The rapid development of intelligent manufacturing provides strong support for the intelligent medical service ecosystem. Researchers are committed to building Wise Information Technology of 120 (WIT 120) for residents and medical personnel with the concept of simple smart medical care and through core technologies such as Internet of Things, Big Data Analytics, Artificial Intelligence, and microservice framework, to improve patient safety, medical quality, clinical efficiency, and operational benefits. Among them, how to use computers and deep learning technology to assist in the diagnosis of tongue images and realize intelligent tongue diagnosis has become a major trend. Tongue crack is an important feature of tongue states. Not only does change of tongue crack states reflect objectively and accurately changed circumstances of some typical diseases and TCM syndrome but also semantic segmentation of fissured tongue can combine the other features of tongue states to further improve tongue diagnosis systems' identification accuracy. Although computer tongue diagnosis technology has made great progress, there are few studies on the fissured tongue, and most of them focus on the analysis of tongue coating and body. In this paper, we do systematic and in-depth researches and propose an improved U-Net network for image semantic segmentation of fissured tongue. By introducing the Global Convolution Network module into the encoder part of U-Net, it solves the problem that the encoder part is relatively simple and cannot extract relatively abstract high-level semantic features. Finally, the method is verified by experiments. The improved U-Net network has a better segmentation effect and higher segmentation accuracy for fissured tongue image dataset. It can be used to design a computer-aided tongue diagnosis system. Nowadays, with the rapid development of mobile and wireless networking technologies, the Internet of Things (IoT) has contributed to Wise Information Technology of 120 (WIT 120). Researchers combine modern computer technology with modern medicine and traditional Chinese medicine theory to achieve computer-assisted diagnosis . MachineThe convolutional neural network plays an important role in the development of deep learning. It plays an irreplaceable role in improving the research level and practical performance of computer vision. In 2012, a historic breakthrough was made in the development of convolutional neural networks. Krizhevsky et al.  proposedImage segmentation can be understood as a method of outputting the category to which each pixel belongs. In object recognition, the number of input layer units is equal to the size of the sample image, and the number of output layer units is equal to the number of categories. During image segmentation, the number of input layer units is the same as object recognition, which is equal to the size of the sample image. But the number of output layer units is equal to the product of the sample image size and the number of categories. The output result of segmentation is the probability that each pixel belongs to each category. Image segmentation objects can be road scenes, face images, and medical images. In 2014, Long et al.  designedFCN is improved based on VGGNet-16  network.Tongue diagnosis is one of the distinctive diagnostic methods in traditional Chinese medicine for doctors to understand the physiological functions of the body and cause changes by observing the changes of the tongue and tongue coating of patients. It plays an important role in the clinical diagnosis of traditional Chinese medicine (TCM).However, the traditional tongue diagnosis is often based on the personal knowledge and experience of doctors, lacking objective evaluation standards. In addition, the valuable experience and tongue image data accumulated in the process of traditional tongue diagnosis cannot be retained scientifically and quantitatively, and the examination results of traditional tongue diagnosis cannot be described scientifically and quantitatively , which sAt present, more and more medical universities and pharmaceutical enterprises have begun to explore the development route of combining TCM tongue diagnosis with computer science and technology and have achieved a series of scientific research results –14. HoweAccording to the description in Discrimination of Tongue Image in Traditional Chinese Medicine , fissureIn this paper, we propose an improved fissured tongue image segmentation model based on the U-Net  model. ETo solve the above problems, we propose a method based on the combination of U-Net, GCN  module, and BR (Boundary Refinement) module . During We have made improvements based on the U-Net network. This method adds GCN and BR modules to the U-Net model. Because large kernels are vital to relieving the contradiction between classification and localization, the improved U-Net structure enables better classification and hence grants the possibility of building a deeper network with higher accuracy.We constructed a database of this study, which was confirmed by consistency assessment by two specialist physicians. We have enhanced the data (eight in the 2D case for the combination of flipping and rotation) to make it available for experiments.We demonstrate the performance of the proposed deep learning architecture by comparing it with the state-of-the-art segmentation methods. Our method outperformed most of the top-ranked methods in terms of segmentation accuracy.In summary, the main contribution of this study can be summarized as follows:This article is organized as follows. In As we previously stated, a convolutional neural network has made great contributions to the field of image segmentation. It has become a well-known standard to apply convolutional neural networks to segmentation models to realize feature extraction. For that reason, we will focus on these excellent image classification networks in this section.In 2012, Alex Krizhevsky proposed a very important convolutional neural network model called AlexNet . It won Inspired by AlexNet, Visual Geometry Group (VGG) is a CNN model introduced by the Visual Geometry Group (VGG) from the University of Oxford. VGGNet  exploredWhile VGGNet demonstrated that deepening model structures can help improve network performance, GoogLeNet  focused As the number of layers increases, deep networks will generally be more difficult to train. When some networks start to converge, they may also have degeneration problems, resulting in saturation of accuracy quickly. The deeper the level, the higher the error rate. Even more surprisingly, the higher error rate caused by this degradation is not overfitting but more layers have been added. In order to solve the degradation problem, a deep residual learning framework was proposed where hundreds of residual networks could be successfully trained. In contrast to a normal neural network, the residual network introduces a cross-layer connection, or shortcut connection, which constructs the residual module see .ResNet  structurThe encoder-decoder networks have been successfully applied to many computers vision tasks, including human pose estimation, object detection, and semantic segmentation . TypicalThe U-Net network which is a simple and effective network used in this paper is based on the full convolution network (FCN) network architecture. Its encoder-decoder structure and skip-connection are very classic design methods. The encoder part is responsible for feature extraction, and the decoder part restores the original graphics and gives the prediction of each pixel. Then, the deep information and shallow information are fused by corresponding pixel stitching.Semantic segmentation can be considered a per-pixel classification problem. There are two challenges in this task: (1) classification: an object associated with a specific semantic concept should be marked correctly; (2) localization: the classification label for a pixel must be aligned to the appropriate coordinates in the output score map. A well-designed segmentation model should deal with the two issues simultaneously .From the above two aspects, two design principles can be followed: (1) from the point of view of localization, a full convolution structure should be used rather than the full connection layer or global pooling layer; (2) from the point of view of classification, a larger convolution kernel should be used to make the pixel and feature map more closely combined and to enhance the ability to process different transformations. Moreover, too small a convolution kernel will cause a small receptive field. The network cannot cover large targets, which is not conducive to classification.In order to solve the problem mentioned above, Chao et al.  proposedAs is well known, the diagnosis of the fissured tongue is one of the important diagnostic methods in traditional Chinese medicine. After the recognition of fissured tongue, it is necessary to extract and analyze the characteristics of cracks in the tongue, which can assist doctors to judge the fissured tongue image and to diagnose people's health status through the fissured tongue image in order to achieve more effective treatment and disease prevention.Because the fissured tongue is an obvious crack groove on the tongue surface, the crack features are usually extracted by setting a threshold for gray and gradient. Wang et al.  proposedFurthermore, Chang et al.  applied Up to now, there is a litter of literature that introduces the extraction and analysis of some features of the fissured tongue , 33, butU-Net  network Compared with U-Net, the improved U-Net model uses pretrained GoogLeNet as the feature extraction network for image feature extraction. After feature extraction, feature information is added through the GCN module and BR module. Through this operation, the decoder can recover the image details and spatial dimension better by an upsampling operation. The improved U-Net network increases the size of the receptive domain by effectively increasing the size of the convolution kernel and improves the segmentation accuracy of small targets.The improved U-Net model still retains the encoder-decoder structure, as shown in On balance, according to Chao's analysis on classification and segmentation in images, we have known that large kernels are vital to relieving the contradiction between classification and localization. Following the principle of large-size kernels, we add the Global Convolutional Network (GCN) module in the U-Net structure. In addition, to further refine the object boundaries, we also add a Boundary Refinement (BR) block. Qualitatively, the GCN module mainly optimizes the internal structure of the network while the BR block increases performance near boundaries which can precisely capture the edge information of the image. The experiments in At present, there is no fully public dataset of tongue images. Therefore, a new dataset is proposed as a reference for fissured tongue images segmentation in this study. The fissured tongue was judged by strictly referring to the 12th Five-Year Plan textbooks for the higher education of Chinese medicine industry in China, such as The Tongue Image Discrimination of Traditional Chinese Medicine  and the Labelme_3.16.7 image annotation software based on Python was used to annotate the crack area of the tongue image. Each tongue image generates the corresponding annotated JSON file and then transforms the JSON file through the program to generate the corresponding semantic label image of the tongue image. In order to ensure the accuracy of data annotation, we also check and confirm the annotation. All the annotated data were randomly divided into training verification set and test set according to the ratio of 7 : 3, in which there were 223 training verification datasets and 93 test datasets. During the experiment, in order to avoid the overfitting problem, we amplified the data of the training verification data set through the geometric transformation of the image, including the data enhancement operations such as flipping transformation and random pruning. We balanced the number of sampled images and randomly divided them into training sets and verification sets according to the ratio of 7 : 3, among which there were 1413 training sets and 596 training sets. The data preprocessing results during the experiment are shown in −5. We use momentum 0.9, and the learning rate is set to gradually decrease with the increase of epoch to achieve a better training effect. We set the size of the input image to 256 × 256. The performance is measured by standard mean intersection over union (MIoU) [The experimental system including the pretraining network is based on the Pytorch framework, and all the experiments are completed on NVIDIA-GP 1060 (6G) graphics card, CUDA_verision 10.1, and Python version 3.6. During the training time, we train Adam with momentum. We use a minibatch size of 4 images and fixed initial learning rates of 1 × 10n (MIoU) .U-Net Network model is mainly composed of Encoder Network and Decoder Network. The encoder network converts the high-dimensional vector into the low-dimensional vector to realize the low-dimensional extraction of high-dimensional features. The encoder network captures more translation-invariant features through multiple maximum pooling operations, but it also loses more important segmentation bases such as the boundary information of the feature map. Therefore, different pretraining networks are used for feature extraction in the encoder part of the U-Net model, and the algorithm accuracy of the model in the process of training and verification is compared, as well as the segmentation effect in the test process. During the experiment, the hyperparameter settings of the network, such as the learning rate, are guaranteed to be the same. The comparative experimental results are shown in In In this section, we mainly discuss the experiments using deep separable convolution instead of ordinary convolution in the GCN module (labeled GCN_D). Since Sifre et al.  proposedIn this study, we conducted experiments on some outstanding models, such as FCN, DeepLab v3_plus , FRN 2424, SegNeIn this section, an experiment was described to validate the improved model and other models. The overall flow of the experiment is shown in In this experiment, the crack tongue image we selected is neither in the training dataset nor in the test dataset, which ensures the effectiveness of the verification experiment. In Tongue crack is an important feature of tongue states. Not only does change of tongue crack states reflect objectively and accurately changed circumstances of some typical diseases and TCM syndrome but also semantic segmentation of the fissured tongue can combine the other features of tongue states to further improve tongue diagnosis systems identification accuracy. Although computer tongue diagnosis technology has made great progress, there are few studies on the fissured tongue, and most of them focus on the analysis of tongue coating and body. Moreover, research fruits of semantic segmentation of fissured tongue would accelerate practical research on tongue crack in computerized tongue diagnosis and also possess potential application in medical clinic practice simultaneously.Furthermore, deep learning has had a tremendous impact on various fields in science . The focIn this study, we propose an image semantic segmentation model based on the U-Net model to detect fissured tongue images and compare the different pretraining networks for the encoder part and whether to add the GCN module and BR module. The improved U-Net model achieves 47.5% semantic segmentation accuracy of fissured tongue images, which is 15.1% higher than the FCN model, 30.5% higher than the SegNet model, 5.3% higher than the VGGNet_Unet model, and 1.8% higher than GoogLeNet_Unet model. At the same time, the improved U-Net model can capture the multiscale context information of the image under the multisampling rate, with high computational efficiency, and it can effectively complete the crack detection on the tongue image dataset. Although the improved U-Net model has been greatly improved to a certain extent, the experiment also has some limitations. It can be seen from the performance of the model on the test dataset that the model still needs to be improved. At the same time, the mapping from input to output in the learning process of a neural network is discontinuous . This diIn the future, the research on computerized fissured tongue diagnosis can be further improved and studied from the following aspects. (1) Feature extraction: defining and extracting more crack features is the top priority of computerized fissured tongue diagnosis in the future. In the future computer tongue diagnosis system, TCM tongue diagnosis can be assisted by using only the mapping relationship between the shape characteristics of tongue cracks and clinical diseases, which further simplifies the steps of computer tongue crack diagnosis. (2) Feature fusion: computer tongue diagnosis and computerized tongue crack diagnosis are combined with other diagnostic methods to promote the objective research of four diagnoses in traditional Chinese medicine. (3) System integration and testing: integrating the research results of computer fissured tongue diagnosis into the system and conducting large-scale clinical tests in some hospitals is a key step for computer tongue diagnosis technology to go to the market. Among them, the “AI Open Platform for Traditional Chinese Medicine Tongue Diagnosis” jointly developed by Anhui University of Chinese medicine and a company in Hefei is a typical case. The system integrates tongue image acquisition, tongue image diagnosis, operation interface, and system advantages. Compared with other traditional tongue diagnosis instruments, it is a relatively complete computer-aided tongue diagnosis system. Moreover, the application of computer tongue diagnostics to mobile is also the mainstream trend. In this process , we anal


Traditional clustering methods often cannot avoid the problem of selecting neighborhood parameters and the number of clusters, and the optimal selection of these parameters varies among different shapes of data, which requires prior knowledge. To address the above parameter selection problem, we propose an effective clustering algorithm based on adaptive neighborhood, which can obtain satisfactory clustering results without setting the neighborhood parameters and the number of clusters. The core idea of the algorithm is to first iterate adaptively to a logarithmic stable state and obtain neighborhood information according to the distribution characteristics of the dataset, and then mark and peel the boundary points according to this neighborhood information, and finally cluster the data clusters with the core points as the centers. We have conducted extensive comparative experiments on datasets of different sizes and different distributions and achieved satisfactory experimental results. In real-world, high-dimensional data lie close to low-dimensional structures corresponding to several classes or categories to which the data belong, and clustering is an efficient way to solve this problem. The goal of clustering is to unsupervisedly partition datasets into different classes or clusters so that the similarity of data objects within the same cluster is as large as possible, while ensuring that the differences between data objects in the clusters are also as large as possible. Among many clustering algorithms, the nearest neighbor-based clustering algorithm has been widely used in many fields, such as data mining, machine learning, image processing, and pattern recognition, and has achieved many good results.k-nearest neighbor (KNN) principle or the ε-nearest neighbor (ε-NN) principle, the neighborhood parameters k and ε are closely related to the distribution characteristics of the dataset, and the performance of the algorithm can change drastically depending on the set of different values.In nearest-neighbor-based clustering algorithms, it is a challenge for clustering algorithms to automatically infer the number of clusters and neighborhood parameters to reduce the dependence on a priori knowledge. In order to obtain more accurate clustering results, existing clustering algorithms generally need to specify the number of clusters in advance. In real-world applications of clustering methods, it is difficult to estimate the number of clusters accurately before the clustering algorithm is performed. On the other hand, either based on the For the above parameter selection problem, we propose an effective clustering algorithm ). SANBP algorithm is based on natural neighbor method and proposes logarithmic natural neighbors that are more applicable to the border peeling method. Therefore, the algorithm solves the neighborhood parameters selecting problem, while adaptively obtaining different neighborhood sizes for each data point. In the process of border peeling, the algorithm can get more accurate initial boundary points according to the logarithmic natural stable state and also can obtain more accurate distribution information during the peeling process by the adaptive neighborhood. Therefore, the algorithm can obtain clustering results that are more consistent with the actual distribution without the need to set prior parameters artificially. We propose a more stable concept of logarithmic stable state based on the natural neighborhood.A self-adaptive neighborhood border peeling clustering algorithm is proposed. This algorithm is able to perform adaptive clustering on different shaped datasets, substantially improving the adaptive nature of the algorithm.SANBP algorithm is capable of clustering datasets with different densities and distributions and adaptively determines the number of clusters and neighborhood sizes.The main contributions of this paper are as follows:ε-NN [The nearest neighbor idea is widely used in clustering algorithms, and almost all clustering algorithms use the nearest neighbor idea in one way or another, and their core methods are based on KNN and ε-NN . Both meε-NNN the biggest difference is natural neighbors do not need to set or fix a certain parameter k or. As an alternative to k, natural neighbors are calculated adaptively, and the number of natural neighbors of each data object in the dataset is not the same, so the natural neighbor is a scale-free neighbor concept [Natural neighbor (NaN) is a new concept of neighbor, which arises from the perception of objective reality , 3. Natu concept .k-nearest neighbor (RKNN) [Nowadays, the idea of incorporating the concept of natural neighbors into clustering algorithms has had many achievements and has good experimental results in various fields, such as hierarchical clustering algorithms based on noise removal , adaptivr (RKNN) search al1-minimization by a new projection neural network to solve the sparse signal reconstruction problem in sparse subspace clustering [As the focus on data analysis has gradually increased, more and more clustering algorithms have been proposed. Among them, the core idea of the division-based clustering algorithm is to divide the dataset into several classes according to the global optimization criteria. However, due to the limitation of the global optimization function of the division-based clustering algorithm itself, it is not applicable to many problems such as stream-shaped and concave datasets. The density-based clustering algorithm is theoretically able to apply to any shaped dataset, but it is more sensitive to parameters and is not applicable to datasets with high density or complex stream shapes . The corustering .Among the above clustering algorithms, the density-based clustering algorithm is closer to the daily application scenarios in terms of clustering results, and researchers have proposed a large number of improved algorithms for different application areas. Rodriguez proposed a novel CFDP clustering algorithm in density clustering algorithm, which can describe the density peak clustering more accurately and quickly . Later, k, which makes the process of boundary points from boundary points to core points in the process of iterative peeling have the possibility of producing deviations, which affects the clustering results and even shows extremely unreasonable data cluster partitioning in some datasets. Based on the above problems, this paper proposes a new algorithm combining natural neighbors and border peeling clustering algorithm, SANBP, which can retain the advantages of the original border peeling clustering but also make up for the shortcomings of the border peeling clustering algorithm that always has the neighborhood parameter and can be used on datasets of different shapes without setting the neighborhood parameter. It can obtain clustering results that match the characteristics of data distribution adaptively without setting the neighborhood parameters on different shaped datasets.However, the initial boundary points selected by the border peeling clustering algorithm on different shaped datasets are extremely dependent on the selection of the neighborhood parameter The natural neighbors used in this paper are a scale-free concept, and natural neighbors do not require parameters in the selection of neighbors.The following is a precise description of natural neighborhood method through the definition of the relevant concepts.X={x1, x2, x3,…, xn} where n is the total number of points in the dataset X.Dataset When the dataset is in a natural stable state, the points that are neighbors of each other are natural neighbors of each other.r increases from 1 to λ.The natural neighbor searching process reaches natural stable state only if all points have at least one mutual neighbor, when the searching round X is in the natural stable state, the natural neighborhood eigenvalue is the current searching round λ. The natural eigenvalue is the maximum number of cycles in the actual running process, and λ reflects the distribution pattern of the dataset.When the dataset X and the artificially set neighborhood parameter k, the neighborhood parameters of KNN and RKNN are first adopted by the natural eigenvalues; for any point xiεXt)((xi) denotes the set of k-nearest neighbors; the RKNN of xi is defined asBy given the dataset xi ∈ Xt) returns the r-th neighbor of point xi in k − d tree T, NaN_Edge means the edge set of natural neighbor, each edge connects two natural neighbor points, and NaN_Num (xi) means the natural neighbor number of point xi. The natural neighbor search algorithm first finds one neighbor for each data point, then calculates the number of points in the dataset whose mutual neighbor point is zero, and then finds two neighbors for each data point to calculate the number of points in the dataset whose mutual neighbor point is zero. The algorithm keeps increasing the number of neighbors for each data point and then calculates ξ as the threshold value. If the number of points with zero mutual neighbors does not change in ξ round, the algorithm determines that the current search has reached the logarithmic natural stable state.Here, findKNN (σj to construct the function f:(similarity metric). To estimate distances between points, we use Euclidean distance as the dissimilarity measure, and apply a Gaussian kernel bit). After determining the similarity metric, we associate each point with a density influence value and use the number of natural neighbors to generate the density impact value k = 20 with self-adaptive neighborhood. Secondly, the neighborhood parameters obtained by SANBP algorithm can better adapt to the distribution pattern of the dataset and can establish good initial boundary points in the process of iterative peeling of boundary points. In the process of boundary point peeling, the establishment of initial boundary points has a great influence on the final clustering effect of different shaped datasets. The BP clustering algorithm uses inherent neighborhood parameters, and the adaptive ability of initial boundary point establishment is obviously insufficient when facing different shaped datasets. SANBP algorithm solves this problem well and demonstrates its superiority graphically in the subsequent experiments.The core steps of the whole algorithm consist of the following two parts: 1) the adaptive dataset reaches logarithmic natural stable state and generates logarithmic natural eigenvalues; 2) the natural neighbor numbers of points are used to establish reasonable initial boundary points and perform border peeling clustering. The steps are described in detail in the pseudocode of Algorithms  the adap the natuk. Therefore, the experiments on the boundary peeling clustering algorithm for different shaped datasets in this paper still retain the original fixed parameters. In contrast, SANBP algorithm proposed in this paper does not use fixed neighborhood parameters, and the variable neighborhood obtained by adaptive data features can make the initial boundary points in border peeling better distributed, and it can show good results on different shaped datasets.In order to evaluate the natural neighbor-based border peeling clustering algorithm to be more generalizable, six datasets with different shapes  are shown in To demonstrate that the adaptive ability of the method in this paper is still very competitive on unsupervised datasets, we test the BP algorithm and our SANBP algorithm using artificial data . On thisThe experimental results on the datasets data_DBSCAN and artificial_data are shown in Experiments show that in unsupervised datasets with a large number of outlier points, SANBP algorithm clusters much better than the BP algorithm in several areas such as the number of clusters and the quality of clusters.To further validate the performance of our technique on large datasets, we generated large sets by extracting feature vectors generated by convolutional neural networks (CNN) that were trained separately on MNIST . MNIST iAs shown in From the experimental results in O(n  log  n).The SANBP algorithm implemented in this paper is written using the Python language, and the time complexity of the BP clustering algorithm is In this paper, we propose a self-adaptive neighborhood border peeling clustering algorithm (SANBP), which can solve the problem of parameter adaption such as the number of clusters and neighborhood parameters in clustering algorithms. Firstly, SANBP algorithm adapts to datasets of different shapes through a robust natural search algorithm. Secondly, SANBP generates self-adaptive neighborhood with the distribution pattern of the dataset and finally uses self-adaptive neighborhood to replace the fixed neighborhood parameters. When the dataset reaches the logarithmic natural stable state, the self-adaptive neighborhood of each point in the dataset is obtained simultaneously and reflects the distribution of the data. SANBP algorithm can establish more ideal initial boundary points according to the different shapes of the dataset, which makes the association between boundary points and core points more reasonable in each iteration of border peeling and finally results in good clusters.Unlike other clustering algorithms, the method in our paper uses self-adaptive neighborhood and can generate high-quality initial boundary points according to different shaped datasets. The experiments also show that the initial boundary point distribution has an important impact on the final clustering effect. In the whole experiment, whether on the original experimental dataset of BP clustering algorithm or on a large number of other datasets with different shapes, our algorithm is more competitive than the original BP clustering algorithm.Although SANBP algorithm has achieved satisfactory results in terms of parameter adaptation and clustering results, it still has room for further improvement. In the follow-up work, we will focus on the improvement of the algorithm to make it better adapted to the performance in semisupervised datasets, and the algorithm will focus more on the application to specific practical problems for generalization. In terms of neighbor relationship, we will explore the idea of optimizing natural neighbors for problems such as stream data overlap and automatic data labelling and try to better apply the natural neighborhood graph of natural neighbors to the clustering algorithm to further improve the search efficiency of the algorithm.


A plate-like structure is located posterior to the portal vein system, between the pancreatic head and roots and/or branches of two major arteries of the aorta: the celiac trunk and superior mesenteric artery. We aimed to clarify the distribution and components of this plate-like structure. Macroscopic examination of the upper abdomen and histological examination of the plate-like structure were performed on 26 cadavers. The plate-like structure is connected to major arteries  and the pancreatic head; it contains abundant fibrous bundles comprising nerves, vessels, collagen fibers, and adipose tissue. Furthermore, it consists of three partly overlapping fibrous components: rich fibrous bundles (superior mesenteric artery plexus) fused to the uncinate process of the pancreas; fibrous bundles arising from the right celiac ganglion and celiac trunk that spread radially to the dorsal side of the pancreatic head and superior mesenteric artery plexus; and fibrous bundles, accompanied by the inferior pancreaticoduodenal artery, entering the pancreatic head. The plate-like structure is the pancreas–major arteries  ligament (P–A ligament). The term “P–A ligament” may be clinically useful and can facilitate comprehensive understanding of the anatomy surrounding the pancreatic head and provide an anatomical basis for further pancreatic surgery studies. Cancer of the pancreatic head often invades the superior mesenteric artery (SMA)  because it lies between the pancreas and major arteries . The present study aimed to clarify the distribution and components of this plate-like structure and advocate clinically useful terminology. We believe that this term will contribute to further discussions of the anatomy and pancreatic cancer treatments.The study has been approved by the Board of Ethics of Tokyo Medical and Dental University  and has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The work was performed in agreement with the Japanese Act on Body Donation for Medical and Dental Education 1983.The cadavers used in the present study were donated to the Department of Anatomy at Tokyo Medical and Dental University in Japan. All of the donors voluntarily declared before their deaths that their remains would be donated as materials for education and study. This voluntary donor system of cadavers is applied throughout Japan, and our study was in complete compliance with the current laws of Japan. All cadavers were fixed by arterial perfusion with 8% formalin and preserved in 30% alcohol.Twenty-two cadavers  were used for macroscopic examinations of the whole upper abdomen. Each upper abdomen, including the pancreas, stomach, duodenum, jejunum, liver, spleen, aorta, inferior vena cava, and adjacent tissues, was obtained en bloc from the cadavers. Seven upper abdomens were dissected from the ventral side. The common hepatic artery was cut and removed along with its branches to identify the portal vein (PV). The pancreas was cut on the right borderline of the PV and superior mesenteric vein (SMV), and the body and tail of the pancreas were removed to examine the PV, SMV, and splenic vein. Subsequently, the PV system was removed to observe the plate-like structure posterior to it. The loose connective tissue was precisely removed from the plate-like structure for observation. Two other upper abdomens were dissected from the dorsal side. The CeT and SMA were cut at the root, and the aorta was removed. After removal of the inferior vena cava, the plate-like structure between the pancreatic head and the CeT/SMA was observed along with the inverted right celiac ganglion. The loose connective tissue was precisely removed to observe the components of the plate-like structure. The other 13 upper abdomens were dissected from both the ventral and dorsal sides using the aforementioned procedure.Two cadavers  were used for macroscopic examinations of specimens isolated en bloc. The limited region of the head of the pancreas was obtained en bloc with the surrounding structures, including the duodenum, CeT, SMA, PV, and SMV. Specimens were dissected from both the ventral and dorsal sides. The PV and SMV were removed to show the plate-like structure between the pancreatic head and the CeT/SMA. Subsequently, the loose connective tissue was precisely removed so that the components of the plate-like structure could be observed. Thereafter, non-arterial tissues were removed to reveal arterioles.The fibrous bundles of the plate-like structure located between the pancreatic head and the CeT/SMA were obtained from the cadaveric specimen after dissection to identify the type of tissue included in these fibrous bundles. The fibrous bundles were fixed in 10% formalin, dehydrated, and embedded in paraffin. They were sectioned into 5-μm-thick specimens and stained with hematoxylin and eosin.Additional two cadavers were used for histological examinations of the transverse section. Their upper abdomens were obtained en bloc, frozen at − 80 °C, and cut into 8-mm-thick transverse sections from which small blocks were obtained for histological analysis. The blocks were fixed in 10% formalin, dehydrated, and embedded in paraffin. Then, 5-μm-thick histological sections were created and stained with hematoxylin and eosin.We examined the anatomy of the pancreatic head and surrounding structures from the viewpoint of pancreaticoduodenectomy Fig. a. The paThe plate-like structure included abundant fibrous tissue composed of nerves, vessels, collagen fibers, and adipose tissue. After removal of the loose connective tissue from the plate-like structure, abundant fibrous bundles were revealed Fig. a. Most oThe transverse histological section exhibited nerves, vessels , collagen fibers, and adipose tissue between the uncinate process of the pancreas and SMA Fig. . Large aMacroscopic examination of the dorsal side showed fibrous bundles of the plate-like structure. The right celiac ganglion was located to the right of the CeT, and fibers protruded near the roots of the CeT and SMA Fig. a. The roThe isolated en bloc specimens were dissected from the ventral and dorsal sides, and finer structures were observed. The plate-like structure connecting the uncinate process of the pancreas and CeT/SMA was observed on the posterior side of the PV and SMV Fig. a, b. ThiWhen observed from the dorsal side of the same specimen, the plate-like structure spread continuously from the tissue around the right celiac ganglion, CeT, and SMA to the pancreatic head Fig. a. The fiThe plate-like structure was observed on the dorsal side of the PV system and was connected to the uncinate process and CeT/SMA Fig. . We propPreviously, the structure between the pancreatic head and the CeT/SMA has been given two main descriptions: “pancreatic head plexus” and “mesopancreas.” Nerves running between the pancreatic head and the CeT/SMA have attracted attention, and the concept of the pancreatic head plexus has been proposed (Yoshioka and Wakabayashi As described previously, there is no consensus regarding the anatomy and terminology of the plate-like structure; therefore, we propose that the term “P–A ligament” should be used because it allows for a comprehensive understanding of the anatomy. We observed the plate-like structure in its intact state and analyzed its components. The precise macroscopic examination revealed the running form of the fibrous bundles. To the best of our knowledge, this is the first study to describe the form of the fibrous bundles that run obliquely between the lower part of the uncinate process and the dorsal side of the distal SMA. Furthermore, the histological examination clarified that the fibrous bundles included collagen fibers, adipose tissue, nerves, and vessels. The term “ligament” is derived from the Latin “ligamentum,” which means a connecting or unifying bond. Therefore, the term “P–A ligament” is a more comprehensive description than those previously used Table .Table 1DThe running form of the fibrous bundles of the P–A ligament differed depending on their location. This difference in fiber directions can be understood by the positional relationship between the pancreatic head and the arteries. The running pattern of fibrous bundles in the ventral part of the P–A ligament seems to be strongly influenced by the SMA. The fibrous bundles do not run transversely and do not flow vertically into the uncinated process; instead, they mainly flow superiorly to inferiorly along the SMA. Therefore, it may be appropriate to consider that the uncinate process is incorporated in the flow of the SMA plexus. However, the radial running pattern of the fibrous bundles in the deep part of the P–A ligament seems to reflect the origin point localized near the right celiac ganglion and the roots of the CeT and SMA. The fibrous bundles exhibit a radial running pattern, probably because the fibers originating from the dorsal location spread widely throughout the dorsal side of the pancreatic head. Furthermore, fibrous bundles run along the IPDA. This running pattern suggests that the fiber arrangement is strongly affected by the arteries.The P–A ligament is the plate-like structure continuously connected to the pancreatic head and CeT/SMA that contains fibrous bundles including collagen fibers, adipose tissue, nerve fibers, and small vessels. It comprises three components—the ventral part, dorsal part, and IPDA part—which have characteristic running patterns depending on their location. The “P–A ligament” may be a clinically useful term that allows for a comprehensive understanding of the anatomy around the pancreatic head and that is expected to provide an anatomical basis for further discussions of pancreatic surgery.


Cell monolayers, including endothelial and epithelial cells, play crucial roles in regulating the transport of biomolecules to underlying tissues and structures via intercellular junctions. Moreover, the monolayers form a semipermeable barrier across which leukocyte transmigration is tightly regulated. The inflammatory cytokines can disrupt the epithelial and endothelial permeability, thus the reduced barrier integrity is a hallmark of epithelial and endothelial dysfunction related with numerous pathological conditions, including cancer-related inflammation. Therefore, the assessment of barrier function is critical in in vitro models of barrier-forming tissues. This review summarizes the commercially available in vitro systems used to measure the permeability of cellular monolayers. The presented techniques are separated in two large groups: macromolecular tracer flux assays, and electrical impedance measurement-based permeability assays. The presented techniques are briefly described and compared. The epithelial and endothelial tissues constitute the barriers of fundamental physiological significance. They provide the compartmentalization of human organisms in order to guarantee the distinct conditions in separated compartments . These dIn our recent study, we utilized the F11R/JAM-A peptide antagonist P4D that disturbs the homophilic interactions between F11R/JAM-A molecules located on the surfaces of two different, adjacent cells and thus inhibits the tight junctions formation to demonstrate the role of F11R/JAM-A in breast cancer metastasis . We haveThe barrier dysfunction is characterized the most frequently by two major parameters, including the macromolecular permeability, which is a direct indicator of the solute flux across the cellular barrier, and electrical resistance, which reports the tightness of cellular monolayer for ion flow . The mac2 atmosphere to reach confluency, and subjected to a suitable treatment, while the non-treated cells serve as a control sample. The cells can be seeded directly on PC, PE or PET membranes or on such membranes coated with collagen type I, collagen type IV, gelatin, fibronectin or a mixture of collagen and fibronectin , while Δc is the initial concentration gradient between both compartments expressed in [mol/cm3]. Consequently, the unit of the permeability coefficient EP is [cm/s]. Thus, EP represents the permeability of the filter covered with the cell monolayer and has to be corrected for the permeability of the cell-free filter membrane FP. For such a correction, the experimentally obtained permeability coefficient EXPP is corrected by the permeability of the cell-free filter FP according to the following equation to estimate the true EP value of the cell monolayer [After incubation in darkness (about 1 h), the buffer from the wells outside the inserts was transferred into the wells of a black 96-well plate and the fluorescence was measured using a microplate reader at 485–492 nm of excitation and 517–520 nm of emission wavelengths, when FITC was used as a label. The intensity of fluorescence was directly proportional to the barrier permeability. Macromolecule diffusion across the epithelial or endothelial monolayers can also be quantified using enzymatic markers, including horseradish peroxidase . MoreoveThe archetype of the transwell system was initially developed for growing confluent primary cultured monolayers of guinea pig gastric mucous cells and described for the first time in 1985, but the insert contained a central hole with a collagen gel instead of a micropore membrane filter . This syThe precursory paper by Cooper et al. contains the detailed morphological analysis of endothelial monolayer grown on the surface of membrane filter . The anaWhile preparing the conditions for the tracer flux assay, several critical points must be taken into account . These fThe use of macromolecular tracer compounds can interfere with the studied transport process and can alter the barrier integrity. Moreover, following the treatment with the chemicals used for a tracer assay, the cells cannot be used for further experiments. Thus, the noninvasive techniques were applied instead for the continuous monitoring of the barrier permeability .t, the reciprocal of the sum of the permeabilities of all ions of the adjacent bath solution times their respective concentrations [R is the resistance , U is the voltage  and I is the current . The evaluation procedure includes the blank resistance measurement (BLANKR) of the microporous membrane only (without cells) and the measurement of the resistance across the cell monolayer on the membrane filter TOTALR, that includes the cell layer resistance TEERR, the resistance of the culture medium MR, the membrane insert resistance IR, and the resistance of the electrode medium interface EMIR. The cell-specific resistance TISSUER can be obtained as:The permeability of epithelial and endothelial cell monolayers is commonly analyzed by the noninvasive measurements of transepithelial/endothelial electrical resistance (TEER). In electrophysiology, this electrical resistance of cellular barrier is represented by Rtrations . The eletrations . The claTEER values are reported in units of Ω × cm2 (REPORTEDTEER) and calculated as:AREAM is the area of the semipermeable membrane (measured in cm2). The TEER value is inversely proportional to the permeability of the epithelial and endothelial cell monolayer.TEER quantifications can be performed with the use of several commercially available systems, which are described below. These systems guarantee the noninvasive measurements and can evaluate the barrier integrity of epithelial or endothelial cells at various stages of differentiation and growth [d growth .The most widely used and commonly distributed TEER measurement instrument is called the epithelial volt–ohm meter . The newest version of the instrument is called EVOM3. The EVOM3 system with all the associated components is presented in Before the measurement procedure, the EVOM3 device has to be calibrated with the use of the 1 kOhm resistor—the display has to show the reading of 1000 Ohms B. OtherwTM culture cup.In practice, the correct positioning of the STX2-plus electrode during the measurements is difficult. This problem is omitted when the measurements are performed with the use of the Endohm electrode chamber F. This dThe changes in the TEER of a cellular barrier can be monitored in real-time. The devices that enable real-time measurements are powered with the alternating current from the mains, thus they in fact measure the barrier’s impedance, which is defined as the resistance to alternating currents (ACs). It is a complex physical quantity that is dependent on the AC frequency, since the latter determines the current pathway across the cell layer: transcellular vs. paracellular . The mac2, or N2. The gassing oxygenates the solution contents and stirs the liquid to provide the complete convection, the so-called ‘bubble lift’. The U-shaped tube prevents the damage caused by the bending of the tissue, since it balances the hydrostatic pressure on both sides of the chamber. During the experimental procedure, the substances are added to one or both sides of the tube in a sequential manner, thus they are present in the solution until the end of the experiment.The Ussing chamber system was originally designed by the Danish zoologist Hans Ussing in the 1950s, to study the net transport of ions through the skin of a frog . It is aChambers are made of Teflon or Lucite and are available in different sizes and shapes. The most common application of the Ussing chamber is for the cell cultures grown on permeable supports, including the transwell inserts, those of which very often are available in the shape that matches the Ussing chamber half-cells.The procedure of an experiment that utilizes an Ussing chamber starts with the preparation of the chambers and solutions. Then, the system without any cell/tissue culture  is flushed with bath solution. The temperature is adjusted to the desired value when there is no liquid leakage from the system. Next, the current and voltage electrodes are inserted into the half-cells. Depending on the electrode type  the electrode resistance can be non-constant, which can result in asymmetries. Therefore, the system is tested for noise and offset voltages as soon as the electrodes are connected to the current/voltage pulse injectors and the volt-/ammeter, respectively. The resistance of the empty chambers can be estimated by turning on the current/voltage pulses. This is necessary for the calculation of resistance and currents. Some devices enable to cancel out the resistance of the solution and the offset voltage generated by non-equilibrated electrodes. This should be performed before the cell/tissue culture or an empty (blank sample) filter is inserted. Then, the chambers are disconnected from the solution supply and the cell/tissue can be mounted. The recording begins after the system is reassembled. Immediately after the cell/tissue culture insertion, the values of all electrical parameters  are oscillating. This can result from the mechanical stress on the sample or from the accidental stimulation. Therefore, the tissue should be allowed to recover for between 10 and 40 min before the experimental treatment. When a stable baseline is obtained, the data acquisition system is switched to a higher time resolution, the initial procedures are completed and the actual experiment can be started.The Ussing chamber technique has both its strengths and limitations . One of This experimental technique was applied in a wide array of research areas, including the study on the mechanisms of diarrheal disease in the setting of Salmonella infection and on the regulation of epithelial chloride secretion by the epidermal growth factor receptor , and the® Real-Time Cell Analysis (RTCA) system . The system consists of three parts: an RTCA instrument, a personal computer-based RTCA control unit and a single-use electronic multiwall plate. The RTCA instrument is available in three versions: RTCA-SP (Single Plate), RTCA-DP (Double Plate), or RTCA-MP (Multiple Plate). The following single-use multiwall plates, termed as E-Plates, are compatible with the RTCA system (16-well ‘E-Plate VIEW 16 PET’ plates or 96-well ‘E-Plate VIEW 96 PET’ plates).The real-time impedance measurements during the cell monolayer development can be performed with the ACEA xCELLigenceCI) values. CI is a dimensionless parameter based on relative impedance changes referenced to the values of the cell-free electrode at each frequency:N is the number of the frequency points at which the impedance is measured, whereas cellR(fi) and bR(fi) are the frequency-dependent electrode resistances with cells present and without cells, respectively [CI is a quantitative measure of cell status in a well, including the cell number, cell viability, adhesion degree and morphology. Although CI is measured at three frequencies, only the most sensitive readings are returned by the software as a function of time and reported as the final results. Since the frequencies are close together, the differences between the CIs at different frequencies are small but different. In general, CI values rise with the increasing coverage of the electrode with cells, which is caused at an early stage by cell sedimentation and, later on, by cell proliferation. In practice, the ‘normalized cell index’ at a certain time point is used for most experimental settings. Its values are acquired by dividing the CI value by the value at a reference time point.The typical experimental procedure is performed as follows. The gold-film electrodes deposited on the bottom of the E-plate electrode arrays are equilibrated with a culture medium overnight. Subsequently, the medium is aspirated and replaced by a volume of fresh medium for impedance background measurements. The cells are seeded at a specified density in a suitable cell culture medium with supplements. Usually, the 30 min time is required to allow the cell sedimentation at room temperature. Afterwards, the plates are locked into the RTCA instrument for continuous recording of the impedance changes at three different alternating current (AC) frequencies , which are expressed as the cell index  and consists of an ECIS® instrument that measures complex impedance at variable frequencies, a 16-well array holder that is stored inside the cell culture incubator, and a computer for instrument control and data storage. The growth surface of disposable 8-well electrode arrays can be coated with an adhesive protein. Subsequently, the epithelial/endothelial cells are seeded on the electrodes. Then, the 8-well arrays are placed inside the ECIS® device for impedance monitoring. The measurements can be performed at a desired AC frequency within a range of 10 Hz–100 kHz. Typically, the exposure of cell monolayers to a substance of interest is performed after reaching constant impedance values , which indicates the cell layer confluence. The impedance values of each well are then recorded at the desired time intervals during the entire analysis. The impedance values are usually presented as recorded along the time course of the experiment normalized to the impedance values immediately before the addition of modulators or test compounds. The main advantage of the ECIS system over the RTCA xCELLigence technique is that the ECIS measurements can deconvolute data into biological parameters, while the xCELLigence readouts measure only the relative total impedance values [Another example of the laboratory equipment enabling the monitoring of cell monolayer impedance in real-time is the electric cell-substrate impedance sensing (ECIS) system . The come values .ECIS enables the study of cell proliferation, migration, differentiation, toxicity and monolayer barrier integrity. Similar to the RTCA technique, the ECIS system is used for hepatological research . MoreoveThe two major parameters of the epithelial/endothelial barrier dysfunction are quantitated by the two distinct methodological groups. The macromolecular permeability characterizes the flux of high molecular weight biomolecules across the cellular barrier, including proteins, peptides, lipids, polysaccharides and nucleic acids. This parameter can be determined with the use of fluorescently labeled macromolecular tracers, with the 3-kDa or 40-kDa dextran as the most common example. The transendothelial/transepithelial electrical resistance (TEER) is a measure of the tightness of the cellular monolayer for the flow of small inorganic molecules, mostly ions.These two methodological groups for the assessment of the cell monolayer permeability are related with two different cellular parameters, which are regulated independently. Therefore, a researcher cannot simply replace a macromolecular permeability assay with a TEER measurement. Instead, these two techniques should be used simultaneously to obtain the complex characterization of the observed effect of an experimental setup on the epithelial/endothelial barrier. On the other hand, the resistance/impedance measurements that characterize the ion flux across the cellular barrier can be performed as the end point measurements using the EVOM system. Moreover, the impedance fluctuations can be observed in real-time by the implementation of the Ussing chamber, or xCELLigence and ECIS systems, which provides the higher scientific significance than the end-point measurements by the EVOM instrument. However, in the case of the Ussing chamber, only one set of experimental conditions can be tested for each experiment, whereas the last two provide the simultaneous targeting of multiple analytes. On the other hand, it is a challenge to purchase the xCELLigence and ECIS systems due to their high prices. The Ussing chamber is a much more complicated system, both in terms of the experimental manipulations and measurement procedures, than EVOM. Thus, for simple, basic analyses, and when the high costs make the purchase impossible, the EVOM system can be the methodological approach of choice for the ion flux characterization. Otherwise, the xCELLigence and ECIS systems are highly recommended, whereas the Ussing chamber system has an additional possibility to be applied not only for in vitro experiments, but also for ex vivo and in vivo studies.


The task of designing an Artificial Neural Network (ANN) can be thought of as an optimization problem that involves many parameters whose optimal value needs to be computed in order to improve the classification accuracy of an ANN. Two of the major parameters that need to be determined during the design of an ANN are weights and biases. Various gradient-based optimization algorithms have been proposed by researchers in the past to generate an optimal set of weights and biases. However, due to the tendency of gradient-based algorithms to get trapped in local minima, researchers have started exploring metaheuristic algorithms as an alternative to the conventional techniques. In this paper, we propose the GGA-MLP (Greedy Genetic Algorithm-Multilayer Perceptron) approach, a learning algorithm, to generate an optimal set of weights and biases in multilayer perceptron (MLP) using a greedy genetic algorithm. The proposed approach increases the performance of the traditional genetic algorithm (GA) by using a greedy approach to generate the initial population as well as to perform crossover and mutation. To evaluate the performance of GGA-MLP in classifying nonlinear input patterns, we perform experiments on datasets of varying complexities taken from the University of California, Irvine (UCI) repository. The experimental results of GGA-MLP are compared with the existing state-of-the-art techniques in terms of classification accuracy. The results show that the performance of GGA-MLP is better than or comparable to the existing state-of-the-art techniques. Artificial Neural networks (ANNs) are computing models inspired by the biological nervous system. An ANN consists of an interconnected network of nodes called artificial neurons which are organized in the form of layers, namely, input layer, hidden layers, and output layer . A set oIn this paper, our focus is on multilayer perceptron (MLP) which is a multilayer feedforward neural network. Classification using MLP is basically a two-step process. The first step is the learning (training) phase in which a classifier is built to describe a predetermined set of data classes for a given dataset (training data). In the second step, the model which has been built in the training phase is used for the classification of the unclassified data (test data) for estimating the accuracy of the classifier. During the learning phase, MLP learns by adjusting synaptic weights and biases iteratively in an attempt to correctly predict the class labels of the input data. The process of weight and bias update continues until the acquired knowledge is sufficient and the network reaches a specified level of accuracy; i.e., a predefined error measure is minimized, or the maximum number of epochs is reached . After tDespite the existence of a large number of learning algorithms, researchers continue to apply new optimization techniques like multimean particle swarm optimization (MMPSO) , whale oA hybrid learning algorithm, GGA-MLP, that integrates greedy techniques with GA is proposed to train MLPGGA-MLP is evaluated and compared with existing state-of-the-art algorithms on 10 datasets of different complexitiesGA is an evolutionary algorithm (EA) and is one of the most widely investigated algorithms among the metaheuristic algorithms in designing neural networks. Over the years, GA and its variants have been successfully applied in several domains for ANN weight –20, topoThe paper is organized as follows. Related work is presented in In conventional methods, backpropagation (BP) is the most widely used algorithm to train multilayer feedforward networks (MLFFNs). BP uses a gradient descent rule that tries to minimize the error of the network by moving in a direction opposite to that of the gradient of the error function. However, BP has certain limitations. It has a tendency to converge toward the local optima, as it is good only at exploiting the current solution, which may result in unsatisfactory classification accuracies. It also has slow convergence as well as scaling problems . To overDue to the global search capabilities of metaheuristic algorithms, they are being widely used by researchers to generate optimal weights and biases in MLP. In –21, GA wSometimes, metaheuristic algorithms suffer from premature convergence. To overcome the problems faced by conventional methods and metaheuristic algorithms, hybrid approaches were proposed by researchers. In , 39, GA Genetic algorithm (GA) is a metaheuristic algorithm proposed by Holland . This alIn this section, we present our proposed approach GGA-MLP which applies a greedy GA to generate an optimal set of synaptic weights and biases of MLP, keeping the architecture and activation function fixed. The various steps of GGA-MLP are explained below.An important aspect that needs to be considered during the design of GGA-MLP is the representation of the possible solutions in the search space in the form of chromosomes and the encoding scheme used to encode the chromosomes. In GGA-MLP, each chromosome represents a candidate MLP. A chromosome is basically divided into different segments, where each segment contains the encoded weights between two layers , hidden-output) and the last segment contains the encoded bias values for the MLP. Chromosome encoding for an MLP having two hidden layers is shown in n input nodes, m hidden layers with h1,  h2,   … .., hm hidden nodes in each hidden layer, and k output nodes, then the length of the chromosome will be calculated usingAs it is clear from MLPj| 1 ≤ j ≤ PS where PS is the population size. Each MLPj| 1 ≤ j ≤ PS in the population is associated with a fitness value which is the measure of its quality. In our case, mean square error (MSE) is chosen as the fitness function. To calculate the fitness of an MLP, the training data sample is made to run on it and the mean square error value is calculated usingyk is the actual output, n is the number of samples in the training dataset. This process is repeated for each MLPj. The goal of GGA is to find an MLP that minimizes the objective function f | f :  MLPj⟶R+, where R+ represents a set of real numbers. The objective function f can be calculated using , the initial population plays a major role in determining the quality of the final solution as well as the convergence speed . SeveralCmean is an indicator of the ideal gene value which minimizes the MSE. In order to move toward a global optimum, this mean chromosome is used as a comparison parameter against individuals having low fitness values in the population. From the top 30% chromosomes, a chromosome Cbf is selected randomly for crossover. Another parent Chs for crossover is chosen from the worst 30% individuals in such a way that it can contribute the most toward the fitness of chromosome Cbf. The method of selection of Chs is shown in Cbf and Chs, MBC is performed by exchanging the genes of Cbf and Chs as shown in After population initialization, the next step is the application of various operators such as selection, crossover, and mutation repeatedly to obtain an MLP with optimal weights and biases. Maintaining diversity is important, but sometimes it is also vital to retain the best individuals of one generation into the next. GGA-MLP uses elitism to the transfer best chromosome(s) from one generation to another. Crossover and mutation are performed to generate offspring by selecting chromosomes from the current generation. The crossover operator takes two chromosomes and combines them to produce new offsprings. It is based on the idea that the exchange of information between good chromosomes will generate even better offsprings. Extreme care should be taken while performing selection and crossover operation as it may reduce the genetic diversity, which may ultimately lead to premature convergence. To avoid premature convergence, we present a crossover technique, known as mean-based crossover (MBC), that aims at improving the fitness of the top individuals of the population with the help of the worst members of the population. The proposed crossover technique involves the calculation of the mean of the fittest chromosomes in the population, thereby generating offsprings that are closer to the solution having minimum losses. Before applying MBC, GGA-MLP sorts the chromosomes in ascending order based on their fitness values. MBC starts by selecting the top 30% of the chromosomes and calculates the gene-wise mean of these chromosomes. The mean chromosome Cbf is inserted into a set S to prevent it from being selected again for MBC in the current iteration. This is done to ensure that a unique chromosome is selected from the population each time MBC is performed, thereby preventing the problem of generating duplicate children. This process continues till the desired number of offsprings is generated. The steps of MBC are shown in After crossover, In GA, the mutation operator is vital for maintaining diversity in the population. Mutation operator introduces diversity in the evolving population. It randomly modifies one or more genes of a chromosome depending upon the mutation probability which avoids getting stuck in the local minima. In traditional GA, every chromosome has an equal probability of getting mutated irrespective of its fitness . It meanCmean. It then selects a chromosome Cj randomly from the worst 30% (M) chromosomes in the population for mutation. A random number R is generated for every gene of Cj and is compared with the mutation probability Pm. If R >  Pm, difference “d” between the value of the selected gene of Cj and that of the corresponding gene of the mean chromosome is calculated, and a random number “r” is generated. The product of r and d is then subtracted from the corresponding gene value in Cj. This helps the chromosome in approaching good gene values, thereby increasing its overall fitness.Greedy mutation starts by calculating the gene-wise mean of the top 30% (N) chromosomes to generate a mean chromosome Due to the use of greedy approaches at each step, diversity of the population may decrease leading to premature convergence. To avoid this, it is important to introduce diversity in the population. GGA-MLP introduces diversity in the population in each iteration by generating 30% of the population using elitism, 50% of the population using MBC and greedy mutation, and the remaining 20% randomly by choosing synaptic weights and biases within the range .First, we present the datasets that are selected to evaluate the effectiveness of GGA-MLP, in terms of accuracy achieved in classifying the input data, in To evaluate the effectiveness of the proposed approach GGA-MLP, ten standard binary classification datasets are selected from the UCI Machine Learning Repository : ParkinsN is the total number of samples in the testing dataset.To evaluate the effectiveness of GGA-MLP, the performance of MLP trained using GGA-MLP is compared with the classification accuracy of MLP trained using existing algorithms, namely, GA , ABC 2727, MMPSON+1, where N is number of relevant features of the dataset. In some cases, the number of hidden neurons taken is 5 × N+1. The architecture of MLP used for each dataset is shown in Before the start of the training phase, it is required to decide the architecture of MLP for each dataset. To perform a fair comparison, the architecture of MLP is kept the same for each algorithm. Here, we take only one hidden layer, as one hidden layer is sufficient to classify the datasets shown in The values of the controlling parameters of ABC, WOA, MMPSO, MVO, GOA, GA, and GGA-MLP are listed in 4It is evident from Tables In 4In this paper, a greedy genetic algorithm, GGA-MLP, is presented to train MLP. The use of domain-specific knowledge enables the generation of good quality initial population. Mean-based crossover and greedy mutation help algorithm in moving toward global optima by exploring the search space thoroughly. Datasets of varying complexities are used to evaluate the performance of GGA-MLP and to compare it with existing state-of-the-art algorithms as well as existing classifiers such as Naïve Bayes, decision tree, logistic regression, and MLP trained using BP. The results show that although GGA-MLP takes more time to converge as compared to other metaheuristic algorithms, the performance of GGA-MLP is better than or comparable to the existing techniques in classifying datasets, especially large datasets, as GGA-MLP searches the solution space properly by maintaining a balance between exploration and exploitation.In future, we plan to extend our work to train other types of ANNs and incorporate architecture optimization in it.


The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy. The wet clutch is widely used in automatic transmission systems and plays a vital role in torque transfer . As showMany researchers have studied the buckling problem in the wet clutch system. Zagrodzki  solved tFrom the maintenance point of view, detecting the disk deformation in time is necessary. Prognostics health management (PHM) for engineering equipment has been developing quickly in recent years, and many researchers have utilized artificial intelligence (AI) techniques for fault diagnosis . Anyi etAlthough the fault diagnosis methods for rotary machineries such as bearing, gearbox, and wind turbines have made progress, relevant applications for wet clutch systems have barely been studied. One obstacle to the fault diagnosis for the wet clutch system lies in feature selection. Fault diagnosis methods for rotary machinery have some clear patterns, for example, the bearing system can be classified based on bearing fault frequencies . HoweverThis paper presents a fault diagnosis method for a multi-disc wet clutch based on time-frequency entropy at varying speed. Some major contributions can be summarized as follows: (1) The multi-disc wet clutch is crucial to transmission systems, but almost zero attention has been paid to fault diagnosis methods for the wet clutch. The proposed paper is a pioneer in developing fault diagnosis models and successfully classifying the wet clutch fault. (2) As for feature selection, traditional time-domain features fail to classify the fault of the wet clutch effectively due to the complexity of the clutch structure and operation mechanism; this paper adopts Hilbert spectrum entropy to identify the tiny differences caused by faults in the system. (3) Most fault diagnosis methods utilize the signal features at the same speed. In the proposed methods, the dimension of the data is the signal features at varying speeds rather than different features at the same speed, which can take advantage of the fault-induced vibration varying along with rotation speed. The rest of this paper is organized as follows. Z-axis, the separate disk and friction disk usually have other motions, such as translating along the Z-axis, and oscillating around the X-axis and Y-axis [This paper mainly adopts the wobble and rub impact of the clutch disks as the initial criteria for distinguishing the signal with different buckling degrees. As shown in d Y-axis .The wobble effect is explained in Before the bench test, four pairs of separate disks and friction disks are selected depending on their buckling degree. Then every single disk is set on a smooth surface (the outer ring is contacting the surface) and scanned via HandyScan3D. After importing the data into the computer, their surface topography can be read in Geomagic. The steps can be seen in Four groups have been marked as A, B, C, and D based on their measured and calculated average heights of the six points. The parameters are listed in The arrangement of the test rig is shown in The signal collection system used in the experiment is provided by the B&K company, including a transducer, a signal pre-processing acceptor, and a PC. The transducer is a B&K 4518-03 accelerometer mounted axially to the transmission system outside the clutch box. The data are collected at a rate of 64 kHz and processed via B&K PULSE software. During the experiment, the lubrication oil  is set to be at ambient temperature. Data are recorded with an increment of 100 rpm ranging from 500 rpm to 1100 rpm (7 different rotation speeds), and each rotating speed lasts 5 s. The experimental parameters are listed in The vibration signal of each fault type at 700 rpm is plotted simply in x(t) can be expressed as:Before applying the Hilbert transform, the whole signal needs to be decomposed into several IMFs (intrinsic mode oscillations); this is the empirical mode decomposition (EMD). The IMFs need to meet several requirements: they are defined as functions having the same (or differing at most by one) numbers of zero-crossing and extrema, and also having symmetric envelopes (with respect to the time axis) defined by the local maxima and minima, respectively. After EMD, a signal X-axis represents the time, and the Y-axis represents the frequency. The local color represents the power intensity of the vibration at a specific time and frequency. From the figure, the energy density of the four appears different. For example, the intensity of the bright dot in Classes B, C, and D is greater than that of Class A, which indicates that at 700 rpm, the signal is more chaotic as the buckling degree increases. One thing that should be mentioned is that because the wet clutch is a complex assembly with many other parts and ATF cooling inside, the vibration variance of the four classes may not be entirely related to the rub impact of the clutch disks, but it is clear that the buckling variance has increased the vibration variance on the spectrum plots. To continue, although the spectra of different categories show a little difference, each spectrum needs to be assigned an identical value for further classification.In order to quantify the signal variance caused by the buckling of friction components, spectrum entropy is introduced and calculated here. The block diagram is presented in A. The energy for all blocks is normalized, and the normalized value for each block is written as:As illustrated in Finally, the total time-frequency entropy of the vibration signal can be expressed as:The present work has calculated the Hilbert spectrum entropy of each group from 500 rpm to 1100 rpm, and plotted the fitted curves as shown in The framework of the fault diagnosis method is depicted in The Naïve Bayes method is a well-known classifier based on Bayes’ theorem and conditional independence assumption. For a given data set i,j,k. This is a dramatic reduction compared with the Y is any discrete value and the attributes Y for each given instance X. Assuming that Y will take on its kth possible value, according to Bayes’ rule, is:Notice that when k nearest instances to x based on a setting distance. The neighborhood containing these k instances is represented by x can be calculated based on decision rules:KNN is an instance-based learning algorithm, which searches for the most similar eigenvectors of the instance in the database . For a tSVM is a computational learning method for classification of small samples . SVM worMany researchers have taken the time-domain features as the input for fault diagnosis methods of rotation machinery ,32. SimiIn the table, the root mean square (k-folds approach for training and testing is used with k = 5.Data are labeled as A, B, C, and D for different buckling degrees. Firstly, all signal clips are split into 40 smaller pieces (7 different speeds, so each group has After the testing process, validation of the classification is done, and the efficiencies of the classifiers for different features are obtained and compared. Since the Hilbert spectrum entropy can better reflect the system characteristics, it positively influences the classification accuracy. This paper tests the methods using the entropy at different speeds as the inputs, and the test results are shown in The confusion matrices for the multi-speed entropy methods of the three classifiers are shown in This paper provides a fault diagnosis method for the wet clutch buckling problem based on Hilbert spectrum entropy. The rub impact and wobble effect of the clutch disk are introduced as an intuition to demonstrate the potential difference caused by buckling. Then, a multi-speed bench test is conducted, and the tested objects are divided into four classes based on buckling degree. Then, the data are processed via Hilbert–Huang transform to obtain the Hilbert spectrum. After converting the spectrum into gray-scale images, the entropy of the spectrum is calculated and stored as the input features. Finally, Naïve Bayes, KNN, and SVM are used to verify the efficiency of the entropy-based method compared with other time-domain features.Hilbert spectrum entropy has a positive impact on the accuracy of fault diagnosis methods. From the results, compared with other time-domain features, the proposed entropy-based method drastically increases the accuracy. Meanwhile, entropy-based SVM has the best effectiveness to classify the clutch with different buckling degrees, especially in identifying Class A among other fault classes.Also, the computation of the entropy suggests that the buckling of the disk could lead to an increase in vibration energy in the clutch pack. This provides valuable information for trend analysis in clutch buckling monitoring. One limitation of the classification model is that the accuracy in identifying a higher buckling degree of the system needs to be improved. Therefore, recognizing the fault frequency more effectively will be a focus of our future work.


Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved.  •State-of-the-art prediction accuracy of allosteric sites on benchmarking datasets•Multiple measures to capture different mechanistic insights in allostery•Provide guidance for molecular design using key interaction residues in silico is of great interest in expanding the chemical space of drug discovery. Here, we demonstrate how bond-to-bond propensity analysis is used to not only predict allosteric sites in large benchmarking datasets but also to shed light on the possible allosteric mechanisms involved. Our quantitative analysis of a given site with a range of statistical measures allows the identification of key residues required for allosteric signaling. The data can be harnessed for artificial-intelligence-driven drug discovery and digital molecular design, which are new areas of interest in the data science community.Proteins are the most common drug targets, and allostery plays a key role in regulating protein activities. Predicting allosteric sites  Modulation of protein activity is important for disease treatment, and challenges associated with common drugs have fueled the need for allosteric drugs that bind distant from the active site. Yet, allosteric site prediction remains challenging. In this study, we benchmark an atomistic, graph-theoretical method called bond-to-bond propensity against allosteric protein databases to illustrate its capability to accurately uncover allosteric sites. Our six scoring measures provide additional insights into potential mechanisms involved and could guide the design of drug molecules. The design of allosteric site binders could follow well-established approaches used to develop molecules that bind to orthosteric sites, such as high-throughput screening,,,,Experimental methods including tethering,,,,There are two general ways of approaching the problem of identifying putative allosteric sites computationally: (1) identifying allosteric sites without considering the communication with orthosteric sites and (2) uncovering the allosteric communication pathways between orthosteric and allosteric sites.M ,We here exemplify the method on bovine seminal ribonuclease  reported in the ASD database are incorrect (they do not form a binding site), and those of one further structure (PDB: 2ATS) do not match with the data in ASBench. From the remaining eight, six structures  obtained from the ASBench are only one part of a large and complex multimeric protein, where the effect of cooperativity might play a crucial role. Therefore, without the complete protein structure, the allosteric signaling cannot be detected. For example, it has been demonstrated with ATCase, a large dodecameric protein with six orthosteric sites, that only when at least three orthosteric sites are involved is allosteric behavior detected.Escherichia coli, phosphoglycerate dehydrogenase (PDB: 2PA3), displays a different allosteric mechanism, the flip flop mechanism,1Z8D) contains two allosteric sites,We focus here on the 12 structures with allosteric ligands where the allosteric site could not be detected by any of the measures. From those 12, the orthosteric residues of three structures (PDB: 3PJG), ASBench has incorrect orthosteric residues reported (not forming a binding pocket), so a wrong perturbation source was used. Hemoglobin (PDB: 1B86) is a well-known protein with cooperativity underpinning its activity3C1N and 3H6O) are large and complex multimeric proteins where again cooperativity would affect the results. The orthosteric sites and allosteric sites of the other three structures , similar to those of 1Z8D above, are in close proximity. The allosteric effect is not mediated by functional coupling and is thus not revealed by propensity analysis.Upon removing the allosteric ligands, allosteric sites of seven more structures could not be identified. For the structure of UDP-glucose dehydrogenase (PDB: 11BG) example, the allosteric site contains eight residues, but only four residues form direct interactions with the allosteric ligand. Therefore, these four residues are responsible for allosteric signaling as the direct interactions connecting the ligand and the protein are essentially where the perturbation starts . In the previous bovine seminal ribonuclease  is removed when carrying out bond-to-bond propensity analysis for the CASBench database.3CSM),Bond-to-bond propensity analysis was conducted for these 314 structures using the orthosteric ligand or orthosteric site residues (with orthosteric ligand removed) as the perturbation source in two separate runs. When multiple orthosteric ligands or sites are present, all of them were used as the perturbation source. Moreover, when there are multiple allosteric sites in the protein structure, each of them is investigated separately with the six statistical measures, and the average value for each of the measures is used to decide whether the allosteric sites can be detected for the protein. It is observed in some cases that some of the allosteric sites of the protein can be detected by a particular measure, whereas the other sites cannot be detected , according to at least one statistical measure. When using the orthosteric site residues as the perturbation source, the allosteric site is detected for 304 of 314 structures (32 of 33 proteins), according to at least one statistical measure. It is observed that, in general, the allosteric site of a protein structure can be identified with more statistical measures when the orthosteric ligand is set as the perturbation source.If the orthosteric ligand is selected as the perturbation source, the source bonds include the weak bonds formed by the ligand and the surrounding residues. The orthosteric site includes all residues within 5 Å of the orthosteric ligand.4R1R) is ribonucleotide reductase protein R1 (CAS0047). It is a large and complex multimeric protein, and only one orthosteric site is reported in the CASBench database. Hence, the effect of cooperativity could affect the performance of propensity analysis as previously discussed. Another two structures (PDB: 1FUQ and 1KQ7) are two out of the four structures of fumarase (CAS0085). This is also a complex multimeric protein where bond-to-bond propensity analysis may not perform well if not all orthosteric ligands are present. The remaining three structures are epoxide hydrolase (CAS0002) . We analyzed 28 structures of epoxide hydrolase in total, each with a different orthosteric ligand. Hence, different ligands, even when binding at the same orthosteric site, exert different perturbation effects on the protein.We focus here on the six structures for which the allosteric site cannot be detected by any of the measures when using orthosteric ligands as the perturbation source. One of them (PDB: 1LLD and 1LTH) of L-lactate dehydrogenase (CAS0028) were not identified. This can be partly explained by the changed perturbation effects as the allosteric sites were identified when sourcing from the orthosteric ligands. In CASBench, the orthosteric sites include residues within 5 Å from the orthosteric ligands, which leads to a large region as the perturbation source. This shows that the specific ligand-site interactions are crucial for accurate allosteric site detection. This is consistent with the overall trend since it has been shown above that successful allosteric site detection is achieved by more statistical measures using the orthosteric ligand as the perturbation source. Moreover, allosteric sites of another eight structures were not detected when only using the orthosteric site residues as the perturbation source. This further strengthens the idea that the method is sensitive to specific interactions between the ligand and the protein and holds the potential to evaluate the performance of different ligands in the orthosteric site.When orthosteric residues were used as the perturbation source, the allosteric sites of two structures  structures and the allosteric site is detected for 99 of 118 (83.9%) structures when the allosteric ligand is removed, according to at least one statistical measure. Despite the strengthening of functional coupling of the allosteric site to the orthosteric site by the allosteric ligand, propensity analysis is still able to reveal the intrinsic connectivity between the two sites. For the CASBench database, we conducted our analysis sourced from the orthosteric ligands or the orthosteric residues and managed to detect the allosteric sites according to at least one statistical measure for 308 of 314 (98.1%) structures (32 of 33 proteins) and for 304 of 314 (96.8%) structures (32 of 33 proteins), respectively. The allosteric site of a protein structure can be identified with more statistical measures when choosing the orthosteric ligand as the perturbation source. This observation suggests that using the ligand as the perturbation source confers the perturbation effect of the binding event more accurately. However, if the information on the orthosteric substrate is not available, it is viable to select the orthosteric residues as the perturbation source.Four existing computational methods have been benchmarked using the allosteric protein data from ASBench and ASD. Since the protocols for protein structure selection and collection of relevant site information are different for each method, a direct comparison cannot be fully achieved. The prediction accuracies of AllositePro,The results presented here strengthen confidence in allosteric site identification predicted by bond-to-bond propensity, which coupled with the efficiency of the method make it an attractive approach. Generally, the definition of orthosteric and allosteric residues, which would significantly affect the size and residues involved, plays an essential part when evaluating allosteric site prediction methods and was also highlighted for bond-to-bond propensity analysis. Finally, more detailed analysis would be usually required in cases where the allosteric site and the orthosteric site are in very close proximity, to elucidate the effect of cooperativity in large and complex multimeric proteins or the role of structural water molecules, which could still be possible given the computational efficiency of the approach. The introduction of the statistical measures coupled with the availability of large datasets and the efficiency of computing bond-to-bond propensity taken together strengthens our understanding of allostery and builds the groundwork to a more targeted and data-driven allosteric drug design.s.yaliraki@imperial.ac.uk).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Sophia N. Yaliraki N is the number of nodes (atoms).Bond-to-bond propensity analysis starts by constructing a weighted atomistic graph using the three-dimensional coordinates of the atoms of the protein in the PDB files. Atoms are represented by nodes, and interactions  between the atoms are represented by edges. The weights of edges correspond to the interaction energies between the atoms with weights derived from relevant interatomic potentials. An in-depth procedure for the atomistic protein graph construction has been described in Delmotte et al. and Amor et al.M was introduced to study non-local edge-coupling in graphs,M is employed to analyze the atomistic protein graph. The element i has on edge j. M is given byn nodes and m edges; i and j, i.e., the bond energy between the atoms. L.L, which defines the diffusion dynamics on the energy-weighted graph,Bond-to-bond propensity was first introduced in Amor et al.b anywhere else in the protein, we calculate the following:To evaluate the effect of perturbations from a group of bonds This is the raw propensity of an individual bond that reflects how strongly the bond is coupled to the perturbation source. As different proteins contain different numbers of bonds, the raw propensity is normalized and the bond propensity is defined as follows:R:The residue propensity is then defined as the sum of normalized bond propensities of all the bonds of a residue, b from the perturbation source is defined as the minimum distance, b and any bond of the perturbation source:d, a linear model for the logarithm of the propensities is adopted to solve the QR minimization problem:p is the quantile, and b with propensity Bond and residue propensities naturally decrease as the distance of the bond or residue from the perturbation source increases. To determine the bonds and residues that are significant, bond and residue propensities at a similar distance from the perturbation source are compared using conditional quantile regression (QR).R is defined similarly by using the residue propensity as shown in The residue quantile score of residue 1The average bond QS of the allosteric site:2The average residue QS of the allosteric site:3The proportion of bonds in the allosteric site with bond QS greater than 0.95,i.e., 4The average reference bond QS of the allosteric site:allosteric site is the number of bonds in the allosteric site.Four statistical measures have been used to evaluate the significance of the QS by Amor et al.5The proportion of residues in the allosteric site with residue QS greater than 0.95,i.e., 6The average reference residue QS of the allosteric site:allosteric site is the number of residues in the allosteric site.For the purpose of complementing these previous measures and to investigate more aspects of allosteric site detection, two additional measures were introduced in this work:If the functional coupling is the result of a cumulative effect of the whole allosteric site, an accurate measure of the allosteric propensity would be the average QS of all bonds or residues in the allosteric site. Hence, measures 1, 2, 4, and 6 would be able to uncover the cumulative effect at both the bond and residue level for both the intrinsic propensities of the protein and the absolute propensities comparing with the SCOP reference set.11BG)To assess the significance of the average bond and residue QS