Effects of four polyphenols on mouse wound healing and the gene expression profile of resveratrol action

Studies have demonstrated the potent effects of polyphenols on cutaneous wound healing. However, the molecular mechanisms underlying polyphenol activity are incompletely understood. Herein, mice were experimentally wounded, intragastrically treated with four polyphenols, resveratrol, tea polyphenols, genistein, and quercetin; and monitored for 14 days. Resveratrol was the most effective compound, promoting wound healing starting at day 7 after wounding, by enhancing cell proliferation and reducing apoptosis and subsequently promoting epidermal and dermal repair, collagen synthesis and scar maturation. RNA sequencing was performed in control and resveratrol-treated tissues on day 7 after wounding. Resveratrol treatment upregulated 362 genes and downregulated 334 genes. Gene Ontology enrichment analysis showed that differentially expressed genes (DEGs) were associated with different biological processes (keratinization, immunity, and inflammation), molecular functions (cytokine and chemokine activities), and cellular components (extracellular region and matrix). Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that DEGs were predominantly enriched in inflammatory and immunological pathways, including cytokine-cytokine receptor interaction, chemokine signaling, and tumor necrosis factor (TNF) signaling. These results show that resveratrol accelerates wound healing by promoting keratinization and dermal repair and attenuating immune and inflammatory responses

The Resistance of Ship Web Girders in Collision and Grounding

Ship web girders play an important role in ship structure performance during collision and grounding accidents. The behavior of web girders subjected to in-plane concentrated load is investigated by numerical simulation and theoretical analysis in this paper. A numerical simulation based on previous experiment is conducted to give insight to the deformation mechanism of crushing web girders. Some new important deformation characteristics are observed through the simulation results. A new theoretical deformation model is proposed featured with these deformation characteristics, and a simplified analytical method for predicting the instantaneous and mean resistances of crushing web girders is proposed. The proposed method is verified by two previous experiments and a series of numerical simulations. The agreement between the solutions by the proposed method and the experiment results is good. The comparison results between the proposed analytical method and numerical simulation results are satisfactory for most cases. The proposed analytical method will contribute to the establishment of an efficient method for fast and reliable assessment of the outcome of ship accidental collisions and grounding events

Training Energy-Based Models with Diffusion Contrastive Divergences

Energy-Based Models (EBMs) have been widely used for generative modeling. Contrastive Divergence (CD), a prevailing training objective for EBMs, requires sampling from the EBM with Markov Chain Monte Carlo methods (MCMCs), which leads to an irreconcilable trade-off between the computational burden and the validity of the CD. Running MCMCs till convergence is computationally intensive. On the other hand, short-run MCMC brings in an extra non-negligible parameter gradient term that is difficult to handle. In this paper, we provide a general interpretation of CD, viewing it as a special instance of our proposed Diffusion Contrastive Divergence (DCD) family. By replacing the Langevin dynamic used in CD with other EBM-parameter-free diffusion processes, we propose a more efficient divergence. We show that the proposed DCDs are both more computationally efficient than the CD and are not limited to a non-negligible gradient term. We conduct intensive experiments, including both synthesis data modeling and high-dimensional image denoising and generation, to show the advantages of the proposed DCDs. On the synthetic data learning and image denoising experiments, our proposed DCD outperforms CD by a large margin. In image generation experiments, the proposed DCD is capable of training an energy-based model for generating the Celab-A $32\times 32$ dataset, which is comparable to existing EBMs

UNeR3D: Versatile and Scalable 3D RGB Point Cloud Generation from 2D Images in Unsupervised Reconstruction

In the realm of 3D reconstruction from 2D images, a persisting challenge is to achieve high-precision reconstructions devoid of 3D Ground Truth data reliance. We present UNeR3D, a pioneering unsupervised methodology that sets a new standard for generating detailed 3D reconstructions solely from 2D views. Our model significantly cuts down the training costs tied to supervised approaches and introduces RGB coloration to 3D point clouds, enriching the visual experience. Employing an inverse distance weighting technique for color rendering, UNeR3D ensures seamless color transitions, enhancing visual fidelity. Our model's flexible architecture supports training with any number of views, and uniquely, it is not constrained by the number of views used during training when performing reconstructions. It can infer with an arbitrary count of views during inference, offering unparalleled versatility. Additionally, the model's continuous spatial input domain allows the generation of point clouds at any desired resolution, empowering the creation of high-resolution 3D RGB point clouds. We solidify the reconstruction process with a novel multi-view geometric loss and color loss, demonstrating that our model excels with single-view inputs and beyond, thus reshaping the paradigm of unsupervised learning in 3D vision. Our contributions signal a substantial leap forward in 3D vision, offering new horizons for content creation across diverse applications. Code is available at https://github.com/HongbinLin3589/UNeR3D.Comment: 17 page

Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models

Due to the ease of training, ability to scale, and high sample quality, diffusion models (DMs) have become the preferred option for generative modeling, with numerous pre-trained models available for a wide variety of datasets. Containing intricate information about data distributions, pre-trained DMs are valuable assets for downstream applications. In this work, we consider learning from pre-trained DMs and transferring their knowledge to other generative models in a data-free fashion. Specifically, we propose a general framework called Diff-Instruct to instruct the training of arbitrary generative models as long as the generated samples are differentiable with respect to the model parameters. Our proposed Diff-Instruct is built on a rigorous mathematical foundation where the instruction process directly corresponds to minimizing a novel divergence we call Integral Kullback-Leibler (IKL) divergence. IKL is tailored for DMs by calculating the integral of the KL divergence along a diffusion process, which we show to be more robust in comparing distributions with misaligned supports. We also reveal non-trivial connections of our method to existing works such as DreamFusion, and generative adversarial training. To demonstrate the effectiveness and universality of Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion models and refining existing GAN models. The experiments on distilling pre-trained diffusion models show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. The experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models across various settings

DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction

Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames. However, they neither fully exploit the 3D point-wise geometric correspondences, nor effectively tackle the ambiguities in the photometric warping caused by occlusions or illumination inconsistency. To address these problems, this work proposes Density Volume Construction Network (DevNet), a novel self-supervised monocular depth learning framework, that can consider 3D spatial information, and exploit stronger geometric constraints among adjacent camera frustums. Instead of directly regressing the pixel value from a single image, our DevNet divides the camera frustum into multiple parallel planes and predicts the pointwise occlusion probability density on each plane. The final depth map is generated by integrating the density along corresponding rays. During the training process, novel regularization strategies and loss functions are introduced to mitigate photometric ambiguities and overfitting. Without obviously enlarging model parameters size or running time, DevNet outperforms several representative baselines on both the KITTI-2015 outdoor dataset and NYU-V2 indoor dataset. In particular, the root-mean-square-deviation is reduced by around 4% with DevNet on both KITTI-2015 and NYU-V2 in the task of depth estimation. Code is available at https://github.com/gitkaichenzhou/DevNet.Comment: Accepted by European Conference on Computer Vision 2022 (ECCV2022

Complexity Matters: Rethinking the Latent Space for Generative Modeling

In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates images through a paired decoder. Although the selection of the latent space is empirically pivotal, determining the optimal choice and the process of identifying it remain unclear. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity. Our investigation starts with the classic generative adversarial networks (GANs). Inspired by the GAN training objective, we propose a novel "distance" between the latent and data distributions, whose minimization coincides with that of the generator complexity. The minimizer of this distance is characterized as the optimal data-dependent latent that most effectively capitalizes on the generator's capacity. Then, we consider parameterizing such a latent distribution by an encoder network and propose a two-stage training strategy called Decoupled Autoencoder (DAE), where the encoder is only updated in the first stage with an auxiliary decoder and then frozen in the second stage while the actual decoder is being trained. DAE can improve the latent distribution and as a result, improve the generative performance. Our theoretical analyses are corroborated by comprehensive experiments on various models such as VQGAN and Diffusion Transformer, where our modifications yield significant improvements in sample quality with decreased model complexity.Comment: Accepted to NeurIPS 2023 (Spotlight

Divergence of exonic splicing elements after gene duplication and the impact on gene structures

An analysis of human exonic splicing elements in duplicated genes reveals their important role in the generation of new gene structures

ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning

Although many recent works have investigated generalizable NeRF-based novel view synthesis for unseen scenes, they seldom consider the synthetic-to-real generalization, which is desired in many practical applications. In this work, we first investigate the effects of synthetic data in synthetic-to-real novel view synthesis and surprisingly observe that models trained with synthetic data tend to produce sharper but less accurate volume densities. For pixels where the volume densities are correct, fine-grained details will be obtained. Otherwise, severe artifacts will be produced. To maintain the advantages of using synthetic data while avoiding its negative effects, we propose to introduce geometry-aware contrastive learning to learn multi-view consistent features with geometric constraints. Meanwhile, we adopt cross-view attention to further enhance the geometry perception of features by querying features across input views. Experiments demonstrate that under the synthetic-to-real setting, our method can render images with higher quality and better fine-grained details, outperforming existing generalizable novel view synthesis methods in terms of PSNR, SSIM, and LPIPS. When trained on real data, our method also achieves state-of-the-art results

Noisy splicing, more than expression regulation, explains why some exons are subject to nonsense-mediated mRNA decay

<p>Abstract</p> <p>Background</p> <p>Nonsense-mediated decay is a mechanism that degrades mRNAs with a premature termination codon. That some exons have premature termination codons at fixation is paradoxical: why make a transcript if it is only to be destroyed? One model supposes that splicing is inherently noisy and spurious transcripts are common. The evolution of a premature termination codon in a regularly made unwanted transcript can be a means to prevent costly translation. Alternatively, nonsense-mediated decay can be regulated under certain conditions so the presence of a premature termination codon can be a means to up-regulate transcripts needed when nonsense-mediated decay is suppressed.</p> <p>Results</p> <p>To resolve this issue we examined the properties of putative nonsense-mediated decay targets in humans and mice. We started with a well-annotated set of protein coding genes and found that 2 to 4% of genes are probably subject to nonsense-mediated decay, and that the premature termination codon reflects neither rare mutations nor sequencing artefacts. Several lines of evidence suggested that the noisy splicing model has considerable relevance: 1) exons that are uniquely found in nonsense-mediated decay transcripts (nonsense-mediated decay-specific exons) tend to be newly created; 2) have low-inclusion level; 3) tend not to be a multiple of three long; 4) belong to genes with multiple splice isoforms more often than expected; and 5) these genes are not obviously enriched for any functional class nor conserved as nonsense-mediated decay candidates in other species. However, nonsense-mediated decay-specific exons for which distant orthologous exons can be found tend to have been under purifying selection, consistent with the regulation model.</p> <p>Conclusion</p> <p>We conclude that for recently evolved exons the noisy splicing model is the better explanation of their properties, while for ancient exons the nonsense-mediated decay regulated gene expression is a viable explanation.</p