defect, frequencies decrease in amplitude and more random broadband vibration develops. High frequencies in the range D may grow excessively just prior to bearing collapse. At this stage, the bearing should be immediately replaced . In this work, the dataset was obtained from the KAt-DataCenter of the Chair of Design and Drive Technology, Paderborn University, Germany . VibratiFor the data acquisition experiment in , the autFaults of different types with various severity levels were inflicted at the same bearing parts aiming to guarantee the higher robustness of the fault diagnosis methods developed using this dataset. To describe the physiognomy of bearing faults with different parameters, the authors of the dataset introduced a method for general categorization and detailed fault specification partially based on the methodology presented in ISO15243. The method consists of four blocks of information concerning general bearing information, manufacturer-specific information, application-specific information, and information about the damage.Additionally, the data were collected for 14 bearings with faults obtained by performing accelerated lifetime tests . Totally, 32 different bearings were used to create the dataset. Besides inflicting faults of the same type with different levels of severity, another way to ensure the robustness of condition monitoring is to analyze its dependence on test rig operation parameters. For that purpose, the test rig was operated under four different conditions with three varying parameters: rotational speed, load torque, and radial force. Specific parameter values for each of the four operating conditions are presented in In this work, out of 32 time-series signals available, 18 were used to construct the final dataset. These bearing fault vibration data were collected from artificially damaged bearings. Artificial damages here are represented by single point damages that do not have repetitions and are not combined with any other type of fault. For experimental purposes, the bearing data were arranged in three groups in the following way: six healthy bearings, seven damaged bearings with outer ring faults, and five damaged bearings with inner ring faults labeled as 1, 2, and 3, respectively, as shown in For vibration data acquisition, a piezoelectric accelerometer  and a charge amplifier with a 30 kHz low-pass filter were used. The acceleration was measured at the adapter at the top end of the testbed bearing module, then the signal was digitized and saved with a 64 kHz sampling rate. Each 4 second-length signal provided in the dataset was cut into 1-second pieces, and thus, the complete dataset used for this research is an array with dimensions 5760 × 64,000. Time-domain plots of raw vibration signals for a healthy state, inner ring, and outer ring faults are presented in FFT is a common technique for converting the time domain contents of bearing vibration signal into the frequency domain for spectrum assessment. The overview of the proposed methodology is depicted in As shown in t is the time, x(τ) is an input signal sample at τ,t) is a sample of the transformed signal at time t, for discrete from x(n) is the bearing vibration signal. Hilbert transform is applied to the bearing vibration signal to calculate the 90-degree phase-shifted signal as follows in the continuous and discrete form:z(t) is analytical signal, input signal x(t) and Hilbert-transformed signal it) are real and imaginary components, respectively.Then, the analytical signal is derived as a complex number: The envelope signal It is important to notice that for further analysis, it is often advisable to use the spectra of the squared envelope signal rather than the envelope signal itself. The reason behind it arises from the comparison of spectra of squared signal with the spectra of rectified signal: mathematically, the envelope of a signal is the square root of the squared envelope and the rectified signal is as well the square root of the squared signal. However, application of the square root operator results in extraneous components emerging that are not inherent in the original squared signal. These high harmonic components extend to infinity and appear because of the presence of sharp cusps in the rectified signal. Forasmuch as the whole operation is performed with digital calculations, it is impossible to eliminate the high harmonics using low pass filtration. Consequently, they generate the alias to the measurement range, which causes masking.The squared envelope spectra of a healthy bearing vibration signal, vibration signal of bearing with outer ring faults, and signal with inner ring faults are presented in As discussed in h is the number of harmonics requiring observation for fault diagnosis task, iP indicates the ith harmonic of the bearing defect frequency, freqN is the number of frequency bins surrounding each harmonic, and k is an index term of each frequency bin, β is a coefficient inversely proportional to the standard deviation of Gaussian random variables defined by Equation (10).For this case, Gaussian windows are used to select the defect frequency components that are useful for fault diagnosis in each specific application. The parameters of the Gaussian window are determined as follows:The Gaussian window generation procedure in this work consists of the following steps: first, the bearing fault characteristic frequencies for the outer and inner ring faults are calculated for each of the four operating conditions to be considered as the mean for the GMM window. The bearing fault components in the real data are found in the vibration squared envelope spectrum, relying on the results of computations with Equations (3) and (4) and placed in The second point is that the inner and outer ring fault frequency harmonics have different spectral shapes. The radial load influences the force of the impact caused by rolling over a defect; thus, since the outer ring is a stationary component, its defects are subject to the same force at each roll. However, the inner ring rotates at the speed of the shaft, and the defects on the inner ring are subject to varying force; therefore, each harmonic of bearing inner ring fault frequency is amplitude modulated by the rotating speed of the inner ring. This results in the sidebands around BPFI harmonics which are caused by the transitions of the inner ring defect into and out of the load area . Figure rangef = 1/4 BPFO, and the frequency range for the inner ring fault evaluation is chosen to be wider rangef = 1/2 BPFI. From this figure, it is evident that the envelope power spectrum around the bearing inner ring fault harmonics has a relatively less sharp bell shape due to the presence of the sidebands at both sides of the harmonic at RPM [Hz] distance, whereas the shape of the spectrum around the bearing outer ring fault harmonics is relatively sharp. Therefore, the frequency range for outer ring fault evaluation is chosen to be narrow The analysis of frequency spectra of vibration signals with different damage generation methods shows that bearing faults generated by EDM have higher amplitude than drilling faults or faults generated by electric engraver. When comparing the spectra of healthy and faulty signals, it is evident that the spectra of the Inner Ring and the Outer Ring faults generated by drilling and electric engraver contain the characteristic frequency harmonics that can be masked by the harmonics of bearing normal vibration signal; however, even though the drilling and electric engraver generated fault harmonics have lower amplitudes, the profile of the sidebands remains inherent and symptomatic for each bearing fault location. Therefore, to prevent possible misclassification of these concurrent harmonics and to achieve better isolation of informative frequency bands containing inherent sideband profiles, the lower and higher-order harmonics containing useful information are segregated using Gaussian windows. Therefore, 10 Gaussian windows are formed to select and explore the first 10 harmonics of the Inner Ring fault signal. The same segregation procedure is performed for the Outer Ring fault vibration signal. As a result of the analysis of several isolated frequency bands, it is possible to distinguish faulty conditions with low signal amplitude even though some of their harmonics can be masked by the healthy bearing vibration frequencies. resolutionf stands for frequency resolution, which in this work is 1 Hz. The coefficient is inversely proportional to the standard deviation of Gaussian random variables and is estimated as follows:wfreqN is the number of frequency bins that are considered as the defect components around the bearing fault characteristic frequency harmonics, and the value ε is a constant related to the convergence of Gaussian window which satisfies the condition 0 < ε < 1. Adjustment of the ε value allows to obtain the necessary window width and flatness according to the needs of the experiment. With the ε values close to 0, the window shape will approach a rectangular window. With the ε values close to 1, the window shape will approach an all-pass filter. In this study, the best-desired shape of the windows for fault harmonics selection was achieved with ε = 0.15.Eventually, the parameters of GMM-based windows are defined as follows:According to the methodology pipeline presented in There is a profuse number of features for dimensionality reduction of time-series data used in various research works in the bearing fault diagnosis field. To make the time-domain feature extraction possible from the signal obtained after the previous steps, the inverse Fourier transform is used to transform the signal from the frequency domain to the time domain. Thus, after the inverse Fourier transform is completed, the time-domain vibration signal envelope containing preselected bearing fault characteristic frequency bands is provided to the feature extraction step. The bearing fault diagnosis task usually utilizes real-life physical data such as vibration, acoustic emission, or current data. For successful fault diagnosis, a large dataset must be collected describing the state of the system under analysis with different types of faults and operating conditions. There are 96 minutes of signal data in the dataset used in this work and each one-second data sample consists of 64,000 variables (according to the sampling frequency). If raw signals were used for classification, an inordinately large amount of computational resources would be required; however, there would not be any guarantee of good classification results. The predicament of the high dimensionality of raw data is resolved utilizing feature extraction. Feature extraction converts data into a set of features called a feature vector, which is a compact informative representation of the data. In this work, 19 statistical features are extracted in total. Specifically, 16 of them are extracted from time-domain sequences and the remaining three are computed from the frequency domain. Those features are widely used in research works on bearing fault diagnosis. The names and equations for them are shown in After the feature extraction process is completed, it is necessary to evaluate the features and eliminate the ones that contain irrelevant or redundant information to prevent the reduction of performance of learning algorithms. This evaluation process is called feature selection. Feature selection aims to form a subset of relevant features with high predictive value, that will allow creating a learning model with the highest possible robustness . In this work, feature selection is implemented to speed up the learning and classification processes, avoid overfitting and to increase the accuracy of classifiers, as well as to improve the interpretability of the model by yielding a reasonable number of features that provide the best separation between different classes analyzed in this study. Therefore, feature selection is performed based on the maximum separation distance between different health states of the bearing. This feature selection method is an improved method from . The features are normalized between 0 and 1.f within class c as follows:x is the feature and n is the number of samples.The mean f and the mean of the same feature in each class.The mean squared Euclidean distance is computed between each feature data point The separation distance with the maximum feature separation is normalized to produce a performance evaluation criterion—Normalized Separation Distance (NSD).Using the notation The change of NSD leads to a different set of features being selected. In this work, for every value of NSD starting from zero and increasing by 0.05, a new set of features is obtained. Each set of features that corresponds with each NSD value is provided to Weighted KNN for classification. The classification accuracies are shown in The experiments were performed on five datasets: four datasets with different operating conditions (states 0–3), parameters of which are presented in It is evident that feature sets corresponding to the same NSD value do not guarantee improvement of the classification performance for each dataset. Therefore, the NSD value that corresponds to the feature set with the most stable performance on any part of data is chosen based on the following performance stability evaluation method.For each feature set corresponding to 0.05 iteration of NSD value shown in Here, the goal is to choose a NSD value which can provide a feature set that will have the accuracies with the highest mean and lowest variance for all datasets. Hence, means and variances for each examined iteration of NSD value are compared and ranked from one to six in such a way that means are ranked from highest to lowest and variances are ranked from lowest to highest, making the best NSD value have the least sum of rank numbers (performance score) and the worst NSD value the most sum of rank numbers. j ≥ 0.25; therefore, this NSD value that corresponds to feature set with the best stable performance for all datasets is chosen for feature selection.As can be seen from Weighted KNN is chosen as the machine learning classification algorithm due to its instance-based nature. For this reason, the authors consider it an appropriate tool for evaluating the results of frequency band selection and feature selection. The training set is given and denoted as:N:To start the assignment of a class for the new query Then, computed distances of the nearest neighbors are sorted in ascending order forming the set:k:Out of the set, the search for k-nearest neighbors of the query k:The weights of k nearest neighbors are calculated for i-th nearest neighbor among its k nearest neighbors. The Dirac delta function The classification result is computed based on majority weight voting:The selected features are provided as labeled data to the Weighted KNN classification algorithm, which is a nonprobabilistic classification algorithm of the nearest-neighbor type that weighs the evidence of a neighbor close to a new unclassified observation more heavily than the evidence of another neighbor located at a greater distance from the unclassified observation. Specifically, the algorithm uses the weighting function that depends on the distance between the sample and the considered neighbor in such a way that its value decreases with this increase in the distance ,47. The The example in In this work, a weighted KNN model was retrained for the number of neighbors  from three to 17 with a feature pool without feature selection. Then, the K-value was chosen based on which number of neighbors gave the least classification error rate for Weighted-KNN as is shown in k, respectively. K denotes the total number of signal classes in the dataset and N states for the total number of data samples in the experimental dataset. The metrics values computed when applying the proposed method to 4 datasets are shown in In this section, the proposed bearing fault diagnostic method is evaluated using the vibration data collected from the real testbed that is described in Performance comparison with state-of-the-art methods is performed according to the pipeline presented in The first approach (ENV) uses the whole vibration envelope squared signal frequency spectrum.The second approach (LP) eliminates bands with possible interconnections and hidden relationships in frequency bands C and D that are useful only for bearing anomaly detection, so the signal contains only the frequency band under 1250 Hz.The third approach is GMM-WBBS. It processes the signal as described in The reason for such comparison arises from the problem described in Column 5 contains the difference in classification accuracy between LP and GMM-WBBS. Even though both methods remove frequency bands C and D, classification results using GMM-WBBS are higher for all three classifiers. This difference is a result of the utilization of fault frequency harmonics band selection using GMM-WBBS, which decreases the interference of normal bearing vibration in the lower frequencies and allows for more accurate classification in comparison with simple low pass filtering. The difference in classification performance among the proposed method WPT-BE-MSVM and WPT-PCA-MSVM can be explained by the difference in feature selection. Feature selection in the proposed method described in The confusion matrix for the GMM-WBBS method for all states data with Weighted KNN is provided in The limitations of the proposed method are within the following factors. Like in most bearing fault diagnosis methods, it is necessary to have the information on RPM at any moment to calculate the fault characteristic frequencies for the GMM-WBBS band selection. The feature selection algorithm uses an NSD value, calculation of which is an iterative process, and which is performed for every bearing operation state with the given data and needs to be improved in the future. Furthermore, in the future, feature extraction quality can be improved by using Deep Learning methods. In recent years, the burgeoning intelligent bearing fault diagnosis field has attracted a lot of researchers, and the performance of data-driven diagnosis techniques continues to rise. However, at the current moment, a majority of proposed bearing fault diagnosis methods that utilize vibration data do not perform the analysis of the raw data provided to ML algorithms. Thus, the utilized bearing vibration signals may contain discriminative data that may be irrelevant to the actual physical phenomenon of bearing fault. The features extracted from this data can unpredictably both aid or degrade the performance of classifiers. Since this problem cannot be resolved by feature selection techniques, a GMM-WBBS method is proposed to address it. To guarantee the immediate relation of the extracted features to actual bearing fault signatures and prevent the use of irrelevant discriminant data, the proposed method exclusively targets bearing fault-related frequency components, that are selected based on construction and parameters of operation. Specifically, the frequency bands in the demodulated bearing vibration signal irrelevant to the bearing fault are suppressed. Then, time domain and frequency domain statistical features are extracted from the demodulated signal containing exclusively bearing fault signature frequencies and the feature selection technique was used to eliminate the low-quality and redundant features. The bearing fault diagnosis was performed using the Weighted KNN classification algorithm. The performance of the proposed method is analyzed and compared to state-of-the-art methods. The comparisons demonstrated the effect of utilization of non-relevant discriminative information. WPT-BE-MSVM and WPT-PCA-MSVM methods retrofitted with the proposed GMM-WBBS method show improved classification performance in all cases for fault diagnosis in case of a single fault present. The method was not tested on data with multiple faults occurring at the same time. In future work, the feature extraction and selection part of the proposed method can be improved by utilizing Deep Learning techniques. Techniques capable of extracting the RPM information from the vibration data could be beneficial for the simplification of GMM window generation and placement. Besides the improvement of the method, testing the method on data with multiple simultaneous faults will allow it to better evaluate its fault diagnosis capability.


What information-processing strategies and general principles are sufficient to enable self-organized morphogenesis in embryogenesis and regeneration? We designed and analyzed a minimal model of self-scaling axial patterning consisting of a cellular network that develops activity patterns within implicitly set bounds. The properties of the cells are determined by internal ‘genetic’ networks with an architecture shared across all cells. We used machine-learning to identify models that enable this virtual mini-embryo to pattern a typical axial gradient while simultaneously sensing the set boundaries within which to develop it from homogeneous conditions—a setting that captures the essence of early embryogenesis. Interestingly, the model revealed several features  for which it was not directly selected, showing how these common biological design principles can emerge as a consequence of simple patterning modes. A novel “causal network” analysis of the best model furthermore revealed that the originally symmetric model dynamically integrates into intercellular causal networks characterized by broken-symmetry, long-range influence and modularity, offering an interpretable macroscale-circuit-based explanation for phenotypic patterning. This work shows how computation could occur in biological development and how machine learning approaches can generate hypotheses and deepen our understanding of how featureless tissues might develop sophisticated patterns—an essential step towards predictive control of morphogenesis in regenerative medicine or synthetic bioengineering contexts. The tools developed here also have the potential to benefit machine learning via new forms of backpropagation and by leveraging the novel distributed self-representation mechanisms to improve robustness and generalization. Hnonlinear computational account of patterning that moreover is also interpretable. Such an account may be invaluable for systematically addressing the inverse patterning problem where one wishes to modify the underlying regulatory systems to achieve desired patterning outcomes [To answer such questions, various conceptual and computational models have been developed ,22,23,24outcomes ,39.One of the central challenges with respect to models is thus an understanding of the high-level information-processing principles they leverage towards the patterning outcomes. For instance, the “balancing model” proposed by Wolpert represents cells as point-entities that make linear measurements such as computing simple averages. As helpful as the model is in hypothesizing high-level principles, they do not elucidate the underlying biological complexity. Models based on reaction-diffusion or artificial neural networks, on the other hand, capture some of the biological complexity but suffer from the drawback that it is difficult to elucidate the underlying nonlinear causal relationships using conventional analysis techniques ,41.Information theory offers tools ,43,44 toThe goal of this paper is thus two-fold: (1) to design a minimal nonlinear dynamical model of generic pattern formation using plausible biological priors; and (2) to characterize the high-level organizational principles that the model employs by applying analysis methods that are extensions of conventional methods used for ANNs.We designed a minimal model of bounded axial patterning—a phenomenon that commonly occurs during embryogenesis and regeneration where a featureless tissue develops an epidermis (boundary) and an axis within it  along which distinct morphological features later appear . Rather The functional output sought in this model is to develop an axial pattern of positional information at the same time marking the boundary (epidermis) within which to develop it . Even thIn summary, while the cells communicate over the gap-junctions to form the target pattern within a boundary, their internal signaling networks help build the tissue-level intercellular network and the network-boundary themselves.The formal definition of the model is depicted in The specific problem that the model learned to solve comprises of two main features (details below): (1) a network activity state pattern that is shaped like a simple gradient, with positive values at the anterior and negative values at the posterior and tapering off (goes to zero) at either pole; and (2) a boundary marker pattern where the boundary cells have double the value compared to the cells in between . These target patterns were meant to serve as idealized axial polarity and boundary marker patterns to encourage the model to develop patterns that approximately match them.Pytorch [The method of BPTT broadly involves instantiating a model with random parameters chosen from a specific range, simulating the model for a certain number of steps, then calculating the loss as the difference in the observed network activity and boundary marker patterns with the corresponding set targets and finally backpropagating the loss back to the parameters. The loss function involved a simple mean squared error (MSE) between the observed and the target patterns with equal weights assigned to the network-activity and the boundary-marker patterns. This process was repeated  until a model with a satisfactory performance was obtained. The parameters  Pytorch , that emWe sought to explore potential information-processing strategies and general principles that biological systems may employ for the purpose of developmental patterning. To that end, we answer the following questions in this paper: (1) can the model be trained to solve the patterning problem? and (2) how does a successful model work, specifically, how is the information about the target network activity pattern organized in the intrinsic controllers? To answer the second question, we formulated a measure of causal influence (CI) to quantify a graded measure of information—the more a variable In the rest of the paper, we use this formulation of multi-timescale causal influence to quantify the amount of information about patterning activity state Pytorch [Here, dynamics ,70. We u[Pytorch , built o[Pytorch   representation of the operations in the dynamical model known as the “computational graph” using the chain rule , whereasWe hypothesized that the biologically inspired minimal dynamical model can solve the patterning problem using self-organization alone, that is, without the aid of any external instructions or special initial conditions. We found that the model can indeed solve this problem in this way. We also found, using the causal network integration approach, that the model dynamically breaks symmetry and integrates into a macroscale network with emergent patterns whose characteristic features explain the shape of the activity pattern. Below, we describe these results in detail. We start with a description of the results of the training and the overt patterning of the best-trained model, followed by a depiction of how the patterning behavior is reflected in the activity of the internal controllers and in single-cells, concluding with a characterization of the causal network machinery that links the controller activity to the overt behavior. The results of the parallel in-depth analysis of the boundary-marker patterning are presented in We simultaneously trained a set of 100 models for about 100,000 iterations, a majority of which (72%) attained satisfying performance with an average MSE loss of about 0.04 and a minimum of about 0.02, compared to the performance of a set of random untrained models with an average MSE loss of about 6.1 , where tThe model develops the network activity pattern and marks the boundaries starting from homogeneous conditions as expected . This prThe properties of the individual cells differentiate into characteristic states depending on the relative position of the cell, when initiated from homogeneous conditions . That isThe PCP-like structure of a single cell  is an imThe model has not only learned to solve the patterning problem it was trained for, but also to regenerate and rescale patterns—abilities that were not rewarded for during the training. Specifically, when a fully developed pattern is partly reset, where just the state of a small portion in the middle portion is retained, the model successfully regenerates the rest of the pattern a. LikewiThe model is surprisingly robust to a wider variety of initial conditions . That isThe states of the intrinsic controller nodes converge to patterns that simultaneously resemble the cell properties and the network activity patterns, depending on whether the states are normalized or not. Specifically, while the absolute intrinsic controller node states resemble the pattern of cellular properties  a, their A potential explanation for why the internal controller activity patterns correlate with the cellular activity patterns, as described above, is that the controllers are inherently fine-tuned to the cell’s activity. That is, when a single cell is isolated from the network and the cell activity state is clamped, thus acting as the external input to the intrinsic controller, most controller node states tend to linearly correlate with the clamped input state a. HoweveWe measured the causal influence exerted by the initial state of every internal controller node of every cell over the asymptotic network activity. The resulting patterns of causal influence  closely Furthermore, the various spatially segregated regions (columns) of the controllers exhibit symmetrically flipped causal influence patterns, reflecting their PCP-like organization at the network level  model itself, nor by that of the causal networks corresponding to lower timescales. In this way, the method of causal network integration partly helps close the gap between the structure and function of a complex dynamical model by focusing on the circuit-space rather than the conventional state-space.The causal network integration approach offers further insights into the underlying mechanisms of patterning. It partly explains why rescaling the model (doubling the number of cells) results in the rescaling of the phenotypic patterns b—the undOne of the emergent abilities of the model is to canalize random initial states (not seen during training) to similar pattern attractors . The reaWe have shown here that it is possible to train, using machine-learning, a recurrent self-organizing dynamical model incorporating biological priors to form gradient-like activity patterns from homogeneous conditions, that is, without the aid of externally supplied positional information or special initial conditions. We have also revealed the multi-timescale causal relationships among the components of the model, thereby describing the high-level mechanistic logic of pattern formation that the model employs.One of the surprising findings of this work is the ability of the model to rescale the final pattern to an arbitrary number of cells b despiteFrom the perspective of morphogenesis, the causal network attractors described here could be conceived as a decoding of the “developmental program” encoded in the model’s architecture and its learned parameters. In other words, these causal networks could be understood as forms of the developmental program itself. Moreover, the same underlying model could have multiple causal networks , each responsible for a unique function, such as activity-patterning or boundary-marking, as we show here. In other words, starting from the 1st order “physiological” network we have uncovered 2nd order “physiocausal” networks. Thus, our work offers new perspectives and tools to achieve one of the major goals of developmental biology—to uncover the developmental programs that organisms use for morphogenesis [From the perspective of computation, the causal networks could be viewed as high-level algorithms that the underlying biophysical machinery (model) employs for the purpose of axial pattern development. By “computation” we mean information-processing or transformation of information that serves a purpose  . One migViewed through the lens of the theory of computation, our model can be seen as an instance of autonomous sequential logic circuits—a type of finite state machine that does not involve external inputs. This is an appropriate class of models for developmental processes, as they are characteristically autonomous  and seqOne of the striking features of the causal network attractors is long-distance influence: the relatively more significant asymptotic influence of the intrinsic controller of a cell on the activity state of another cell than itself, an emergent feature that is not baked in the original model . A bioloOur work also offers new tools for solving the problem of top-down control in biology ,91, wherOur work shows that the method of causal network integration has the potential to generate multi-timescale insights into how information is organized in the network. This approach of the analysis of a dynamical system focuses on the circuit-space , whereas conventional approaches tend to focus on the state-space of the system. By casting the emergent dynamics at multiple scales in the circuit space, this approach brings us a step closer to closing the gap between the structure and the function of a complex dynamical system. In this regard, our method also contributes to the theory of complex systems by complementing and potentially generalizing existing approaches to characterizing canalization , controlhttps://gitlab.com/smanicka/MinimalDevelopmentalComputation (accessed on: 4 January 2022).All the code developed for this project can be found at:


The anterior segment of the eye is a complex set of structures that collectively act to maintain the integrity of the globe and direct light towards the posteriorly located retina. The eye is exposed to numerous physical and environmental insults such as infection, UV radiation, physical or chemical injuries. Loss of transparency to the cornea or lens (cataract) and dysfunctional regulation of intra ocular pressure (glaucoma) are leading causes of worldwide blindness. Whilst traditional therapeutic approaches can improve vision, their effect often fails to control the multiple pathological events that lead to long-term vision loss. Regenerative medicine approaches in the eye have already had success with ocular stem cell therapy and ex vivo production of cornea and conjunctival tissue for transplant recovering patients’ vision. However, advancements are required to increase the efficacy of these as well as develop other ocular cell therapies. One of the most important challenges that determines the success of regenerative approaches is the preservation of the stem cell properties during expansion culture in vitro. To achieve this, the environment must provide the physical, chemical and biological factors that ensure the maintenance of their undifferentiated state, as well as their proliferative capacity. This is likely to be accomplished by replicating the natural stem cell niche in vitro. Due to the complex nature of the cell microenvironment, the creation of such artificial niches requires the use of bioengineering techniques which can replicate the physico-chemical properties and the dynamic cell–extracellular matrix interactions that maintain the stem cell phenotype. This review discusses the progress made in the replication of stem cell niches from the anterior ocular segment by using bioengineering approaches and their therapeutic implications. The eye is a complex organ that allows the visual perception of the environment by capturing the light reflected by the surrounding objects and transforming it into electrical signals that can be recognized and processed by the brain. This phenomenon requires a fine coordination between the different tissues that constitute the eye , as eachThe maintenance of a healthy status in every single constituent of the ocular globe is essential for the preservation of the individual’s visual quality. However, this is particularly difficult in the anterior segment of the eye, which is constantly exposed to environmental insults such as UV radiation, injuries or infections. The treatment of the wide range of diseases that result from such stresses poses a significant challenge for clinicians, as conventional therapies have been generally unsuccessful in curing such syndromes. On the other hand, reconstructive therapies based on the use of stem cells hold great promise for treating ocular diseases .Stem cells are relatively undifferentiated cells that have the ability to self-renew and generate one or more differentiated progeny cells ,11,12. IGiven the heterogeneity of the tissues that comprise the ocular globe, different stem cell niches can be found in this organ . The repThis review describes the advancements that have been made in the replication of each of the stem cell microenvironments that are located in the anterior segment of the vertebrate eye. The biofabrication methods employed in their synthesis and their therapeutic implications are discussed in detail.Blindness or vision loss can be caused either by the failure of the light to reach the retina or the inability of the retina to convert the light into electrical signals . The maiDifferent types of stem cells are currently available for use in ocular regeneration. These include embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs) and adult stem cells. While the use of ESCs and iPSCs is highly advantageous due to their pluripotency, it also presents several challenges, such as the potential immune rejection of allogeneic cells, the difficulty to purify a specific cell type from the heterogeneous stem cell-derived population and their tumorigenic potential. These issues can be circumvented with the utilisation of adult stem cells, which allow for autologous cell therapy and have a lower tumorigenic potential than ESCs and iPSCs ,18. As pStem cell niches are specialised and controlled local tissue compartments that contribute to the maintenance of multipotent stem cell populations in adult tissue . They prThe maintenance of stem cells requires a coordinated interaction between physical, chemical and biological factors. Physical cues include the ECM elasticity and stiffness, which can regulate stem cell differentiation to different lineages . SourcesThe replication of stem cell microenvironments is highly challenging and requires the use of biofabrication techniques. This involves the use of biomaterials that mimic the nanoscale architecture and chemical composition of the ECM and the incubation in bioreactors which recreate the physiological conditions ,51. The A wide variety of biofabrication techniques are available for the production and functionalisation of bioengineered constructs. Some of these methods are capable of creating 3D structures with controlled geometries and physico-chemical properties. Furthermore, these technologies allow the integration of biomacromolecules in order to increase the bioactivity of the resulting structures . Some ofThe working principle of photolithography techniques is based on the use of light to transfer geometric shapes or patterns to a photosensitive material by means of a photomask. The popularity of this method in tissue engineering is due to the uniform cell encapsulation that can be achieved, the reduced heat production and the high degree of control over the reaction kinetics. However, the photoinitiator molecules that are present in the formulations can generate toxic free radicals .Electrospinning is a versatile method to produce of nanofibers by applying a high voltage to a polymer solution as it passes through a needle. The adjustment of parameters such as the applied voltage, flow rate or polymer concentration results in the generation of fibres with controlled size and morphology. Some of the advantages of the use of this technique include the wide variety of polymers that can be processed with it, as well as the relatively low cost and simplicity of the equipment required ,55. An aThree-dimensional printing, also known as additive manufacturing, is a technology that fabricates objects by depositing materials layer by layer until a 3D structure is formed. Before printing, the structure is modelled on computer-aided design software (CAD) or obtained via computerised tomography (CT) scan. This method allows the fabrication of biomimetic structures with specific designs ,58,59. FAll these techniques have been employed in the development of artificial stem cell microenvironments from the anterior surface of the eye. The following sections provide a revision of the advancements made to date on the replication of each of these niches by making use of such technologies.The cornea is the avascular, multi-layered and transparent conical shaped outer structure of the eye and it is found anterior to the iris and pupil. The cornea is essential for maintaining vision as it provides approximately 65–75% of the refraction and transmission of light rays to the lens and focuses light on the retina. As the outermost structure, the cornea is mechanically tough as it acts as the physical barrier protecting the inner content of the eye against infection and environmental damage along with the tear film ,65. The As one of the most densely innervated tissues in the body, the corneal nerves are crucial in regulating the protection and maintenance of the ocular surface integrity as well as being important indicators  of a healing cornea after surgery ,68. The The corneal epithelium lines the external surface of the stroma and is continuous with the conjunctival epithelium. The limbus is the unique junctional zone (1–2 mm) that separates the corneal–conjunctival boundary and the underlying sclera. The limbal epithelial stem cells (LESCs), which are resident in the palisades of Vogt, the infoldings of the limbus, are physiologically responsible for replenishing the corneal epithelial layer  79]. Un. Un79]. The corneal endothelium lacks mitotic activity after birth and, therefore, has limited capacity for regeneration. Normally, in such cases, the neighbouring healthy endothelial cells spread out and change shape to cover the gaps left by lost cells, this is clinically known as polymegathism. However, a line of evidence has accumulated over recent years indicating that there is a putative population of endothelial progenitor cells (EPCs) that exhibit stem cell-like characteristics  present in the peripheral endothelium  38,73,9,96,97,98The World Health Organisation (WHO) world report on vision stated that of the 2.2 billion people worldwide that have a vision impairment, 4.2 million of those are due to corneal opacities. There are many types of corneal disease; the more common ones include allergies, dry eyes and keratitis, whilst others such as limbal epithelial stem cell deficiency (LSCD), Stevens–Johnson syndrome and ocular pemphigoid can cause distortion, scarring and eventually blindness .Corneal transplantation is a common treatment option; however, (i) there is a donor tissue shortage due to lack of supplies of corneal donors, (ii) significant immune rejection rates, which then require re-grafting, (iii) possibilities of infection, (iv) not all patients are viable candidates and (v) the waiting lists are lengthy ,108. TisSignificant efforts have been made to develop substitutes to replace the damaged or diseased corneal tissue via two approaches: (i) a cell-based strategy and (ii) a scaffold-based strategy. Subsequently, researchers have been able to generate viable levels of complexity in engineering cornea tissue equivalents of the epithelium, stroma and endothelium layers ,111.The feasibility of harvesting corneal epithelial stem cells on human amniotic membrane (hAM) to bioengineer a corneal surface replacement was demonstrated by Schwab et al., which pointed towards opportunities to tackle difficult ocular surface diseases such as LSCD . HoweverThese results demonstrate the substantial progress made in the research towards developing a commercially available and suitable substitute for hAM in corneal transplantation. However, crucial fine-tuning is required in the biomechanical properties of the PLGA membrane which will have impact on the final product when it comes to (i) storage and packaging, (ii) accessibility and handling by surgeons, (iii) pre- and post-treatment in cases of recurrence of pannus and LSCD in patients and (iv) the use of fibrin glue to hold the limbus explants on the PLGA membranes. Future actions that would be interesting to see is how the newly chemically modified membranes (solvent and/or plasticiser) integrated (i) with vs without micro-pockets, (ii) with vs without fibrin glue, (iii) limbus explants or dissociated limbal cells and (iv) patients’ own limbus explants vs donor explants would perform in an in vivo study.TM system, which is a 3D cell culture system. They created microtopographies of 100, 150, 200 or 250 µm micro-ridges in collagen I hydrogels that mimic the 3D architecture of the limbal crypt using a rapid, one-step process. The RAFT constructs were able to provide support to a mixed population of human limbal epithelial cells (hLECs), thus enabling cell survival, proliferation and differentiation, as well as sustaining a population of cells with stem cell characteristics as observed by the high percentage of p63α positive cells lining the engineered microscale crypts. Ultimately, RAFT proved to be an excellent in vitro model to study cell–cell interaction and cell–material interaction by reconstructing the LSCN .Corneal stromal dystrophy and degeneration, together with stromal scarring due to infection, are the most common corneal stromal diseases, and, as the most complex of the three layers that constitute the cornea, the stroma so far has traditionally been replaced by human donor corneas or artificial corneas . HoweverHuman embryonic stem cell-derived limbal epithelial stem cells (hESC-LESC) were laser-assisted bioprinted (LaBP) into epithelium-mimicking structures on a base constituted from human sourced collagen I and recombinant human laminin. A bioengineering tactic that could mimic the native corneal tissue was investigated by Sorkio et al. where their 3D bioprinted stromal constructs exhibited cell survival, proliferation as well as differentiation, as demonstrated through their co-expression of Ki67, p63a, p40, CK3, CK15, collagen type I and VWF. Seven days after implantation into a porcine corneal organ culture, the 3D bioprinted stromal constructs attached to the host tissue as a result of cell migration from the layered construct. Further studies are required to determine the feasibility of the approach in vitro and in vivo; however, this first study demonstrates its potential for corneal application ,125.The 3D bioprinting approach of stromal constructs provides insight into the characteristics required to re-establish an LSCN to support LESCs that differentiate into functional LECs attached without the use of fibrin glue. The cornea is a layered tissue, so a layered tissue-engineered approach would hope to produce a tissue most closely related to that of the native LSCN. Furthermore, 3D bioprinting facilitates high-resolution printing of parameters that allow the incorporation of simple and complex features that are geometrically well-defined in the same construct, such as those observed in the native niche. Ultimately, this approach has the promise of making medical care more personalised, more cost-effective, faster and accessible.Fuch’s endothelial corneal dystrophy (FECD) is characterised by a thickening of the Descemet’s membrane and loss of corneal endothelial cells. As corneal endothelial cells (CECs) have an extremely limited proliferative capacity in vivo, when this loss reaches a critically low level, corneal oedema occurs, leading to loss of transparency and subsequent impairment in vision and an endothelial transplant is usually required . In ordeCurrently, all the attempts to bioengineer the corneal endothelium have been to create a mature endothelial graft rather than to reconstruct an assumed endothelial progenitor/stem cell niche. Whether a corneal endothelial stem cell niche even exists is still under debate, with Sie et al. proposing the summarised table for various corneal endothelial stem cell niches  95]. Fu. Fu95]. A similar microstructure to that of the native DM was reconstructed using two-photon lithography by Gutermuth et al., where they cultured skin-derived human mesenchymal stem cells (hMSCs), which had a neural crest origin in accordance with naturally derived human CECs. Their results demonstrated that the physical cues of the microenvironment influenced the hMSCs to differentiate into functional CEC-like monolayers which were assessed through gene expression  using polymerase-chain reaction (PCR) and by observing the characteristic polygonal morphology of native CEC monolayers. Thus, presenting an innovative and promising approach in regenerating a functional CEC monolayer from hMSCs .Thus far, a combinational approach has been taken with scaffolds that are 2D (hydrogels and/or non-hydrogels) and 3D (bioprinting and/or lithography). Unfortunately, these approaches have not yet reached first in-human studies for the corneal endothelial emphasising that considerable work is still needed for the bioengineered construct to be regulatory compliant and successful in the clinic.As the most densely innervated tissue, the state of the corneas’ health is dependent on the epithelial and stromal nerve organisation. Much of the research has been focused on the tissue engineering and regenerative medicine of the cornea; however, little advancement has been made in understanding and mimicking the in vivo stromal–nerve interactions . Wang etDamage in the endothelium is one of the main reasons for corneal transplantation; however, more focus is required to establish an engineering solution that is able to replicate all of the three cell layers and two membranes. A bioengineering approach must be developed that facilitates a 3D system that would be able to maintain the populations of stem cells, promote differentiation when required, maintain the function of cells such as the pump/leak function of the endothelium, as well as encourage innervation throughout the layers.The conjunctiva is a thin, translucent membrane that covers the posterior surface of the eyelids and the anterior surface of the sclera, spanning from the eyelid margin to the limbus. This highly vascularised tissue produces the mucin component of the tear film that lubricates the eye ,133. FurIt has been reported that the conjunctiva is a self-renewing tissue with a rapid cell turnover and a stem cell niche that replenishes the conjunctival epithelial cells that are continuously lost . SeveralThe conjunctiva is susceptible to various diseases, including trachoma, chemical and thermal burns, conjunctival lacerations, autoimmune diseases, mucous membrane pemphigoid and Stevens–Johnson syndrome. These can result in cicatrisation, chronically painful eyes and blindness ,150. SomAs cell–niche interactions have a direct effect on stem cell survival and function, the presence of a functional niche is key for the success of stem cell transplantation ,153,154.One of the most promising techniques for the production of artificial conjunctival microenvironments is 3D bioprinting. This approach allows a high degree of control over the cell density and location in the engineered construct. Furthermore, the use of bioprinting technologies enables the production of structures with dimensions, geometry and mechanical properties that are comparable to those of the natural tissue. The inclusion of biological materials in the bioinks leads to a high biocompatibility and an improved regenerative capacity . These pAnother technique that has been explored in the production of artificial conjunctival replacements is electrospinning. The nanofibrous scaffolds produced with this method have been shown to promote cell adhesion and proliferation. Furthermore, their architecture facilitates the exchange of nutrients and metabolites. Yao et al. reported the development of electrospun scaffolds made of silk fibroin (SF) and poly(L-lactic acid-co-ε-caprolactone) (PLCL) for conjunctival reconstruction. The rationale behind blending these materials is based on the fact that SF is a natural material that would increase the bioactivity of the scaffold, while the synthetic polymer PLCL would improve the mechanical strength of the resulting material. It is important to note that the SF/PLCL scaffolds supported the phenotypic development of goblet cells. The authors suggested that this could be due to the fibrous and highly porous structure of the membranes, which resembles that of the native ECM and enables the exchange of nutrients . While tWhile engineering approaches focus more on the replication of the structure and the biochemical composition of the cell microenvironment, other methods are based on the co-culture of stem cells with other cell types that support their growth and the maintenance of their phenotype. As mentioned above, such cells include stromal fibroblasts, goblet cells, melanocytes and immune cells. The presence of fibroblasts is key for the maintenance of the stem cell population, as they provide growth factors that are fundamental for them. This has been corroborated by experiments performed in vitro that confirmed the role of fibroblast support in the maintenance of an undifferentiated phenotype ,161,162.The iris is a circular structure that divides the space between the cornea and the lens into anterior and posterior chambers . It has The anterior surface of the iris contains fibroblasts and collagen and is exposed to the aqueous humour. Underneath the fibroblasts lies a layer of melanocytes whose melanin content determines the colour of the iris ,164. BenIris pigmented epithelium cells are highly plastic, and studies conducted in vertebrates have shown their ability to transdifferentiate into cells from different lineages . Two stuIPE cells can also be used to replace retinal pigment epithelium (RPE) cells, as they are both derived from the neural ectoderm. Both cell types share various characteristics such as heavy pigmentation, apical/basolateral polarity, tight junctions, retinol metabolism and phagocytic ability. IPE cell transplantation into the retina requires the use of a substrate for the formation of a cell monolayer, as cells need to maintain a specific orientation. A promising material for the synthesis of such substrates is expanded polytetrafluoroethylene (ePTFE). As this material is hydrophobic, its surface must be functionalised in order to support cell attachment. Nian et al. reported the fabrication of ePTFE coated with fibronectin and n-heptylamine (F-HA ePTFE) and the successful transplantation of IPE cells into the subretinal space of rats by using this substrate .The crystalline lens of the vertebrate eye is a transparent, biconvex and elastic structure that is located in the posterior chamber of the eye ,179,180.The vertebrate lens contains high levels of structural proteins known as crystallins. Different types of crystallins, αA-, αB-, β- and γ-, have been detected in this tissue. Higher concentrations of these proteins can be found in the lens fibre cells, leading to the generation of a high refractive index . FurtherNTR in the germinative zone of the developing mouse lens. However, this becomes undetectable in older lenses. Other stem cell markers such as Msi1 and Lgr4 have been detected in the lens epithelium and differentiating fibre cells [NTR, Nestin, Bcl2, β1 integrin and Sca-1 [The lens possesses regenerative capacity under specific circumstances where the lens capsule is preserved after trauma. Some authors consider that a subpopulation of the LeECs that reside in the anterior capsule act as stem cells, as they have a slow proliferation rate, differentiate into fibre cells and are recruited after injury of the central epithelium ,185. Yamre cells ,193. Anond Sca-1 .Despite all the evidence that seems to confirm the presence of stem cells in the lens, it is important to note that this structure lacks some of the characteristic features of a stem cell niche, such as a high vascularisation or innervation. However, the fact that lens cell division occurs during the entire lifetime of an individual indicates that a stem cell population should be present in this tissue. To explain this, Remington and Meyer hypothesised that the lens stem cell niche may be located outside the lens capsule. Due to its proximity to the lens germinative zone, they suggested that the ciliary body could be the tissue that houses the lens stem cell population. This idea is supported by the fact that cells from the ciliary body can develop lentoids in vitro. The migration of lens stem cells from the ciliary body into the lens capsule could be mediated by the zonular fibres, which integrate with the lens capsule near the germinative zone. Therefore, these could serve as entry points into the lens .Given the inconclusive and scarce evidence with regard to the presence of a stem cell population in the lens, further studies need to be performed in order to confirm it. The knowledge gained from this research would be highly beneficial for the development of novel therapies for lens diseases such as cataract. The current treatment for this disease is a surgical procedure in which an artificial intraocular lens (IOL) is implanted in the capsular bag . Whilst The ciliary body forms a ring of muscular tissue around the interior part of the anterior sclera that extends from the posterior side of the iris to the beginning of the retina, where the ciliary margin zone (CMZ) is located  199,200200. Thispars plana or orbicularis ciliaris, is connected to the choroid at the ora serrata and also with the vitreous and the periphery of the retina. Its inner surface is flat and contains a high amount of pigment. The anterior portion is known as the pars plicata or corona ciliaris, which is the area that is involved in the production of aqueous humour. This part of the ciliary body is contiguous to the posterior surface of the iris and has a length of approximately 2 mm. It is formed of 70–100 ridges known as ciliary processes, which increase the surface area for aqueous humour production. These structures are oriented radially around the pupil. Between the ciliary processes, there are spaces where the zonular fibres originate. These structures connect the lens to the ciliary body. Another structure that forms part of the ciliary body is the ciliary muscle, whose contraction opens the trabecular meshwork and the Schlemm’s canal. Therefore, the ciliary body is also involved in the regulation of the aqueous humour outflow through the sclera to the extraocular tissues and ultimately to the lymphatic system [The ciliary body is divided into two different regions. The posterior part, known as the c system ,210,211.Xenopus laevis and Xenopus tropicalis demonstrated that RSCs from the ciliary margin contributed to retinal regeneration after whole retinal removal [The ciliary processes are covered by two layers of an epithelium that is formed of secretory cells ,205,209. removal ,215. The removal . With re removal ,217. Whi removal ,219,220. removal . Another removal . However removal ,223,224. removal . The abs removal . NeverthGiven the conflicting results obtained regarding to the differentiation potential of these cells, it is essential to conduct functional studies that show whether or not this cell population has the characteristics that are required for them to yield functionally mature cells. A way to assess this is to evaluate their activity after retinal injury. Frøen et al. hypothesised that this cell population would be activated in human eyes that suffer from vitreoretinopathy. While this retinal disease induced cell proliferation in the ciliary epithelium, neural stem cell markers were only detected in a small region of the ciliary epithelium, near the retinal periphery. When performing this same study in mice, the authors detected a Nestin upregulation in the ciliary epithelium. This is of high importance as most of the studies that showed positive results with regard to differentiation of these stem cells into neural lineages were conducted in rodents. Therefore, the contradictory results reported in the literature may be a result of interspecies differences. However, further studies are required in order to confirm this theory .As well as the inconclusive evidence regarding the differentiation potential of the stem cell population found in the ciliary body, the limited availability of these cells would pose a challenge in their use for retinal regeneration . A potenThe anterior segment of the eye is filled with aqueous humour, a fluid that supplies nutrients to the avascular cornea and lens and which maintains the right level of intraocular pressure. This fluid is produced by the ciliary body and flows through the pupil into the anterior chamber. After reaching this point, 40–96% of the aqueous humour is drained into the blood circulation after passing through the trabecular meshwork (TM) pathway ,230. ThiAnatomically, the TM is a triangular structure with a length that ranges from 500 to 800 µm and which is located in the anterior segment of the eye, at the iridocorneal angle ,232. It The specialised cells lining the lamellae of the TM contribute to the filtering ability of this structure by secreting enzymes and phagocytosing cellular debris and reactive oxygen species present in the aqueous humour as it passes through the TM . No singTrabecular meshwork progenitor cells are believed to be primarily but not exclusively located within the CMZ ,238,244.Understanding the pathological changes in the TM is paramount to current and future bioengineering approaches to replicate an artificial TM for medical research or therapy. Glaucoma is the most common cause of irreversible blindness worldwide. Patients suffer from progressive vision loss due to axonal damage and atrophy of the optic nerves . A rise Trabecular meshwork cells respond to their environment, with changes in stiffness causing changes in gene expression and also cell stiffness ,260. HeaDue to its complex architecture, creating a bioengineered TM in vitro poses a major challenge . To mimiPeptide hydrogels have been used to create artificial scaffolds able to repair the TM and to be used as in vitro models to test the efficacy of drugs as glaucoma treatments . WaduthaAnother approach that has been explored in the development of artificial TM is electrospinning, a technique based on the application of high voltage to a polymer solution injected through a needle in order to generate micro or nanofibers . ElectroPhotolithography can provide a higher degree of control over the pore size and arrangement in order to mimic the structure of the TM. This technique is based on the use of light and a photomask to synthesise or degrade a polymeric material in a specific geometric pattern . The resScleral thinning is a characteristic change that is observed in the condition of myopia, which is the most common ocular disorder that affects approximately 2 billion people worldwide and this number is projected to rise at least twofold by 2050. Research has shown both genetic and environmental factors play a crucial part in the cause of myopia where the vitreous chamber of the ocular globe is lengthened causing the retina to lie between the focal planes. This causes refractive error, “short-sightedness” that is the leading cause of visual impairment worldwide ,269,270.The sclera is the white 5/6 parts of the outer coat of the eye that is continuous with the cornea anteriorly. This fibrous outer coat consists of 90% collagen and a mixture of 10% proteoglycans and glycoproteins ,272,273.The sclera consists of the episclera, stromal sclera and laminafusca, which contains the spindle-shaped scleral fibroblasts. Tsai et al. examined whether the sclera contained mesenchymal stem/progenitor cells and found that scleral stem cells were able to express ABCG2, Six2, Pax6 and Notch1, and had a mesenchymal origin. Furthermore, due to their multipotent nature, these scleral stem/progenitor cells (SSPCs) have great potential to be used in cell-based therapy, for the reconstruction of artificial sclera ,278 and The importance of the biomechanical characteristics of the limbal stem cell niche (LSCN) was reviewed by Eberwein and Renhard as it is an underestimated aspect of the niche. The LSCN is found at the anatomic border of the conjunctiva/sclera and cornea. They highlighted how imperative it is when generating artificial biomimetic constructs such as the cornea, conjunctiva and sclera to keep in mind the mechanical characteristics of the encompassing niche . This noNew approaches for regenerating the scleral tissue are currently being investigated by attempting to make use of tissue components, namely (i) cells, (ii) soluble regulators , (iii) insoluble regulators (scaffolds) or (iv) a mixture of the above. For instance, treating a thinned sclera can be attempted via cell printing and transfer technology by using the patients’ own cells .Currently, there are very few treatments that target the sclera directly as all strategies so far have been optical or pharmacologic and the long-term negative implications of these treatments on the health of the whole ocular system remain unknown .Researchers must consider the close proximity of the sclera to the limbus as embryologically they originate from the mesoderm. It would be reasonable to reconstruct a niche that incorporates the physical and chemical cues that make up the bordering regions as well as the encompassing cell–cell interactions, as it is these components that maintain cell stemness as well as promote differentiation in homeostasis. Consequently, as this task is so complex, it may explain why most previous research is focused on the biomechanical properties of the sclera rather than attempting to recreate a scleral stem cell niche.Stem cell therapy has great potential for the cure of ocular diseases, and early success has already allowed clinicians to successfully restore the corneal epithelium . While dWhilst the use of biofabrication techniques has proved successful in the replication of structural features of the cell microenvironment, further work is needed in order to recapitulate the specific cell–cell interactions that take place in native niches. The development of engineered co-culture systems would be highly advantageous in this endeavour. It is also important to consider that the ECM is highly dynamic and that it is through the continuous fine-tuning of its biophysical and biochemical properties that stem cells are regulated . From thAs well as the implications that the development of biomimetic stem cell niches would have in ocular stem cell therapy, other areas would also benefit from this advancement. For example, the pharmaceutical industry would benefit from the accessibility to models that can be used as tools for drug screening prior to clinical trials. Regenerative therapies which are not based on the use of stem cells could also benefit from this, as bioengineered constructs can be used as implantable scaffolds for tissue regeneration. Finally, artificial stem cell niches can be used as in vitro models for cancer research, as they can provide an insight into the modulation of cancer progression by the stem cell niche. This could result in the development of more efficient therapies against this disease .


Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers.This systematic review focuses on the current research investigating the impact of merging information from image features and patient data on the performance of CNN-based skin cancer image classification. This study aims to explore the potential in this field of research by evaluating the types of patient data used, the ways in which the nonimage data are encoded and merged with the image features, and the impact of the integration on the classifier performance.skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined.Google Scholar, PubMed, MEDLINE, and ScienceDirect were screened for peer-reviewed studies published in English that dealt with the integration of patient data within a CNN-based skin cancer classification. The search terms A total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex, and lesion location. The patient data were mostly one-hot encoded. There were differences in the complexity that the encoded patient data were processed with regarding deep learning methods before and after fusing them with the image features for a combined classifier.This study indicates the potential benefits of integrating patient data into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhance classification performance, especially in the case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for patients’ benefits. The incidence of skin cancer has been increasing throughout the world, resulting in substantial health and economic burdens . Early dHowever, single-image classification does not reflect the clinical reality. In fact, dermatologists’ diagnoses are based on both the visual inspection of a single image and the integration of information from various sources. This review presents the status quo of CNN-based skin lesion classification using image input and patient data. The included studies were analyzed with respect to the amount and type of patient data used for integration, the encoding and fusing techniques, and the reported results. The review also discusses the heterogeneity of the studies that have been conducted so far and points out the potential and challenges of such combined classifiers that should be addressed in the future.skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined.Google Scholar, PubMed, MEDLINE, and ScienceDirect were searched for peer-reviewed publications, restricted to human research published in English. The search terms This review only includes skin lesion classification studies using CNNs that consider both image and patient data. It must be noted that there are a few studies that investigated the incorporation of visual and nonvisual information on skin cancer classification, but did not obtain visual features using deep learning techniques, for example, the studies by Binder et al , Alcon eThe objective of this review is to update practitioners on the status quo approaches toward patient data incorporation into CNN-based skin lesion diagnostics regarding all relevant practical aspects.Key question: Which and how many different types of patient data have been tested for CNN-based classification?The goal is to achieve better performance of the CNN-based classifier by integrating new information that cannot be extracted from a digitized image. Various types of patient data have been shown to assist dermatologists. Key questions: What are the encoding and fusing techniques applied in the studies? Do the studies focus on the quantitative relationship between image and nonimage features?A CNN-based classifier extracts various visual features from a digitized image as the basis for its diagnosis. Patient data are nonimage data and are mostly provided as numbers or strings in tables. The patient data can be classified in a dichotomous fashion (presence of the feature: yes or no), fall into several discrete categories , or be continuous . This may require different, carefully chosen encoding and fusing techniques. Moreover, the weight attributed to patient data in comparison with image features can strongly influence how the different features contribute toward the decision making of the system. Key questions: What is the classification task? Is it a binary or multiclass problem? Which skin lesions should be distinguished? How is the influence of individual and/or combined patient data documented? In the case of multiclass classification, is the impact also shown for each single class of skin lesion individually?This review aims to summarize the recent findings regarding the impact of patient data on the performance of CNN-based classifiers. The included publications reported different statistical metrics as the study end points. If the classes in the test set are approximately equally distributed, then accuracy is a frequently used performance metric, where the total number of correctly predicted samples is divided by the total number of samples in the test set. In binary classification problems with a positive and a negative class, sensitivity and specificity are further common study end points, especially if there is an imbalance between the samples of both classes. Sensitivity was determined only on the basis of the actual positive samples in the test set. It is calculated by counting the correctly classified positive samples by the total number of positive samples. In contrast, specificity was determined based on actual negative samples in the test set. Here, the correctly classified negative samples were divided by the total number of negative samples. While using a CNN, the sensitivity and specificity depend on the selected cutoff value. If the output of the neural network is greater than the cutoff value, the input is assigned to the positive class, and if it is below that value, then the input is assigned to the negative class. Thus, this value represents a central parameter for the trade-off between sensitivity and specificity. A decrease in the threshold value leads to an increase in the sensitivity with a simultaneous decrease in specificity and vice versa. The dependence of the cutoff value of the specificity and sensitivity of the two metrics is shown in the receiver operating characteristic curve. Here, the sensitivity is plotted against the false-positive rate (1−specificity) in a diagram for each possible cutoff value. The area under the receiver operating characteristic curve was used as an integral performance measure for the algorithms.A total of 11 publications fulfilling the inclusion criteria are summarized in Most of the studies included three types of patient data . Compared with the diversity of potentially useful patient data illustrated in melanoma=010; BCC=100; NV=001). Different techniques were used for continuous parameters such as the patient’s age. One-hot encoding is only possible after discretizing the continuous range, which was performed by Bonechi et al [The means of choice to encode the patient data was one-hot encoding in most cases. One-hot encoding is one way to encode several discrete classes with a string of bits, where exactly one value in the string of bits encoding one class is assigned 1 and all others are assigned 0  of publications went into detail on how they dealt with missing values. Gessert et al  suggesteThe fusing techniques differ in the way they actively weigh the image and patient data. In 82% (9/11) of studies, a concatenation-based fusion was applied, that is, the feature vector extracted from the images was enlarged by attaching the encoded patient data. In this case, weighting is achieved by defining the ratio between the number of features originating from the image and the patient data input. Common CNN architectures extract 1024, 2048, or even more features from the image input. In most studies, the authors decided to reduce the image features before concatenating them with patient data. In only 27% (3/11) of studies, the authors provided sufficient information on this point and revealed a considerable variance in the ratio of image features to patient data: 112 to 28 , 128 to In addition, the studies vary in the extent to which deep learning methods were applied to the patient data before fusing or on the combined feature vector after fusing it with the image data. Sriwong et al  applied As summarized in Although 5 studies reported results for binary classification tasks, 55% (6/11) of studies dealt with a multiclass classification problem, distinguishing between up to 8 different skin diseases, and revealed insights on how the use of patient data influences the classification performance for an individual type of skin lesion. In total, 36% (4/11) of studies analyzed the influence of the used patient data on the classification performance in a more differentiated way. They showed the impact of either individual patient data or special combinations of patient data on classification performance, thereby providing a more detailed insight into the contribution of individual patient data.As the only ones, Pacheco and Krohling  performeLi et al  considerSriwong et al  addresseBonechi et al  considerAlthough the main evidence for a good diagnosis is still provided by the image input, all 11 publications indicate a possible benefit of integrating patient data in CNN classifiers, as illustrated in One focus of further research into combined CNN-based classifiers should be to render its classification process transparent, easy to understand, and applicable in a clinical setting. The 11 studies published so far have dealt with these aspects only marginally. Therefore, these issues need to be addressed in future studies to reliably reveal the potential of integrating patient data.No objective benchmarks exist in the field of integrating patient data into CNN-based classifiers. The heterogeneity of the studies conducted so far is substantial. This applies to the number and types of skin diseases or lesions to be classified, databases and data augmentation, CNN architectures, patient data, and fusion techniques. These aspects have a great influence on the way that the algorithm learns to diagnose the lesions in question and render it very difficult to reproduce and compare the approaches and results externally and independently. A way to solve this would be the more extensive use of external and publicly available data sets to objectively optimize the classification accuracy in an experimental setting. This needs to be done systematically in preparation for clinical trials that will be required to prove the algorithm’s generalizability and applicability in the clinic. In addition, the best way to handle missing data needs to be addressed.All presented studies lack an investigation of the impact of patient data individually and in combination on single-lesion classes. Both the fusion method and weight attributed to the patient data in addition to the biological significance itself may substantially influence the classification results. Further research should be dedicated to explaining the mechanisms by which the incorporation of these factors contributes to the decision making of the CNN-based combined classifier to render the results more transparent.As shown in All 11 studies published so far indicate that the integration of patient data into CNN-based skin lesion classifiers may improve classification accuracy. The studies mainly used patient data that were routinely recorded . Regarding the technical details, the main differences in the presented approaches occur in the fusing techniques. Further research should be dedicated to systematically evaluating the impact of incorporation of individual and combined patient data into CNN-based classifiers to show its benefit reproducibly and transparently and to pave the way for the translation of these combined classifiers into the clinic.