Ints7 deficiency activates DNA damage response to elicit resurgence of endogenous retrovirus MERVL and anastasis of embryonic stem cells.

The murine endogenous retrovirus MERVL is dynamically activated in a small population of in vitro cultured mouse embryonic stem cells (mESCs) exhibiting totipotent-like features. Yet, the relationship between MERVL activation and cell fate decisions of mESCs is incompletely understood. Through a genome-wide knockout screen, we discovered that MERVL activity is intrinsically linked to DNA damage response pathways.

HcN13, a Fibroblast Growth Factor Binding Protein, Is Associated with Shell Formation in Hyriopsis cumingii.

Fibroblast growth factor binding proteins (FGF-BPs) are involved in bone formation by binding to FGFs and modulating FGF signaling in vertebrates. Herein, a novel shell matrix protein gene, HcN13, was identified from the mussels Hyriopsis cumingii. Sequence analysis indicated that HcN13 belongs to the FGF-BP1 family.

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.

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".

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.

ShenKang Injection Attenuates Renal Fibrosis by Inhibiting EMT and Regulating the Wnt/-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/-catenin pathway and explore its potential anti-fibrosis mechanism.

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.

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.

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).

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.

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.

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.

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.

Robust Distance Metric Learning via Bayesian Inference.

Distance metric learning (DML) has achieved great success in many computer vision tasks. However, most existing DML algorithms are based on point estimation, and thus are sensitive to the choice of training examples and tend to be over-fitting in the presence of label noise. In this paper, we present a robust DML algorithm based on Bayesian inference.

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.

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.

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.

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.