The development of the cornea, a highly specialized transparent tissue located at the anterior of the eye, is coordinated by a variety of molecules and cells. Here, we report that mast cells (MCs), recently found to be involved in morphogenesis, played a potentially important role in corneal development in mice. We show that two different waves of MC migration occurred during corneal development. In the first wave, MCs migrated to the corneal stroma and became distributed throughout the cornea. This wave occurred by embryonic day 12.5, with MCs disappearing from the cornea at the time of eyelid opening. In the second wave, MCs migrated to the corneal limbus and became distributed around limbal blood vessels. The number of MCs in this region gradually increased after birth and peaked at the time of eyelid opening in mice, remaining stable after postnatal day 21. We also show that integrin α4β7 and CXCR2 were important for the migration of MC precursors to the corneal limbus and that c-Kit-dependent MCs appeared to be involved in the formation of limbal blood vessels and corneal nerve fibers. These data clearly revealed that MCs participate in the development of the murine cornea.

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The microenvironments that cells encounter with in vitro.

Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some "insignificant' feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

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Acoustic signals are an ideal source of diagnosis data thanks to their intrinsic non-directional coverage, sensitivity to incipient defects, and insensitivity to structural resonance characteristics. However this makes prevailing signal de-nosing and feature extraction methods suffer from high computational cost, low signal to noise ratio (S/N), and difficulty to extract the compound acoustic emissions for various failure types. To address these challenges, we propose a hybrid signal processing technique to depict the embedded signal using generally effective features. The ensemble empirical mode decomposition (EEMD) is adopted as the fundamental pre-processor, which is integrated with the sample entropy (SampEn), singular value decomposition (SVD), and statistic feature processing (SFP) methods. The SampEn and SVD are identified as the condition indicators for periodical and irregular signals, respectively. Moreover, such a hybrid module is self-adaptive and robust to different signals, which ensures the generality of its performance. The hybrid signal processor is further integrated with a probabilistic classifier, pairwise-coupled relevance vector machine (PCRVM), to construct a new fault diagnosis system. Experimental verifications for industrial equipment show that the proposed diagnostic system is superior to prior methods in computational efficiency and the capability of simultaneously processing non-stationary and nonlinear condition monitoring signals.

In order to reduce operation and maintenance costs, reliability, and quick response capability of multi-fault intelligent diagnosis for the wind turbine system are becoming more important. This paper proposes a rapid data-driven fault diagnostic method, which integrates data pre-processing and machine learning techniques. In terms of data pre-processing, fault features are extracted by using the proposed modified Hilbert-Huang transforms (HHT) and correlation techniques. Then, time domain analysis is conducted to make the feature more concise. A dimension vector will then be constructed by including the intrinsic mode function energy, time domain statistical features, and the maximum value of the HHT marginal spectrum. On the other hand, as the architecture and the learning algorithm of pairwise-coupled sparse Bayesian extreme learning machine (PC-SBELM) are more concise and effective, it could identify the singleand simultaneous-fault more quickly and precisely when compared with traditional identification techniques such as pairwise-coupled probabilistic neural networks (PC-PNN) and pairwise-coupled relevance vector machine (PC-RVM). In this case study, PC-SBELM is applied to build a real-time multi-fault diagnostic system. To verify the effectiveness of the proposed fault diagnostic framework, it is carried out on a real wind turbine gearbox system. The evaluation results show that the proposed framework can detect multi-fault in wind turbine gearbox much faster and more accurately than traditional identification techniques. INDEX TERMS Wind turbine, gearbox, multi-fault diagnosis, Hilbert-Huang transform, pairwise-coupled, sparse Bayesian extreme learning machine.