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Idiopathic membranous nephropathy (IMN) belongs to an important pathogenic category of adult nephrotic syndrome. PLA2R1 exposure is critical for triggering the pathogenesis of PLA2R1-related IMN. However, the pathogenesis of IMN and the molecular mechanism of treatment remain to be further clarified. The expression changes of activated protein C (APC) and PLA2R1 in IMN patients were quantified by qPCR. A zymosan activated serum (ZAS)-induced IMN podocyte model was established in vitro. Podocyte apoptosis was detected via flow cytometry and caspase‑3 assay. The expression levels of APC, p-ERK1/2, ERK1/2, YB-1 and PLA2R1 were detected by western blotting. The regulation relationship between YB-1 and PLA2R1 was detected by dual fluorescent reporter system. In IMN patients, the expression level of PLA2R1 was increased, whereas the expression level of APC was decreased. When APC was added to podocytes in vitro, the phosphorylation of ERK1/2 was increased, which could promote the translocation of YB-1 to the nucleus that reduces the expression of PLA2R1 at the cellular transcriptional level, thereby inhibiting podocyte apoptosis. Our study is the first to report that APC can improve membranous nephropathy by affecting podocyte apoptosis through the ERK1/2/YB-1/PLA2R1 axis. This study will provide a new targeted therapy for IMN patients with high PLA2R1 expression.

Background - Diabetic nephropathy (DN) is a principal reason for kidney disease worldwide. High glucose (HG) is a major factor for DN. Kruppel like factor 5 (KLF5) participates in DN development. In the present study, we aim to elaborate the role of KLF5 in HG-induced renal tubular epithelial cell (RTEC) transdifferentiation in DN. Methods - RTECs (HK-2 cells) were treated with HG and were transfected with si-KLF5 or oe-HMGB1. Afterwards, expression of KLF5 and HMGB1 was detected, HK cell viability was determined, and levels of alpha-smooth muscle actin (α-SMA), E-cadherin, vimentin, and transforming growth factor beta 1 (TGF-β1) were assessed. Additionally, the binding relation between KLF5 and HMGB1 was analyzed. Results - In HK-2 cells with HG treatment, expression of KLF5 and HMGB1 was upregulated; levels of α-SMA, vimentin, and TGF-β1 were increased; and E-cadherin level was decreased. Moreover, KLF5 silencing resulted in down-regulated levels of α-SMA, vimentin, and TGF-β1 but upregulated level of E-cadherin. On the other hand, KLF5 could bind to the HMGB1 promoter and activate HMGB1 transcription. HMGB1 overexpression partially counteracted the inhibitive effect of KLF5 silencing on HG-induced HK-2 transdifferentiation. Conclusion - HG induced overexpressed KLF5 in RTECs, and as a transcription factor, KLF5 could bind to the HMGB1 promoter, thereby promoting HMGB1 transcription and RTEC transdifferentiation.

Due to the lack of the labeled data and the complex structures of various data, it is very hard to learn the uncertainty and representativeness accurately in active learning. In this paper, we propose a multiple kernel active learning framework that incorporates a group regularizer of distribution information into the estimation of uncertainty. The proposed method takes the advantage of multiple kernel learning to learn the kernel space in which the complex structures can be well captured by kernel weights. Meanwhile, we have developed an efficient optimization algorithm to solve the proposed method. Experimental results on twelve UCI benchmark data sets and eight subsets of ImageNet show that the proposed method outperforms several state-of-the-art active learning methods. Moreover, we also have applied the proposed method to multiple feature scenario on Caltech101, and the promising results are also obtained compared with single feature scenario.

Sound field recording using spherical harmonics (SH) has been widely used. However, too many microphones are needed when recording sound fields over large areas, due to the capture of the higher order of spherical harmonic coefficients. The theory of GO in deep learning inspired us. With training the data much less than all GO’s legal positions data, the Alpha Go has defeated top GO players. According to the information learned from a specific dataset, the higher spherical harmonics coefficients may be estimated with few captured sound pressures. In this paper, a learning-based approach for estimation of the SH coefficients has been investigated. In the proposed approach, SH coefficients are estimated with a feed-forward neural network (FNN) based on measurements of a spherical array. We generate a uniformly distributed dataset, try to evaluate the method on an average situation. Moreover, with the real sound field data in the SOFiA dataset, we try to evaluate the performance of our method when the correlations of data are weak. Experimental results show that the proposed approach achieves higher estimation accuracy of SH coefficients than a previously reported method. In simulations, 9 microphones’ performance using the proposed approach can approximate an array with 16 microphones. The experiments confirmed the feasibility of estimating the SH coefficients with the data-driven method. Thus in a specific application, it can be used to reduce the required number of microphones.

The head-related transfer function (HRTF) involves the cues for human auditory localization, which turns it into an essential item of virtual auditory display technology. In practice, the interpolation of HRTF is necessary for the virtual auditory display systems to achieve high spatial resolution. Traditional geometric-based interpolation methods are generally restrained by the spatial distribution of reference on HRTF. When the spatial distribution is sparse, the accuracy of interpolation decreases significantly. Therefore, an interpolation method using the common-pole/zero model and the fitting neural network is proposed. First, we propose a common-pole/zero model to represent HRTFs across multiple subjects, in which the low-dimensional features of the measured HRTFs are extracted. Then, for a new spatial direction, we predict the corresponding low-dimensional HRTF with a fitting neural network. Finally, we reconstruct the high-dimensional HRTF from the predicted low-dimensional HRTF. The simulation results suggest that the proposed method outperforms other interpolation methods such as Linear_AMBC, Bilinear_AMBC, and the Combination method.

Sound field reproduction using Higher-order Ambisonics (HOA) has many studies in recent years. However, in the HOA, sound fields are reproduced with the least square solution of spherical harmonics (SH) coefficients and not the global sound fields. In this paper, we try to reduce the reproduction error with a data-driven method. As we all known, the Generative Adversarial Networks (GAN) can be used to generate data similar to a data set. With the GAN, the target sound fields are converted to sound fields that can be reproduced accurately in the proposed approach. The data set of target sound fields is updated with the generated fields which have less reproduction error, and thus reproduction errors are reduced. We simulated the performance with four loudspeakers, sound fields of 4 orders SH coefficients are reproduced with GAN and HOA at 1000 Hz, with average reproduction errors of 0.3 and 0.6, respectively. Simulations show that the space between the least-square solution and the optimization solution is reduced with our method. Furthermore, the performances of HOA are optimized.

Unsupervised domain adaptation aims to use labeled instances from a source domain to train a good learning model, which can classify unlabeled instances from a target domain as accurate as possible. The biggest challenge is that datasets from the source and target domains have different distributions, thus the general classification model trained on the source domain can not perform well on the target domain data. The classic methods solve this problem mainly by narrowing the distance between the source and target domains. Those methods, however, is not optimal since the nonlinear feature space may not match the kernel-based learning machine. In this paper, we design a new method called bi-adapt kernel learning (BAKL) to learn a domain-invariant kernel by transferring the source and target domains to each other simultaneously. Specifically, we derive the new source and target domain kernel matrix according to the Mercer’s theorem. The domain-invariant kernel machines are then constructed by minimizing the approximation error between the newly generated kernel matrices and the ground truth source domain kernel matrices. Experiments on benchmark tasks of text and object recognition demonstrate that it significantly improves classification accuracy compared to the state-of-art methods.

SILK, as an efficient speech codec, has been widely used in real-time interactive communication and can be a good carrier for hiding information. This paper proposes a secure steganographic scheme ...