Journal articles: 'Neural border'

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Li, Yongbin, Di Zhao, Takeo Horie, Geng Chen, Hongcun Bao, Siyu Chen, Weihong Liu, et al. "Conserved gene regulatory module specifies lateral neural borders across bilaterians." Proceedings of the National Academy of Sciences 114, no. 31 (July 17, 2017): E6352—E6360. http://dx.doi.org/10.1073/pnas.1704194114.

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The lateral neural plate border (NPB), the neural part of the vertebrate neural border, is composed of central nervous system (CNS) progenitors and peripheral nervous system (PNS) progenitors. In invertebrates, PNS progenitors are also juxtaposed to the lateral boundary of the CNS. Whether there are conserved molecular mechanisms determining vertebrate and invertebrate lateral neural borders remains unclear. Using single-cell-resolution gene-expression profiling and genetic analysis, we present evidence that orthologs of the NPB specification module specify the invertebrate lateral neural border, which is composed of CNS and PNS progenitors. First, like in vertebrates, the conserved neuroectoderm lateral border specifier Msx/vab-15 specifies lateral neuroblasts in Caenorhabditis elegans. Second, orthologs of the vertebrate NPB specification module (Msx/vab-15, Pax3/7/pax-3, and Zic/ref-2) are significantly enriched in worm lateral neuroblasts. In addition, like in other bilaterians, the expression domain of Msx/vab-15 is more lateral than those of Pax3/7/pax-3 and Zic/ref-2 in C. elegans. Third, we show that Msx/vab-15 regulates the development of mechanosensory neurons derived from lateral neural progenitors in multiple invertebrate species, including C. elegans, Drosophila melanogaster, and Ciona intestinalis. We also identify a novel lateral neural border specifier, ZNF703/tlp-1, which functions synergistically with Msx/vab-15 in both C. elegans and Xenopus laevis. These data suggest a common origin of the molecular mechanism specifying lateral neural borders across bilaterians.

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Zaaboub, Wala, Lotfi Tlig, Mounir Sayadi, and Basel Solaiman. "Neural Network-based System for Automatic Passport Stamp Classification." Information Technology And Control 49, no. 4 (December 19, 2020): 583–607. http://dx.doi.org/10.5755/j01.itc.49.4.25919.

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The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.

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Craft, Edward, Hartmut Schütze, Ernst Niebur, and Rüdiger von der Heydt. "A Neural Model of Figure–Ground Organization." Journal of Neurophysiology 97, no. 6 (June 2007): 4310–26. http://dx.doi.org/10.1152/jn.00203.2007.

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Psychophysical studies suggest that figure–ground organization is a largely autonomous process that guides—and thus precedes—allocation of attention and object recognition. The discovery of border-ownership representation in single neurons of early visual cortex has confirmed this view. Recent theoretical studies have demonstrated that border-ownership assignment can be modeled as a process of self-organization by lateral interactions within V2 cortex. However, the mechanism proposed relies on propagation of signals through horizontal fibers, which would result in increasing delays of the border-ownership signal with increasing size of the visual stimulus, in contradiction with experimental findings. It also remains unclear how the resulting border-ownership representation would interact with attention mechanisms to guide further processing. Here we present a model of border-ownership coding based on dedicated neural circuits for contour grouping that produce border-ownership assignment and also provide handles for mechanisms of selective attention. The results are consistent with neurophysiological and psychophysical findings. The model makes predictions about the hypothetical grouping circuits and the role of feedback between cortical areas.

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Milet, Cécile, and Anne H. Monsoro-Burq. "Neural crest induction at the neural plate border in vertebrates." Developmental Biology 366, no. 1 (June 2012): 22–33. http://dx.doi.org/10.1016/j.ydbio.2012.01.013.

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Shen, Jianjun. "Research on the International Trade Performance Evaluation of Cross-Border e-Commerce Based on the Deep Neural Network Model." Journal of Sensors 2022 (October 8, 2022): 1–9. http://dx.doi.org/10.1155/2022/3006907.

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The rapid development of e-commerce international trade has driven the rapid growth of the economic system of international trade enterprises. This also means that industry competition is gradually intensifying, which also makes performance evaluation the key to cross-border e-commerce international trade. At present, my country’s research on the performance evaluation of cross-border e-commerce international trade is in a blank state. Therefore, this paper takes the international trade performance evaluation of cross-border e-commerce as the research object and, based on the deep neural network model, develops a cross-border international trade performance evaluation model, changes trade strategies, and improves trade performance. This paper first analyzes various neural network models, such as artificial neural network, “BP” neuron model, and LSTM neural network. This paper summarizes a deep neural network model that is conducive to the development of cross-border e-commerce and points out the problems in the current performance evaluation of cross-border e-commerce international trade: the e-commerce market supervision system is not perfect; the second is the inconsistent evaluation indicators; the third is the evaluation system. There are some differences with the actual. Finally, this paper puts forward relevant suggestions for the performance evaluation of cross-border e-commerce international trade and points out the advantages and disadvantages of various neural networks, as well as their roles in cross-border e-commerce performance evaluation, and compares these neural networks through experiments. Experiments show that among these neural network models, the deep neural network model is the best and has the highest accuracy and stability in e-commerce trade performance evaluation. In the later stage, we will improve the global logistics system, strengthen the application of big data technology, and improve the overall performance of global operations. First, a set of indicators is designed to evaluate the performance of e-commerce systems, using the enterprise key factor model concept. In addition, this evaluation method is different from the commonly used expert evaluation method and physical evaluation method in evaluating the construction quality, cost, education and growth ability, and performance level of the international business system of cross-border e-commerce.

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Birgbauer, E., J. Sechrist, M. Bronner-Fraser, and S. Fraser. "Rhombomeric origin and rostrocaudal reassortment of neural crest cells revealed by intravital microscopy." Development 121, no. 4 (April 1, 1995): 935–45. http://dx.doi.org/10.1242/dev.121.4.935.

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Neural crest cell migration in the hindbrain is segmental, with prominent streams of migrating cells adjacent to rhombomeres (r) r2, r4 and r6, but not r3 or r5. This migratory pattern cannot be explained by the failure of r3 and r5 to produce neural crest, since focal injections of the lipophilic dye, DiI, into the neural folds clearly demonstrate that all rhombomeres produce neural crest cells. Here, we examine the dynamics of hindbrain neural crest cell emigration and movement by iontophoretically injecting DiI into small numbers of cells. The intensely labeled cells and their progeny were repeatedly imaged using low-light-level epifluorescence microscopy, permitting their movement to be followed in living embryos over time. These intravital images definitively show that neural crest cells move both rostrally and caudally from r3 and r5 to emerge as a part of the streams adjacent to r2, r4, and/or r6. Within the first few hours, cells labeled in r3 move within and/or along the dorsal neural tube surface, either rostrally toward the r2/3 border or caudally toward the r3/4 border. The labeled cells exit the surface of the neural tube near these borders and migrate toward the first or second branchial arches several hours after initial labeling. Focal DiI injections into r5 resulted in neural crest cell contributions to both the second and third branchial arches, again via rostrocaudal movements of the cells before migration into the periphery. These results demonstrate conclusively that all rhombomeres give rise to neural crest cells, and that rostrocaudal rearrangement of the cells contributes to the segmental migration of neural crest cells adjacent to r2, r4, and r6. Furthermore, it appears that there are consistent exit points of neural crest cell emigration; for example, cells arising from r3 emigrate almost exclusively from the rostral or caudal borders of that rhombomere.

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Rideaux, Reuben, and William J. Harrison. "Border ownership-dependent tilt aftereffect for shape defined by binocular disparity and motion parallax." Journal of Neurophysiology 121, no. 5 (May 1, 2019): 1917–23. http://dx.doi.org/10.1152/jn.00111.2019.

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Discerning objects from their surrounds (i.e., figure-ground segmentation) in a way that guides adaptive behaviors is a fundamental task of the brain. Neurophysiological work has revealed a class of cells in the macaque visual cortex that may be ideally suited to support this neural computation: border ownership cells (Zhou H, Friedman HS, von der Heydt R. J Neurosci 20: 6594–6611, 2000). These orientation-tuned cells appear to respond conditionally to the borders of objects. A behavioral correlate supporting the existence of these cells in humans was demonstrated with two-dimensional luminance-defined objects (von der Heydt R, Macuda T, Qiu FT. J Opt Soc Am A Opt Image Sci Vis 22: 2222–2229, 2005). However, objects in our natural visual environments are often signaled by complex cues, such as motion and binocular disparity. Thus for border ownership systems to effectively support figure-ground segmentation and object depth ordering, they must have access to information from multiple depth cues with strict depth order selectivity. Here we measured in humans (of both sexes) border ownership-dependent tilt aftereffects after adaptation to figures defined by either motion parallax or binocular disparity. We find that both depth cues produce a tilt aftereffect that is selective for figure-ground depth order. Furthermore, we find that the effects of adaptation are transferable between cues, suggesting that these systems may combine depth cues to reduce uncertainty (Bülthoff HH, Mallot HA. J Opt Soc Am A 5: 1749–1758, 1988). These results suggest that border ownership mechanisms have strict depth order selectivity and access to multiple depth cues that are jointly encoded, providing compelling psychophysical support for their role in figure-ground segmentation in natural visual environments. NEW & NOTEWORTHY Figure-ground segmentation is a critical function that may be supported by “border ownership” neural systems that conditionally respond to object borders. We measured border ownership-dependent tilt aftereffects to figures defined by motion parallax or binocular disparity and found aftereffects for both cues. These effects were transferable between cues but selective for figure-ground depth order, suggesting that the neural systems supporting figure-ground segmentation have strict depth order selectivity and access to multiple depth cues that are jointly encoded.

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Li, Yanting. "A Cloud Computing-Based Intelligent Forecasting Method for Cross-Border E-Commerce Logistics Costs." Advances in Mathematical Physics 2022 (March 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/3838293.

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Aiming at the problems of poor forecasting effect and low accuracy and low efficiency in current cross-border e-commerce logistics cost prediction methods, a cloud computing-based intelligent method for cross-border e-commerce logistics cost prediction is proposed. Analyze cloud computing concepts, characteristics, and service models, study cloud computing-related technologies, and train BP neural network algorithms based on BP neural network principles. The BP neural network structure is obtained by determining the number of neurons in the input layer, the number of neurons in the hidden layer, the number of neurons in the output layer, and the activation function of the neural network. Normalize the input data samples of the input layer, and select the initial weight, threshold, and learning rate parameters of the BP neural network to determine the momentum coefficient. This paper uses neural network model combined with Spark cloud computing platform to realize the intelligent prediction of cross-border e-commerce logistics cost. This method has good predictive ability. After a large amount of data input and output relationship training, it has obtained the most suitable model for prediction. The experimental results show that the cross-border e-commerce logistics cost prediction effect of the proposed method is good, and it can effectively improve the accuracy and efficiency of cross-border e-commerce logistics cost prediction.

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Long, Gerald M., and Philip M. Garvey. "The Effects of Target Borders on Dynamic Visual Acuity: Practical and Theoretical Implications." Perception 17, no. 6 (December 1988): 745–51. http://dx.doi.org/10.1068/p170745.

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The effects of target borders on the ability of observers to resolve moving targets (Landolt Cs) under a range of conditions were examined. Contrary to reported findings with stationary targets, it was predicted that the presence of borders would improve acuity for slow-moving targets because (i) overall stimulus energy is kept relatively constant as target detail varies, and (ii) a low-spatial-frequency component is held constant as target detail varies. In an experiment in which a two-sided border (above and below the target) was used, the predicted beneficial effect of the border at slow speeds was obtained. The results are discussed in terms of practical implications for the assessment of dynamic visual acuity as well as the potential neural mechanisms underlying performance.

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Zhao, ShuTong, Zhenjie Yin, and Pingping Xie. "Multi-angle perception and convolutional neural network for service quality evaluation of cross-border e-commerce logistics enterprise." PeerJ Computer Science 10 (February 29, 2024): e1911. http://dx.doi.org/10.7717/peerj-cs.1911.

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The development of cross-border e-commerce logistics services has injected new vitality into the development of international trade, and therefore has become a new hot spot in theoretical research. In order to ensure the healthy development of cross-border e-commerce, it is urgent to build a set of scientific and effective evaluation mechanisms to scientifically evaluate the logistics service quality of cross-border e-commerce. Multi-angle perceptual convolutional neural network is a framework for service scene identification of cross-border e-commerce logistics enterprises based on deep convolutional neural network and multi-angle perceptual width learning. In this article, both shallow features and deep features were input into the deep perception model (DPM) to obtain a set of distinguishable features with causal structure, which was used to completely describe the high-level semantic information of cross-border e-commerce logistics enterprise services. Among them, DPM mainly adopts the fusion strategy of shallow feature and deep feature. Meanwhile, the feature representation is input into the width learning pattern recognition system for training and classification, so as to evaluate the service quality of cross-border e-commerce logistics enterprises. The multi-angle perceptual convolutional neural network can effectively solve the problems of high similarity between service classes of cross-border e-commerce logistics enterprises and large differences within the class, and achieve better generalization performance and algorithm complexity than support vector machine, random forest and convolutional neural network.

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Williford, J., and R. von der Heydt. "Neural Coding of Border-Ownership in Natural Scenes." Journal of Vision 12, no. 9 (August 10, 2012): 121. http://dx.doi.org/10.1167/12.9.121.

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Patthey, Cédric, and Lena Gunhaga. "Specification and regionalisation of the neural plate border." European Journal of Neuroscience 34, no. 10 (November 2011): 1516–28. http://dx.doi.org/10.1111/j.1460-9568.2011.07871.x.

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Wilinski, P., B. Solaiman, A. Hillion, and W. Czarnecki. "Toward the border between neural and Markovian paradigms." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 28, no. 2 (April 1998): 146–59. http://dx.doi.org/10.1109/3477.662756.

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Hong, Chang-Soo, and Jean-Pierre Saint-Jeannet. "The Activity of Pax3 and Zic1 Regulates Three Distinct Cell Fates at the Neural Plate Border." Molecular Biology of the Cell 18, no. 6 (June 2007): 2192–202. http://dx.doi.org/10.1091/mbc.e06-11-1047.

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In Xenopus, the neural plate border gives rise to at least three cell populations: the neural crest, the preplacodal ectoderm, and the hatching gland. To understand the molecular mechanisms that regulate the formation of these lineages, we have analyzed the role of two transcription factors, Pax3 and Zic1, which are among the earliest genes activated in response to neural plate border-inducing signals. At the end of gastrulation, Pax3 and Zic1 are coexpressed in the neural crest forming region. In addition, Pax3 is expressed in progenitors of the hatching gland, and Zic1 is detected in the preplacodal ectoderm. Using gain of function and knockdown approaches in whole embryos and animal explants, we demonstrate that Pax3 and Zic1 are necessary and sufficient to promote hatching gland and preplacodal fates, respectively, whereas their combined activity is essential to specify the neural crest. Moreover, we show that by manipulating the levels of Pax3 and Zic1 it is possible to shift fates among these cells. These findings provide novel information on the mechanisms regulating cell fate decisions at the neural plate border.

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NOTHDURFT, HANS-CHRISTOPH, JACK L. GALLANT, and DAVID C. VAN ESSEN. "Response profiles to texture border patterns in area V1." Visual Neuroscience 17, no. 3 (May 2000): 421–36. http://dx.doi.org/10.1017/s0952523800173092.

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Cells in area V1 of the anesthetized macaque monkey were stimulated with large texture patterns composed of homogeneous regions of line elements (texels) with different orientations. To human observers, such patterns appear to segregate, with the percept of sharp boundaries between texture regions. Our objective was to investigate whether the boundaries are reflected in the responses of single cells in V1. We measured responses to individual texels at different distances from the texture border. For each cell, patterns of optimally or orthogonally orientated texels were adjusted so that only one texel fell into the receptive field and all other texels fell in the visually unresponsive regions outside. In 37 out of 156 neurons tested (24%), texels immediately adjacent to a texture border evoked reliably larger responses than identical texels farther away from the border. In 17 neurons (11%), responses to texels near the border were relatively reduced. Border enhancement effects were generally stronger than border attenuation effects. When tested with four different border configurations (two global orientations and two edge polarities), many cells showed reliable effects for only one or two configurations, consistent with cells encoding information about the orientation of the texture border or its location with respect to the segmented region. Across the sample, enhancement effects were similar for all texture borders. Modulation by the texture surround was predominantly suppressive; even the responses near texture borders were smaller than those to a single line. We compared these results with the results of a popout test in which the line in the receptive field was surrounded by homogeneous texture fields either orthogonal or parallel to the center line. The patterns of response modulation and the temporal onset of differential responses were similar in the two tests, suggesting that the two perceptual phenomena are mediated by similar neural mechanisms.

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Kotlyar, D. I., and A. N. Lomanov. "SEGMENTATION OF PICTURES CONTAINING BLADE EDGE OF A GAS TURBINE ENGINE." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 227 (May 2023): 3–10. http://dx.doi.org/10.14489/vkit.2023.05.pp.003-010.

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The article describes common techniques for semantic segmentation pictures containing edges of gas turbine engines blades for detecting left and right borders for further using in forming trajectory algorithms with direct metal deposition. For analysis such metrics, as pixel accuracy, mean pixel accuracy, intersection over union, frequency weighed intersection over union are used. Classic method of computer vision with threshold filters, border segmentation neural network method, fully convoluted neural network for semantic segmentation are focused on. The classic method of computer vision process image by several sequential applied filters: translate RBG to HSL, select lightness layer, threshold for this layer, morphological transformation, select top and bottom pixels in blade edge. This method gave 95,18 % pixel accuracy and 65,19 % intersection over union. Several architectures neural network for edge’s border segmentation, such as DexiNed, RCF, PiDiNet were compared. PiDiNet gave the best result: this architecture gave 96,37 % pixel accuracy and 77,57 % intersection over union. The last method in this research was fully convoluted neural network. 75 combinations of encoders and decoders architectures were trained and tested. The represented encoders were ResNet34, ResNet50, ResNet101, VGG11, VGG16, VGG19, InceptionResNetV2, InceptionV4, Efficientnet-b0, Efficientnet-b4, Efficientnet-b7, Xception. The represented decoders architectures were Unet, Unet++, MAnet, Linknet, PSPNet, FPN, DeepLabV3, DeepLabV3+, PAN. Fully convoluted neural network method gave the best result. The most accurate combination was Unet-InceptionResNetV2 model with 99,22 % pixel accuracy and 97,25 % intersection over union metric. The best method for semantic segmentation pictures contain blade edges was chosen.

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Guo, Hongli, and Tong Zou. "Cross-Border E-Commerce Platform Logistics and Supply Chain Network Optimization Based on Deep Learning." Mobile Information Systems 2022 (May 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/2203322.

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E-commerce and logistics are symbioses with each other, but cross-border e-commerce (CBEC) still cannot break away from cross-border logistics. With the progress of economic internationalization, economic and trade ties around the world have become closer and closer, and the level of international business exchanges has been improved. The rise of multinational e-commerce has also caused unprecedented difficulties to multinational logistics and supply chain management. The application of deep neural networks in various fields provides opportunities for cross-border e-commerce platforms to solve these problems. The existing logistics distribution model cannot keep up with the development of CBEC and has become a constraint and bottleneck for the development of CBEC. Therefore, this article introduces deep learning neural network to cross-border logistics and supply chain based on the analysis of the existing cross-border logistics model and supply chain model and the status quo of e-commerce development. It optimizes the existing cross-border logistics and supply chain network in order to break through the current bottleneck in the development of CBEC. This paper shows through research that introducing deep learning neural networks into CBEC logistics and supply chain can improve the efficiency of logistics and supply chain. Compared with the previous efficiency, the efficiency of network optimization can be increased to about 50%, reducing the cost of cross-border logistics and supply chain. The research in this article has great theoretical and guiding significance for the development of CBEC.

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Qiao, Wei. "E-Commerce across Boarder Logistics Risk Evaluation Model Based on Improved Neural Network." Journal of Function Spaces 2022 (July 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/2355298.

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BP neural network is a typical algorithm in artificial intelligence network. It has strong nonlinear mapping ability and is the most prominent part to solve some nonlinear problems. In the traditional BP algorithm, the coincidence initialization of weights and thresholds is random, which reduces the efficiency of the algorithm on the one hand and affects the accuracy of the algorithm results on the other hand. In order to solve these problems, this paper studies an e-commerce cross-border logistics risk assessment model based on improved neural network. This model can help merchants engaged in cross-border e-commerce to select appropriate third-party settlement platforms, so as to reduce the cost of merchants in the process of capital settlement. The key information in BP neural network algorithm is stored in weights and thresholds, which is enough to prove the importance of weights and thresholds for the effective operation of the whole network. The e-commerce cross-border logistics risk assessment model based on improved neural network aims to solve the problem of low level of risk assessment and the bottleneck of logistics risk assessment. The improved e-commerce cross-border logistics risk assessment model based on neural network can be used for risk rating before business development, so as to adopt different risk management methods for different risk levels.

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Bellchambers, Helen M., Kristen S. Barratt, Koula E. M. Diamand, and Ruth M. Arkell. "SUMOylation Potentiates ZIC Protein Activity to Influence Murine Neural Crest Cell Specification." International Journal of Molecular Sciences 22, no. 19 (September 28, 2021): 10437. http://dx.doi.org/10.3390/ijms221910437.

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The mechanisms of neural crest cell induction and specification are highly conserved among vertebrate model organisms, but how similar these mechanisms are in mammalian neural crest cell formation remains open to question. The zinc finger of the cerebellum 1 (ZIC1) transcription factor is considered a core component of the vertebrate gene regulatory network that specifies neural crest fate at the neural plate border. In mouse embryos, however, Zic1 mutation does not cause neural crest defects. Instead, we and others have shown that murine Zic2 and Zic5 mutate to give a neural crest phenotype. Here, we extend this knowledge by demonstrating that murine Zic3 is also required for, and co-operates with, Zic2 and Zic5 during mammalian neural crest specification. At the murine neural plate border (a region of high canonical WNT activity) ZIC2, ZIC3, and ZIC5 function as transcription factors to jointly activate the Foxd3 specifier gene. This function is promoted by SUMOylation of the ZIC proteins at a conserved lysine immediately N-terminal of the ZIC zinc finger domain. In contrast, in the lateral regions of the neurectoderm (a region of low canonical WNT activity) basal ZIC proteins act as co-repressors of WNT/TCF-mediated transcription. Our work provides a mechanism by which mammalian neural crest specification is restricted to the neural plate border. Furthermore, given that WNT signaling and SUMOylation are also features of non-mammalian neural crest specification, it suggests that mammalian neural crest induction shares broad conservation, but altered molecular detail, with chicken, zebrafish, and Xenopus neural crest induction.

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Zhang, Zuming, Yu Shi, Shuhua Zhao, Jiejing Li, Chaocui Li, and Bingyu Mao. "Xenopus Nkx6.3 Is a Neural Plate Border Specifier Required for Neural Crest Development." PLoS ONE 9, no. 12 (December 22, 2014): e115165. http://dx.doi.org/10.1371/journal.pone.0115165.

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Yu, Li, Yi Guo, Yuanyuan Wang, Jinhua Yu, and Ping Chen. "Determination of Fetal Left Ventricular Volume Based on Two-Dimensional Echocardiography." Journal of Healthcare Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4797315.

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Determination of fetal left ventricular (LV) volume in two-dimensional echocardiography (2DE) is significantly important for quantitative analysis of fetal cardiac function. A backpropagation (BP) neural network method is proposed to predict LV volume more accurately and effectively. The 2DE LV border and volume are considered as the input and output of BP neural network correspondingly. To unify and simplify the input of the BP neural network, 16 distances calculated from the border to its center with equal angle are used instead of the border. Fifty cases (forty frames for each) were used for this study. Half of them selected randomly are used for training, and the others are used for testing. To illustrate the performance of BP neural network, area-length method, Simpson’s method, and multivariate nonlinear regression equation method were compared by comparisons with the volume references in concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC), and Bland-Altman plots. The ICC and CCC for BP neural network with the volume references were the highest. For Bland-Altman plots, the BP neural network also shows the highest agreement and reliability with volume references. With the accurate LV volume, LV function parameters (stroke volume (SV) and ejection fraction (EF)) are calculated accurately.

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Deng, Zhichao, Meiji Yan, and Xu Xiao. "An Early Risk Warning of Cross-Border E-Commerce Using BP Neural Network." Mobile Information Systems 2021 (March 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/5518424.

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In this paper, we propose an early warning model of credit risk for cross-border e-commerce. Our proposed model, i.e., KPCA-MPSO-BP, is constructed using kernel principal component analysis (KPCA), improved particle swarm optimization (IPSO), and BP neural network. Initially, we use KPCA to reduce the credit risk index for cross-border e-commerce. Next, the inertia weight and threshold of BP neural network are searched using MPSO. Finally, BP neural network is used for training the data of 13 different enterprises of cross-border e-commerce’s credit risk. To analyze the efficiency of our proposed approach, we use the data of five different enterprises for testing and evaluation. The experimental results show that the mean absolute error (MAE) and root mean square error (RMSE) of our model are the lowest in comparison to the existing models and have much better efficiency.

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Layton, Oliver W., and Arash Yazdanbakhsh. "A neural model of border-ownership from kinetic occlusion." Vision Research 106 (January 2015): 64–80. http://dx.doi.org/10.1016/j.visres.2014.11.002.

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Garnett, Aaron, Tyler Square, and Daniel M. Medeiros. "Subfunctionalization of neural plate border genes by enhancer modification." Developmental Biology 344, no. 1 (August 2010): 530–31. http://dx.doi.org/10.1016/j.ydbio.2010.05.394.

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Garnett, Aaron T., Tyler Square, and Daniel M. Medeiros. "Divergence of neural plate border genes by enhancer modification." Developmental Biology 356, no. 1 (August 2011): 246–47. http://dx.doi.org/10.1016/j.ydbio.2011.05.545.

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Ali, Abder-Rahman, Jingpeng Li, Guang Yang, and Sally Jane O’Shea. "A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images." PeerJ Computer Science 6 (June 29, 2020): e268. http://dx.doi.org/10.7717/peerj-cs.268.

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Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively.

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Zhang, Xionghui. "Exchange Rate Risk Assessment of Cross-border E-commerce Based on BP Neural Network." Tobacco Regulatory Science 7, no. 5 (September 30, 2021): 4950–62. http://dx.doi.org/10.18001/trs.7.5.2.58.

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Objectives: At present, the domestic exchange rate system takes the market supply and demand as a benchmark, and then compares with other currencies to complete the exchange rate setting. This approach allows the RMB exchange rate to be more flexible and elastic. The RMB is controlled through the market mechanism. However, for many cross-border electricity suppliers in China, if the exchange rate of RMB fluctuates widely, they will face the operational risks brought by exchange rate fluctuations. Methods: In recent years, due to the continuous appreciation of the RMB, the cross-border e-commerce companies are under pressure. Results: BP neural network is an ideal processing tool to deal with the risk assessment of cross-border e-commerce caused by exchange rate changes, and it also has a very good future for practical application. Conclusion: In this paper, the exchange rate risk of cross-border e-commerce companies in China was evaluated by BP neural network.

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Bradley, R. S. "Neural crest development in Xenopus requires Protocadherin 7 at the lateral neural crest border." Mechanisms of Development 149 (February 2018): 41–52. http://dx.doi.org/10.1016/j.mod.2018.01.002.

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Hendricks, K. A., J. S. Simpson, and R. D. Larsen. "Neural Tube Defects along the Texas-Mexico Border, 1993-1995." American Journal of Epidemiology 149, no. 12 (June 15, 1999): 1119–27. http://dx.doi.org/10.1093/oxfordjournals.aje.a009766.

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Schille, Carolin, and Alexandra Schambony. "Signaling pathways and tissue interactions in neural plate border formation." Neurogenesis 4, no. 1 (January 2017): e1292783. http://dx.doi.org/10.1080/23262133.2017.1292783.

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Kobayashi, Gerson Shigeru, Camila Manso Musso, Danielle de Paula Moreira, Giovanna Pontillo-Guimarães, Gabriella Shih Ping Hsia, Luiz Carlos Caires-Júnior, Ernesto Goulart, and Maria Rita Passos-Bueno. "Recapitulation of Neural Crest Specification and EMT via Induction from Neural Plate Border-like Cells." Stem Cell Reports 15, no. 3 (September 2020): 776–88. http://dx.doi.org/10.1016/j.stemcr.2020.07.023.

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McLarren, Keith W., Anna Litsiou, and Andrea Streit. "DLX5 positions the neural crest and preplacode region at the border of the neural plate." Developmental Biology 259, no. 1 (July 2003): 34–47. http://dx.doi.org/10.1016/s0012-1606(03)00177-5.

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Law, Jera, and Kristin B. Artinger. "prdm1a regulates Rohon-Beard neuron and neural crest cell fate at the neural plate border." Developmental Biology 344, no. 1 (August 2010): 500. http://dx.doi.org/10.1016/j.ydbio.2010.05.309.

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Pla, Patrick, and Anne H. Monsoro-Burq. "The neural border: Induction, specification and maturation of the territory that generates neural crest cells." Developmental Biology 444 (December 2018): S36—S46. http://dx.doi.org/10.1016/j.ydbio.2018.05.018.

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Leung, Alan W., Barbara Murdoch, Ahmed F. Salem, Maneeshi S. Prasad, Gustavo A. Gomez, and Martín I. García-Castro. "WNT/β-catenin signaling mediates human neural crest induction via a pre-neural border intermediate." Development 143, no. 3 (February 1, 2016): 398–410. http://dx.doi.org/10.1242/dev.130849.

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Lei, Yi, and Xiaodong Qiu. "Evaluating the Investment Climate for China’s Cross-Border E-Commerce: The Application of Back Propagation Neural Network." Information 11, no. 11 (November 12, 2020): 526. http://dx.doi.org/10.3390/info11110526.

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China’s cross-border e-commerce will usher in a new golden age of development. Based on seven countries which include the Russian Federation, Mongolia, Ukraine, Kazakhstan, Tajikistan, Kyrgyzstan and Belarus along the “Belt and Road”, an evaluation system for cross-border e-commerce investment climate indicators is established in this study. This research applied the entropy method twice to evaluate the investment climate of seven countries based on 5 years panel data comprehensively and these countries are then classified into politics-oriented and industry-oriented countries, and then the weight of indicators for each category is analyzed. In addition, cross-border e-commerce investors are proposed to prioritize industry-oriented countries. Back propagation neural network algorithm is used to map the existing data and optimize the evaluation index system in combination with the genetic algorithm. This research denotes the effort to find out the index evaluation combination corresponding to the best overall score, make the established evaluation index system applicable to other countries, and provide reference for cross-border e-commerce investors when evaluating the investment climate in each country. This study provides the important practical implications in the sustainable development of China’s cross-border e-commerce environment.

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Garcı́a-Castro, Martı́n I., Christophe Marcelle, and Marianne Bronner-Fraser. "Ectodermal Wnt Function as a Neural Crest Inducer." Science 297, no. 5582 (August 2, 2002): 848–51. http://dx.doi.org/10.1126/science.1070824.

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Neural crest cells, which generate peripheral nervous system and facial skeleton, arise at the neural plate/ectodermal border via an inductive interaction between these tissues. Wnts and bone morphogenetic proteins (BMPs) play roles in neural crest induction in amphibians and zebrafish. Here, we show that, in avians, Wnt6 is localized in ectoderm and in vivo inhibition of Wnt signaling perturbs neural crest formation. Furthermore, Wnts induce neural crest from naı̈ve neural plates in vitro in a defined medium without added factors, whereas BMPs require additives. Our data suggest that Wnt molecules are necessary and sufficient to induce neural crest cells in avian embryos.

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de Croze, Noemie, and Anne Helene Monsoro-Burq. "04-P015 AP2α plays a central role in both neural border patterning and neural crest induction." Mechanisms of Development 126 (August 2009): S111. http://dx.doi.org/10.1016/j.mod.2009.06.200.

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Heydt, R., F. T. Qiu Krieger, and Z. J. He. "Neural mechanisms in border ownership assignment: motion parallax and gestalt cues." Journal of Vision 3, no. 9 (March 18, 2010): 666. http://dx.doi.org/10.1167/3.9.666.

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Eguchi, Akihiro, and Simon M. Stringer. "Neural network model develops border ownership representation through visually guided learning." Neurobiology of Learning and Memory 136 (December 2016): 147–65. http://dx.doi.org/10.1016/j.nlm.2016.10.007.

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Yazdanbakhsh, A., O. Layton, and E. Mingolla. "A neural model of border-ownership and motion in early vision." Journal of Vision 12, no. 9 (August 10, 2012): 759. http://dx.doi.org/10.1167/12.9.759.

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Wagner, E., and M. Levine. "FGF signaling establishes the anterior border of the Ciona neural tube." Development 139, no. 13 (May 23, 2012): 2351–59. http://dx.doi.org/10.1242/dev.078485.

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Huang, Xin, and Michael A. Paradiso. "V1 Response Timing and Surface Filling-In." Journal of Neurophysiology 100, no. 1 (July 2008): 539–47. http://dx.doi.org/10.1152/jn.00997.2007.

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There is ample evidence from demonstrations such as color induction and stabilized images that information from surface boundaries plays a special role in determining the perception of surface interiors. Surface interiors appear to “fill-in.” Psychophysical experiments also show that surface perception involves a slow scale-dependent process distinct from mechanisms involved in contour perception. The present experiments aimed to test the hypothesis that surface perception is associated with relatively slow scale-dependent neural filling-in. We found that responses in macaque primary visual cortex (V1) are slower to surface interiors than responses to optimal bar stimuli. Moreover, we found that the response to a surface interior is delayed relative to the response to the surface's border and the extent of the delay is proportional to the distance between a receptive field and the border. These findings are consistent with some forms of neural filling-in and suggest that V1 may provide the neural substrate for perceptual filling-in.

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Ahmad, Iftikhar, Abdul Qayyum, Brij B. Gupta, Madini O. Alassafi, and Rayed A. AlGhamdi. "Ensemble of 2D Residual Neural Networks Integrated with Atrous Spatial Pyramid Pooling Module for Myocardium Segmentation of Left Ventricle Cardiac MRI." Mathematics 10, no. 4 (February 17, 2022): 627. http://dx.doi.org/10.3390/math10040627.

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Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset. Further, the ensemble technique based on a majority voting ensemble method was used to blend the results of recent deep learning models on different set of hyperparameters. The proposed model produced an 85.43% dice score on validation samples and 98.23% on training samples and provided excellent performance compared to recent deep learning models. The myocardium border was successfully segmented across diverse subject slices with different shapes, sizes and contrast using the proposed deep learning ensemble models. The proposed model can be employed for automatic detection and segmentation of the myocardium border for precise quantification of reflow, myocardial infarction, myocarditis, and h cardiomyopathy (HCM) for clinical applications.

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Spann, P., M. Ginsburg, Z. Rangini, A. Fainsod, H. Eyal-Giladi, and Y. Gruenbaum. "The spatial and temporal dynamics of Sax1 (CHox3) homeobox gene expression in the chick's spinal cord." Development 120, no. 7 (July 1, 1994): 1817–28. http://dx.doi.org/10.1242/dev.120.7.1817.

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Sax1 (previously CHox3) is a chicken homeobox gene belonging to the same homeobox gene family as the Drosophila NK1 and the honeybee HHO genes. Sax1 transcripts are present from stage 2 H&H until at least 5 days of embryonic development. However, specific localization of Sax1 transcripts could not be detected by in situ hybridization prior to stage 8-, when Sax1 transcripts are specifically localized in the neural plate, posterior to the hindbrain. From stages 8- to 15 H&H, Sax1 continues to be expressed only in the spinal part of the neural plate. The anterior border of Sax1 expression was found to be always in the transverse plane separating the youngest somite from the yet unsegmented mesodermal plate and to regress with similar dynamics to that of the segregation of the somites from the mesodermal plate. The posterior border of Sax1 expression coincides with the posterior end of the neural plate. In order to study a possible regulation of Sax1 expression by its neighboring tissues, several embryonic manipulation experiments were performed. These manipulations included: removal of somites, mesodermal plate or notochord and transplantation of a young ectopic notochord in the vicinity of the neural plate or transplantation of neural plate sections into the extraembryonic area. The results of these experiments revealed that the induction of the neural plate by the mesoderm has already occurred in full primitive streak embryos, after which Sax1 is autonomously regulated within the spinal part of the neural plate.

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Bódi, Ildikó, Krisztina H.-Minkó, Zsolt Prodán, Nándor Nagy, and Imre Oláh. "A thymus szerkezete a huszonegyedik század elején." Orvosi Hetilap 160, no. 5 (February 2019): 163–71. http://dx.doi.org/10.1556/650.2019.31224.

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Abstract: The classical histological features of the thymus are the cortex and medulla, the Hassall’s bodies as well as the lobules. Anti-pan-cytokeratin immunocytochemistry shows that the keratin staining pattern of the cortical and medullary epithelial cells is different. The medulla is further compartmentalized: it consists of keratin-positive network and keratin-negative areas. Histology of the keratin-negative area is identical with the connective tissue of the septae. The basal lamina is continuous at the capsule and septae, but it becomes discontinuous at the border between the keratin-positive network and keratin-negative area. This immunohistochemical finding is the first histological sign, which may explain that the medulla has no blood-thymus barrier. The supporting tissue of the keratin-negative area is identical with that of the septae. The connective tissue of thymic capsule and septae develops from the cranial neural crest cells, therefore we hypothesize that the keratin-negative area has neural crest origin. Blood vessels of the thymic medulla localize in the keratin-negative area. Every emigrating or immigrating immunologically competent cells should enter the keratin-negative area, therefore this area is the transit zone of the thymus. The hematoxylin-eosin staining of the thymus shows that the thymic cortico-medullary border does not represent cellular background. However, the border between keratin-positive network and keratin-negative area is determined by cellular identity (epithelial and mesenchymal tissues). Therefore, it can be assumed that the real histological and functional border is the border between the keratin-positive network and the keratin-negative area. Orv Hetil. 2019; 160(5): 163–171.

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Bushuyev, Sergiy, Yurii Kulakov, Liudmyla Tereikovska, Ihor Tereikovskyi, and Oleh Tereikovskyi. "A NEURAL NETWORK MODEL FOR HUMAN FACE BOUNDARY DETECTION." Management of Development of Complex Systems, no. 51 (October 7, 2022): 5–11. http://dx.doi.org/10.32347/2412-9933.2022.51.5-11.

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The relevance of the implementation of means of recognition of the emotional state by the image of the face into the personnel management system is well-founded. It is shown that the implementation of such tools leads to the need to adapt the values of architectural parameters of neural network models for detecting the boundaries of target objects on bitmap images to the expected conditions of use. An approach to determining the most effective type of neural network model is proposed, which involves expert evaluation of the effectiveness of acceptable types of models and conducting computer experiments to make a final decision. As a result of the conducted research, it was determined that among the types of neural network models tested in the task of segmentation of raster images, the U-Net model is the most effective for detecting facial borders on small raster images. Using this neural network model provides a mask selection accuracy of 0.88. At the same time, the necessity of improving the mathematical support, which is used to determine the accuracy of face border detection, is determined. It is also advisable to correlate the ways of further research with the correction of typical shortcomings associated with the incorrect marking of the boundaries of various objects that are perceived by the neural network model as a human face.

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Zhang, Xiang. "Prediction of Purchase Volume of Cross-Border e-Commerce Platform Based on BP Neural Network." Computational Intelligence and Neuroscience 2022 (April 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/3821642.

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As a new form of foreign trade, cross-border e-commerce has huge development potential. Although the development prospect of cross-border e-commerce is good, the management of global supply chain is very important in order to gain a place in the fierce competition and develop steadily. The traditional forecasting of purchasing volume adopts time series, and the forecasting model is relatively simple. The purchase volume of the platform is related to the various consumption behaviors of consumers, such as the number of product reviews, the number of product collections, and whether there are tax subsidies. The sales volume in the next few days is predicted by the item number, time, and sales quantity. The four-layer BP neural network model is used, and the MATLAB neural network toolbox is used to draw the training error curve and the correlation coefficient curve. After network training, the training correlation coefficient R reaches 95.823%, and the prediction accuracy obtained at this time is higher. Further, using the established model based on BP algorithm, the traditional BP algorithm is optimized to obtain the purchase quantity of commodities. The method is applied to the forecast of commodity purchase volume of a cross-border e-commerce platform, and the results show that the average error rate of this method is 5.9%, which has high practical application value. The research results show that this paper considers multiple influencing factors and selects an appropriate forecasting method, which can effectively improve the accuracy of the company’s commodity sales forecast, so as to better formulate procurement plans and optimize inventory structure, which has certain implications for the actual operation of cross-border e-commerce platforms.

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Zhang, Tianfu, Heyan Huang, Chong Feng, and Longbing Cao. "Self-supervised Bilingual Syntactic Alignment for Neural Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14454–62. http://dx.doi.org/10.1609/aaai.v35i16.17699.

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While various neural machine translation (NMT) methods have integrated mono-lingual syntax knowledge into the linguistic representation of sequence-to-sequence, no research is available on aligning the syntactic structures of target language with the corresponding source language syntactic structures. This work shows the first attempt of a source-target bilingual syntactic alignment approach SyntAligner by mutual information maximization-based self-supervised neural deep modeling. Building on the word alignment for NMT, our SyntAligner firstly aligns the syntactic structures of source and target sentences and then maximizes their mutual dependency by introducing a lower bound on their mutual information. In SyntAligner, the syntactic structure of span granularity is represented by transforming source or target word hidden state into a source or target syntactic span vector. A border-sensitive span attention mechanism then captures the correlation between the source and target syntactic span vectors, which also captures the self-attention between span border-words as alignment bias. Lastly, a self-supervised bilingual syntactic mutual information maximization-based learning objective dynamically samples the aligned syntactic spans to maximize their mutual dependency. Experiment results on three typical NMT tasks: WMT'14 English to German, IWSLT'14 German to English, and NC'11 English to French show the SyntAligner effectiveness and universality of syntactic alignment.

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Sánchez-Sánchez, M. Araceli, Cristina Conde, Beatriz Gómez-Ayllón, David Ortega-DelCampo, Aristeidis Tsitiridis, Daniel Palacios-Alonso, and Enrique Cabello. "Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems." Entropy 22, no. 11 (November 14, 2020): 1296. http://dx.doi.org/10.3390/e22111296.

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Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using the learned features from a convolutional neural network. Three sensors are considered to design and develop a new database that is composed of visible (VIS), near-infrared (NIR), and thermal images. Most studies are based on laboratory or ideal conditions-controlled environments. However, in a real scenario, a subject’s situation is completely modified due to diverse physiological conditions, such as stress, temperature changes, sweating, and increased blood pressure. For this reason, the added value of this study is that this database was acquired in situ. The attacks considered were printed, masked, and displayed images. In addition, five classifiers were used to detect the presentation attack. Note that thermal sensors provide better performance than other solutions. The results present better outputs when all sensors are used together, regardless of whether classifier or feature-level fusion is considered. Finally, classifiers such as KNN or SVM show high performance and low computational level.

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Journal articles on the topic 'Neural border'

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