HcN57, A Novel Unusual Acidic Silk-Like Matrix Protein from Hyriopsis cumingii, Participates in Framework Construction and Nacre Nucleation During Nacreous Layer Formation.

In the classic molecular model of nacreous layer formation, unusual acidic matrix proteins rich in aspartic acid (Asp) residues are essential for nacre nucleation due to their great affinity for binding calcium. However, the acidic matrix proteins discovered in the nacreous layer so far have been weakly acidic with a high proportion of glutamate. In the present study, several silk-like matrix proteins, including the novel matrix protein HcN57, were identified in the ethylenediaminetetraacetic acid-soluble extracts of the nacreous layer of Hyriopsis cumingii. HcN57 is a highly repetitive protein that consists of a high proportion of alanine (Ala, 34.4%), glycine (Gly, 22.5%), and serine (Ser, 11.4%). It forms poly Ala blocks, GlynX repeats, an Ala-Gly repeat, and a Ser-Ala-rich region, exhibiting significant similarity to silk proteins found in spider species. The expression of HcN57 was specifically located in the dorsal epithelial cells of the mantle pallium and mantle center. Notably, expression of HcN57 was relatively high during nacreous layer regeneration and pearl nacre deposition, suggesting HcN57 is a silk matrix protein in the nacreous layer. Importantly, HcN57 also contains a certain content of Asp residues, making it an unusual acidic matrix protein present in the nacreous layer. These Asp residues are mainly distributed in three large hydrophilic acidic regions, which showed inhibitory activity against aragonite deposition and morphological regulation of calcite in vitro. Moreover, HcN57-dsRNA injection resulted in failure of nacre nucleation in vivo. Taken together, our results show that HcN57 is a bifunctional silk protein with poly Ala blocks and Gly-rich regions that serve as space fillers within the chitinous framework to prevent crystallization at unnecessary nucleation sites and Asp-rich regions that create a calcium ion supersaturated microenvironment for nucleation in the center of nacre tablets. These observations contribute to a better understanding of the mechanism by which silk proteins regulate framework construction and nacre nucleation during nacreous layer formation.

Sensitive Quantitative In Vivo Assay for Evaluating the Effects of Biomolecules on Hair Growth and Coloring Using Direct Microinjections into Mouse Whisker Follicles.

Many people suffer from hair loss and abnormal skin pigmentation, highlighting the need for simple assays to support drug discovery research. Current assays have various limitations, such as being in vitro only, not sensitive enough, or unquantifiable. We took advantage of the bilateral symmetry and large size of mouse whisker follicles to develop a novel in vivo assay called "whisker follicle microinjection assay". In this assay, we plucked mouse whiskers and then injected molecules directly into one side of the whisker follicles using microneedles that were a similar size to the whiskers, and we injected solvent on the other side as a control. Once the whiskers grew out again, we quantitatively measured their length and color intensity to evaluate the effects of the molecules on hair growth and coloring. Several chemicals and proteins were used to test this assay. The chemicals minoxidil and ruxolitinib, as well as the protein RSPO1, promoted hair growth. The effect of the clinical drug minoxidil could be detected at a concentration as low as 0.001%. The chemical deoxyarbutin inhibited melanin production. The protein Nbl1 was identified as a novel hair-growth inhibitor. In conclusion, we successfully established a sensitive and quantitative in vivo assay to evaluate the effects of chemicals and proteins on hair growth and coloring and identified a novel regulator by using this assay. This whisker follicle microinjection assay will be useful when investigating protein functions and when developing drugs to treat hair loss and abnormal skin pigmentation.

A Cre knockin mouse reveals specific expression of Agouti gene in mesenchymal lineage cells in multiple organs and provides a unique tool for conditional gene targeting.

The mouse Agouti gene encodes a paracrine signaling factor which promotes melanocytes to produce yellow instead of black pigment. It has been reported that Agouti mRNA is confined to the dermal papilla after birth in various mammalian species. In this study, we created and characterized a knockin mouse strain in which Cre recombinase was expressed in-frame with endogenous Agouti coding sequence. The Agouti-Cre mice were bred with reporter mice (Rosa26-tdTomato or Rosa26-ZsGreen) to trace the lineage of Agouti-expressing cells during development. In skin, the reporter was detected in some dermal fibroblasts at the embryonic stage and in all dermal fibroblasts postnatally. It was also expressed in all mesenchymal lineage cells in other organs/tissues, including eyes, tongue, muscle, intestine, adipose, prostate and testis. Interestingly, the reporter expression was excluded from epithelial cells in the above organs/tissues. In brain, the reporter was observed in the outermost meningeal fibroblasts. Our work helps to illustrate the Agouti expression pattern during development and provides a valuable mouse strain for conditional gene targeting in mesenchymal lineage cells in multiple organs.

SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multiagent Reinforcement Learning.

Learning a stable and generalizable centralized value function (CVF) is a crucial but challenging task in multiagent reinforcement learning (MARL), as it has to deal with the issue that the joint action space increases exponentially with the number of agents in such scenarios. This article proposes an approach, named SMIX( λ ), that uses an OFF-policy training to achieve this by avoiding the greedy assumption commonly made in CVF learning. As importance sampling for such OFF-policy training is both computationally costly and numerically unstable, we proposed to use the λ -return as a proxy to compute the temporal difference (TD) error. With this new loss function objective, we adopt a modified QMIX network structure as the base to train our model. By further connecting it with the Q(λ) approach from a unified expectation correction viewpoint, we show that the proposed SMIX( λ ) is equivalent to Q(λ) and hence shares its convergence properties, while without being suffered from the aforementioned curse of dimensionality problem inherent in MARL. Experiments on the StarCraft Multiagent Challenge (SMAC) benchmark demonstrate that our approach not only outperforms several state-of-the-art MARL methods by a large margin but also can be used as a general tool to improve the overall performance of other centralized training with decentralized execution (CTDE)-type algorithms by enhancing their CVFs.

Robust RGB-T Tracking via Graph Attention-Based Bilinear Pooling.

RGB-T tracker possesses strong capability of fusing two different yet complementary target observations, thus providing a promising solution to fulfill all-weather tracking in intelligent transportation systems. Existing convolutional neural network (CNN)-based RGB-T tracking methods often consider the multisource-oriented deep feature fusion from global viewpoint, but fail to yield satisfactory performance when the target pair only contains partially useful information. To solve this problem, we propose a four-stream oriented Siamese network (FS-Siamese) for RGB-T tracking. The key innovation of our network structure lies in that we formulate multidomain multilayer feature map fusion as a multiple graph learning problem, based on which we develop a graph attention-based bilinear pooling module to explore the partial feature interaction between the RGB and the thermal targets. This can effectively avoid uninformed image blocks disturbing feature embedding fusion. To enhance the efficiency of the proposed Siamese network structure, we propose to adopt meta-learning to incorporate category information in the updating of bilinear pooling results, which can online enforce the exemplar and current target appearance obtaining similar sematic representation. Extensive experiments on grayscale-thermal object tracking (GTOT) and RGBT234 datasets demonstrate that the proposed method outperforms the state-of-the-art methods for the task of RGB-T tracking.

ShenKang Injection Attenuates Renal Fibrosis by Inhibiting EMT and Regulating the Wnt/ß-Catenin Pathway.

Shenkang Injection (SKI) is a traditional Chinese medicine injection commonly used in the clinical treatment of chronic kidney disease. Although it has been confirmed that SKI has anti-kidney fibrosis effects, the underlying mechanism remains unclear. To investigate the effects of SKI on epithelial-mesenchymal transition (EMT) and Wnt/ß-catenin pathway and explore its potential anti-fibrosis mechanism. A unilateral ureteral obstruction (UUO) model was induced by ligating the left ureter of male SD rats. A total of 24 rats were randomly divided into the following four groups: sham group, model group, SKI group, and benazepril group. The rats in each group were treated for 28 days, and renal function was evaluated by blood urea nitrogen (BUN) and serum creatinine (Scr). The degree of renal fibrosis was assessed by hematoxylin and eosin (HE) and Masson staining. Extracellular matrix (ECM) deposition was evaluated by immunohistochemistry. Real-time fluorescent quantitative PCR (RT-qPCR) and western blotting were used to detect the expression of genes and proteins in the Wnt/ß-catenin signaling pathway. Further studies were performed in vitro using HK-2 cells treated with TGF-ß1. At 28 days postoperation, the levels of BUN and Scr expression were significantly increased in the UUO group. SKI and benazepril reduced the levels of BUN and Scr, which displayed protective renal effects. Pathological staining showed that compared with the sham operation group, the renal parenchymal structure was severely damaged, the number of glomeruli was reduced, and a large amount of collagen was deposited in the kidney tissue of the UUO group. SKI treatment reduced morphological changes. Immunohistochemistry showed that compared with the sham operation group, the content of collagen I and FN in the kidney tissue of the UUO group were significantly increased, whereas the SKI content was decreased. In addition, compared with the UUO group, the levels of Wnt1, active ß-catenin, Snail1, and PAI-1 expression were reduced in the SKI group, suggesting that SKI may reduce renal fibrosis by mediating the Wnt/ß-catenin pathway. Further in vitro studies showed that collagen I, FN, and α-SMA levels in HK-2 cells were significantly increased following stimulation with TGF-ß1. SKI could significantly reduce the expression of collagen I, FN, and α-SMA. A scratch test showed that SKI could reduce HK-2 migration. In addition, by stimulating TGF-ß1, the levels of Wnt1, active ß-catenin, snail1, and PAI-1 were significantly upregulated. SKI treatment could inhibit the activity of the Wnt/ß-catenin signaling pathway in HK-2 cells. SKI improves kidney function by inhibiting renal fibrosis. The anti-fibrotic effects may be mediated by regulation of the Wnt/ß-catenin pathway and EMT inhibition.

Ferroptosis-related genes are candidate diagnostic and prognostic biomarkers for skin cutaneous melanoma.

Skin cutaneous melanoma (SKCM) is a disease with the highest mortality rate among skin cancers. As a new type of programmed cell death, ferroptosis has been confirmed to be related to the occurrence and development of a variety of cancers. At present, the expression and prognostic value of ferroptosis-related genes (FRGs) in SKCM are still unclear. In this study, we selected seven FRGs that were differentially expressed in SKCM and related to the patient's prognosis through the databases. Further studies have shown that these genes are closely related to immune cell infiltration and immune checkpoints. All in all, these seven FRGs may be potential targets for clinical diagnosis, prognosis and treatment of SKCM patients.

A Lightweight Encoder-Decoder Path for Deep Residual Networks.

In this article, we present a novel lightweight path for deep residual neural networks. The proposed method integrates a simple plug-and-play module, i.e., a convolutional encoder-decoder (ED), as an augmented path to the original residual building block. Due to the abstract design and ability of the encoding stage, the decoder part tends to generate feature maps where highly semantically relevant responses are activated, while irrelevant responses are restrained. By a simple elementwise addition operation, the learned representations derived from the identity shortcut and original transformation branch are enhanced by our ED path. Furthermore, we exploit lightweight counterparts by removing a portion of channels in the original transformation branch. Fortunately, our lightweight processing does not cause an obvious performance drop but brings a computational economy. By conducting comprehensive experiments on ImageNet, MS-COCO, CUB200-2011, and CIFAR, we demonstrate the consistent accuracy gain obtained by our ED path for various residual architectures, with comparable or even lower model complexity. Concretely, it decreases the top-1 error of ResNet-50 and ResNet-101 by 1.22% and 0.91% on the task of ImageNet classification and increases the mmAP of Faster R-CNN with ResNet-101 by 2.5% on the MS-COCO object detection task. The code is available at https://github.com/Megvii-Nanjing/ED-Net.

Emerging role of TWEAK-Fn14 axis in lupus, a disease related to autoimmunity and fibrosis.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder. Lupus nephritis (LN) is one of the severe clinical implications in SLE, and this was relates to fibrosis in the kidney. As an important marker in the tumor necrosis factor (TNF) superfamily, TNF-like weak inducer of apoptosis (TWEAK) has been given much attention with respect to its role in regulating pro-inflammatory immune response. Fibroblast growth factor-inducible 14 (Fn14), the sole receptor for TWEAK, has been found expressed in different immune and non-immune cells. TWEAK binds to Fn14, and then regulates inflammatory components production via downstream signaling pathways. To date, dysregulated expression of TWEAK, Fn14 has been reported in SLE, LN patients, and in vivo, in vitro studies have discussed the significant role of TWEAK-Fn14 axis in SLE, LN pathogenesis, partly through mediating the fibrosis process. In this review, we will discuss the association of TWEAK-Fn14 axis in lupus. Understanding the relationship will better realize the potential for making TWEAK-Fn14 as a marker for the diseases, and will help to give many clues for targeting them in treatment of lupus in the future.

Interleukin-33/ Suppression of Tumorigenicity 2 in Renal Fibrosis: Emerging Roles in Prognosis and Treatment.

Chronic kidney disease (CKD) is a major public health problem that affects more than 10% of the population worldwide and has a high mortality rate. Therefore, it is necessary to identify novel treatment strategies for CKD. Incidentally, renal fibrosis plays a central role in the progression of CKD to end-stage renal disease (ESRD). The activation of inflammatory pathways leads to the development of renal fibrosis. In fact, interleukin-33 (IL-33), a newly discovered member of the interleukin 1 (IL-1) cytokine family, is a crucial regulator of the inflammatory process. It exerts pro-inflammatory and pro-fibrotic effects via the suppression of tumorigenicity 2 (ST2) receptor, which, in turn, activates other inflammatory pathways. Although the role of this pathway in cardiac, pulmonary, and hepatic fibrotic diseases has been extensively studied, its precise role in renal fibrosis has not yet been completely elucidated. Recent studies have shown that a sustained activation of IL-33/ST2 pathway promotes the development of renal fibrosis. However, with prolonged research in this field, it is expected that the IL-33/ST2 pathway will be used as a diagnostic and prognostic tool for renal diseases. In addition, the IL-33/ST2 pathway seems to be a new target for the future treatment of CKD. Here, we review the mechanisms and potential applications of the IL-33/ST2 pathway in renal fibrosis; such that it can help clinicians and researchers to explore effective treatment options and develop novel medicines for CKD patients.

Pornographic Image Recognition via Weighted Multiple Instance Learning.

In the era of Internet, recognizing pornographic images is of great significance for protecting children's physical and mental health. However, this task is very challenging as the key pornographic contents (e.g., breast and private part) in an image often lie in local regions of small size. In this paper, we model each image as a bag of regions, and follow a multiple instance learning (MIL) approach to train a generic region-based recognition model. Specifically, we take into account the regions' degree of pornography, and make three main contributions. First, we show that based on very few annotations of the key pornographic contents in a training image, we can generate a bag of properly sized regions, among which the potential positive regions usually contain useful contexts that can aid recognition. Second, we present a simple quantitative measure of a region's degree of pornography, which can be used to weigh the importance of different regions in a positive image. Third, we formulate the recognition task as a weighted MIL problem under the convolutional neural network framework, with a bag probability function introduced to combine the importance of different regions. Experiments on our newly collected large scale dataset demonstrate the effectiveness of the proposed method, achieving an accuracy with 97.52% true positive rate at 1% false positive rate, tested on 100K pornographic images and 100K normal images.

Bayesian Neighborhood Component Analysis.

Learning a distance metric in feature space potentially improves the performance of the nearest neighbor classifier and is useful in many real-world applications. Many metric learning (ML) algorithms are, however, based on the point estimation of a quadratic optimization problem, which is time-consuming, susceptible to overfitting, and lacks a natural mechanism to reason with parameter uncertainty-a property useful especially when the training set is small and/or noisy. To deal with these issues, we present a novel Bayesian ML (BML) method, called Bayesian neighborhood component analysis (NCA), based on the well-known NCA method, in which the metric posterior is characterized by the local label consistency constraints of observations, encoded with a similarity graph instead of independent pairwise constraints. For efficient Bayesian inference, we explore the variational lower bound over the log-likelihood of the original NCA objective. Experiments on several publicly available data sets demonstrate that the proposed method is able to learn robust metric measures from small size data set and/or from challenging training set with labels contaminated by errors. The proposed method is also shown to outperform a previous pairwise constrained BML method.

Enhanced local texture feature sets for face recognition under difficult lighting conditions.

Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three main contributions: 1) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; 2) we introduce local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions, and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and 3) we further improve robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources--Gabor wavelets and LBP--showing that the combination is considerably more accurate than either feature set alone. The resulting method provides state-of-the-art performance on three data sets that are widely used for testing recognition under difficult illumination conditions: Extended Yale-B, CAS-PEAL-R1, and Face Recognition Grand Challenge version 2 experiment 4 (FRGC-204). For example, on the challenging FRGC-204 data set it halves the error rate relative to previously published methods, achieving a face verification rate of 88.1% at 0.1% false accept rate. Further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets, data sets and lighting conditions.

Generalized low-rank approximations of matrices revisited.

Compared to singular value decomposition (SVD), generalized low-rank approximations of matrices (GLRAM) can consume less computation time, obtain higher compression ratio, and yield competitive classification performance. GLRAM has been successfully applied to applications such as image compression and retrieval, and quite a few extensions have been successively proposed. However, in literature, some basic properties and crucial problems with regard to GLRAM have not been explored or solved yet. For this sake, we revisit GLRAM in this paper. First, we reveal such a close relationship between GLRAM and SVD that GLRAM's objective function is identical to SVD's objective function except the imposed constraints. Second, we derive a lower bound of GLRAM's objective function, and discuss when the lower bound can be touched. Moreover, from the viewpoint of minimizing the lower bound, we answer one open problem raised by Ye (Machine Learning, 2005), i.e., a theoretical justification of the experimental phenomenon that, under given number of reduced dimension, the lowest reconstruction error is obtained when the left and right transformations have equal number of columns. Third, we explore when and why GLRAM can perform well in terms of compression, which is a fundamental problem concerning the usability of GLRAM.

Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kappa-NN ensemble.

Most classical template-based frontal face recognition techniques assume that multiple images per person are available for training, while in many real-world applications only one training image per person is available and the test images may be partially occluded or may vary in expressions. This paper addresses those problems by extending a previous local probabilistic approach presented by Martinez, using the self-organizing map (SOM) instead of a mixture of Gaussians to learn the subspace that represented each individual. Based on the localization of the training images, two strategies of learning the SOM topological space are proposed, namely to train a single SOM map for all the samples and to train a separate SOM map for each class, respectively. A soft kappa nearest neighbor (soft kappa-NN) ensemble method, which can effectively exploit the outputs of the SOM topological space, is also proposed to identify the unlabeled subjects. Experiments show that the proposed method exhibits high robust performance against the partial occlusions and variant expressions.