Tight junction CLDN2 gene is a direct target of the vitamin D receptor

The breakdown of the intestinal barrier is a common manifestation of many diseases. Recent evidence suggests that vitamin D and its receptor VDR may regulate intestinal barrier function. Claudin-2 is a tight junction protein that mediates paracellular water transport in intestinal epithelia, rendering them "leaky". Using whole body VDR(-/-) mice, intestinal epithelial VDR conditional knockout (VDR(ΔIEC)) mice, and cultured human intestinal epithelial cells, we demonstrate here that the CLDN2 gene is a direct target of the transcription factor VDR. The Caudal-Related Homeobox (Cdx) protein family is a group of the transcription factor proteins which bind to DNA to regulate the expression of genes. Our data showed that VDR-enhances Claudin-2 promoter activity in a Cdx1 binding site-dependent manner. We further identify a functional vitamin D response element (VDRE) 5΄-AGATAACAAAGGTCA-3΄ in the Cdx1 site of the Claudin-2 promoter. It is a VDRE required for the regulation of Claudin-2 by vitamin D. Absence of VDR decreased Claudin-2 expression by abolishing VDR/promoter binding. In vivo, VDR deletion in intestinal epithelial cells led to significant decreased Claudin-2 in VDR(-/-) and VDR(ΔIEC) mice. The current study reveals an important and novel mechanism for VDR by regulation of epithelial barriers.status: publishe

MAE-GAN: A Novel Strategy for Simultaneous Super-resolution Reconstruction and Denoising of Post-stack Seismic Profile

Post-stack seismic profiles are images reflecting containing geological structures which provides a critical foundation for understanding the distribution of oil and gas resources. However, due to the limitations of seismic acquisition equipment and data collecting geometry, the post-stack profiles suffer from low resolution and strong noise issues, which severely affects subsequent seismic interpretation. To better enhance the spatial resolution and signal-to-noise ratio of post-seismic profiles, a multi-scale attention encoder-decoder network based on generative adversarial network (MAE-GAN) is proposed. This method improves the resolution of post-stack profiles, and effectively suppresses noises and recovers weak signals as well. A multi-scale residual module is proposed to extract geological features under different receptive fields. At the same time, an attention module is designed to further guide the network to focus on important feature information. Additionally, to better recover the global and local information of post-stack profiles, an adversarial network based on a Markov discriminator is proposed. Finally, by introducing an edge information preservation loss function, the conventional loss function of the Generative Adversarial Network is improved, which enables better recovery of the edge information of the original post-stack profiles. Experimental results on simulated and field post-stack profiles demonstrate that the proposed MAE-GAN method outperforms two advanced convolutional neural network-based methods in noise suppression and weak signal recovery. Furthermore, the profiles reconstructed by the MAE-GAN method preserve more geological structures

Transformer For Low-frequency Extrapolating of Seismic Data

Full waveform inversion (FWI) is used to reconstruct the physical properties of subsurface media which plays an important role in seismic exploration. However, the precision of FWI is seriously affected by the absence or inaccuracy of low-frequency information. Therefore, reconstructing the low-frequency signals accurately is highly significant in seismic data processing. Low-frequency extrapolation of seismic records can be approached as a deep learning regression problem. Thus, to obtain low-frequency information from band-limited seismic records, a novel network structure called low-frequency extrapolation transformer (LFET) is proposed to construct the nonlinear mapping relationship between the data missing low-frequency and low-frequency data in a supervised learning approach, which is inspired by the transformer model widely used in natural language processing (NLP). We apply multi-head self-attention (MSA) modules to model the remote dependencies of seismic data. Based on this, we introduce a shifted window partitioning approach to reduce the calculating amount. Due to the field data are not suitable for supervised learning, we generate synthetic seismic records using submodels selected from the benchmark Marmousi model as training data whose characteristics are similar to that of the field data. A single trace of synthetic band-limited seismic data in the time domain is used as the input data, and the parameters of LFET are updated based on the errors between the predicted trace and the corresponding label. The experimental results on the data generated by different models, different wavelets, and different kinds of field marine data demonstrate the feasibility and generalization of the proposed method. Furthermore, the proposed method achieves higher accuracy with lower computational expense than the traditional CNN method

Seismic Interpolation Transformer for Consecutively Missing Data: A Case Study in DAS-VSP Data

Distributed optical fiber acoustic sensing (DAS) is a rapidly-developed seismic acquisition technology with advantages of low cost, high resolution, high sensitivity, and small interval, etc. Nonetheless, consecutively missing cases often appear in real seismic data acquired by DAS system due to some factors, including optical fiber damage and inferior coupling between cable and well. Recently, some deep-learning seismic interpolation methods based on convolutional neural network (CNN) have shown impressive performance in regular and random missing cases but still remain the consecutively missing case as a challenging task. The main reason is that the weight sharing makes it difficult for CNN to capture enough comprehensive features. In this paper, we propose a transformer-based interpolation method, called seismic interpolation transformer (SIT), to deal with the consecutively missing case. This proposed SIT is an encoder-decoder structure connected by some U-shaped swin-transformer blocks. In encoder and decoder part, the multi-head self-attention (MSA) mechanism is used to capture global features which is essential for the reconstruction of consecutively missing traces. The U-shaped swin-transformer blocks are utilized to perform feature extraction operations on feature maps with different resolutions. Moreover, we combine the loss based on structural similarity index (SSIM) and L1 norm to propose a novel loss function for SIT. In experiments, this proposed SIT outperforms U-Net and swin-transformer. Moreover, ablation studies also demonstrate the advantages of new network architecture and loss function

Chronic Effects of a Salmonella Type III Secretion Effector Protein AvrA In Vivo

Salmonella infection is a common public health problem that can become chronic and increase the risk of inflammatory bowel diseases and cancer. AvrA is a Salmonella bacterial type III secretion effector protein. Increasing evidence demonstrates that AvrA is a multi-functional enzyme with critical roles in inhibiting inflammation, regulating apoptosis, and enhancing proliferation. However, the chronic effects of Salmonella and effector AvrA in vivo are still unknown. Moreover, alive, mutated, non-invasive Salmonella is used as a vector to specifically target cancer cells. However, studies are lacking on chronic infection with non-pathogenic or mutated Salmonella in the host.We infected mice with Salmonella Typhimurium for 27 weeks and investigated the physiological effects as well as the role of AvrA in intestinal inflammation. We found altered body weight, intestinal pathology, and bacterial translocation in spleen, liver, and gallbladder in chronically Salmonella-infected mice. Moreover, AvrA suppressed intestinal inflammation and inhibited the secretion of cytokines IL-12, IFN-gamma, and TNF-alpha. AvrA expression in Salmonella enhanced its invasion ability. Liver abscess and Salmonella translocation in the gallbladder were observed and may be associated with AvrA expression in Salmonella.We created a mouse model with persistent Salmonella infection in vivo. Our study further emphasizes the importance of the Salmonella effector protein AvrA in intestinal inflammation, bacterial translocation, and chronic infection in vivo

Vitamin D receptor protects against dysbiosis and tumorigenesis via the JAK/STAT pathway in intestine

BACKGROUND & AIMS: Vitamin D exerts regulatory roles via vitamin D receptor (VDR) in mucosal immunity, host defense, and inflammation involving host factors and microbiome. Human Vdr gene variation shapes the microbiome and VDR deletion leads to dysbiosis. Low VDR expression and diminished vitamin D/VDR signaling are observed in colon cancer. Nevertheless, how intestinal epithelial VDR is involved in tumorigenesis through gut microbiota remains unknown. We hypothesized that intestinal VDR protects mice against dysbiosis via modulating the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway in tumorigenesis. METHODS: To test our hypothesis, we used an azoxymethane/dextran sulfate sodium-induced cancer model in intestinal VDR conditional knockout (VDR RESULTS: VDR CONCLUSIONS: We provide insights into the mechanism of VDR dysfunction leading to dysbiosis and tumorigenesis. It indicates a new target: microbiome and VDR for the prevention of cancer

Constraints on the structure of the oceanic crust of the Tamu Massif by teleseismic P-wave coda autocorrelation

The Tamu Massif, considered the biggest single volcano on Earth, was formed by the accumulation of enormous amounts of magma erupting to the surface. It is the largest and oldest seamount in Shatsky Rise, which is the third largest oceanic plateau on Earth. However, the formation mechanism of Tamu Massif is still controversial because evidence point to different formation hypotheses. In this paper, we applied the P-wave coda autocorrelation method and used the hydrophone waveform data acquired by the ocean bottom seismometer (OBS) deployed on Tamu Massif to constrain the oceanic crust, and these results provide new finding on the structure of the oceanic crust for Tamu Massif. We hope it can provide some implications to research the formation mechanism of Tamu Massif. These results show that some stations in Tamu Massif received reflection signals from shallower depths that are nearly parallel to the seafloor. We infer that in the shallow oceanic crust, there is a layer composed of alternating eruptions of dense, higher velocity massive lava and sparse, lower velocity pillow lava flows, which have less density and lower velocity compared to the lower oceanic crust, with a strong acoustic impedance contrasts between them and thus able to generate a reflection signal, which is observed in our autocorrelation results

Specification Test for Fixed Effects in Binary Panel Data Model: A Simulation Study

Abstract In this paper, we examine the specification tests which have been proposed for fixed effects in binary panel data model, using several different data generating processes to evaluate the performance of the specification test in different situations. By simulations, we find the specification test based on moment conditions is able to outperform the Lagrange multiplier test proposed b

Heterogeneous Distribution of Erucic Acid in Brassica napus Seeds

Article describes study examining low- and high-erucic acid accessions of B. napus seeds for the distribution of erucic acid-containing lipids and the gene transcripts encoding the enzymes involved in pathways for its incorporation into triacylglycerols (TAGs) across the major tissues of the seeds