RainDiffusion:When Unsupervised Learning Meets Diffusion Models for Real-world Image Deraining

What will happen when unsupervised learning meets diffusion models for real-world image deraining? To answer it, we propose RainDiffusion, the first unsupervised image deraining paradigm based on diffusion models. Beyond the traditional unsupervised wisdom of image deraining, RainDiffusion introduces stable training of unpaired real-world data instead of weakly adversarial training. RainDiffusion consists of two cooperative branches: Non-diffusive Translation Branch (NTB) and Diffusive Translation Branch (DTB). NTB exploits a cycle-consistent architecture to bypass the difficulty in unpaired training of standard diffusion models by generating initial clean/rainy image pairs. DTB leverages two conditional diffusion modules to progressively refine the desired output with initial image pairs and diffusive generative prior, to obtain a better generalization ability of deraining and rain generation. Rain-Diffusion is a non adversarial training paradigm, serving as a new standard bar for real-world image deraining. Extensive experiments confirm the superiority of our RainDiffusion over un/semi-supervised methods and show its competitive advantages over fully-supervised ones.Comment: 9 page

Enhanced heterologous protein productivity by genome reduction in Lactococcus lactis NZ9000

Background: The implementation of novel chassis organisms to be used as microbial cell factories in industrial applications is an intensive research field. Lactococcus lactis, which is one of the most extensively studied model organisms, exhibits superior ability to be used as engineered host for fermentation of desirable products. However, few studies have reported about genome reduction of L. lactis as a clean background for functional genomic studies and a model chassis for desirable product fermentation. Results: Four large nonessential DNA regions accounting for 2.83% in L. lactis NZ9000 (L. lactis 9 k) genome (2,530,294 bp) were deleted using the Cre-loxP deletion system as the first steps toward a minimized genome in this study. The mutants were compared with the parental strain in several physiological traits and evaluated as microbial cell factories for heterologous protein production (intracellular and secretory expression) with the red fluorescent protein (RFP) and the bacteriocin leucocin C (LecC) as reporters. The four mutants grew faster, yielded enhanced biomass, achieved increased adenosine triphosphate content, and diminished maintenance demands compared with the wild strain in the two media tested. In particular, L. lactis 9 k-4 with the largest deletion was identified as the optimum candidate host for recombinant protein production. With nisin induction, not only the transcriptional efficiency but also the production levels of the expressed reporters were approximately three-to fourfold improved compared with the wild strain. The expression of lecC gene controlled with strong constitutive promoters P5 and P8 in L. lactis 9 k-4 was also improved significantly. Conclusions: The genome-streamlined L. lactis 9 k-4 outcompeted the parental strain in several physiological traits assessed. Moreover, L. lactis 9 k-4 exhibited good properties as platform organism for protein production. In future works, the genome of L. lactis will be maximally reduced by using our specific design to provide an even more clean background for functional genomics studies than L. lactis 9 k-4 constructed in this study. Furthermore, an improved background will be potentially available for use in biotechology.Peer reviewe

Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal

Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions. We observe that: (i) rain is a mixture of rain streaks and rainy haze; (ii) the scene depth determines the intensity of rain streaks and the transformation into the rainy haze; (iii) most existing deraining methods are only trained on synthetic rainy images, and hence generalize poorly to the real-world scenes. Motivated by these observations, we propose a new SEMI-supervised Mixture Of rain REmoval Generative Adversarial Network (Semi-MoreGAN), which consists of four key modules: (I) a novel attentional depth prediction network to provide precise depth estimation; (ii) a context feature prediction network composed of several well-designed detailed residual blocks to produce detailed image context features; (iii) a pyramid depth-guided non-local network to effectively integrate the image context with the depth information, and produce the final rain-free images; and (iv) a comprehensive semi-supervised loss function to make the model not limited to synthetic datasets but generalize smoothly to real-world heavy rainy scenes. Extensive experiments show clear improvements of our approach over twenty representative state-of-the-arts on both synthetic and real-world rainy images.Comment: 18 page

GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling

Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding.Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity. In this paper, we present a robust semantic segmentation network by deeply exploring the geometry of point clouds, dubbed GeoSegNet. Our GeoSegNet consists of a multi-geometry based encoder and a boundary-guided decoder. In the encoder, we develop a new residual geometry module from multi-geometry perspectives to extract object-level features. In the decoder, we introduce a contrastive boundary learning module to enhance the geometric representation of boundary points. Benefiting from the geometric encoder-decoder modeling, our GeoSegNet can infer the segmentation of objects effectively while making the intersections (boundaries) of two or more objects clear. Experiments show obvious improvements of our method over its competitors in terms of the overall segmentation accuracy and object boundary clearness. Code is available at https://github.com/Chen-yuiyui/GeoSegNet

Polymer Nanoparticle-Based Chemotherapy for Spinal Malignancies

Malignant spinal tumors, categorized into primary and metastatic ones, are one of the most serious diseases due to their high morbidity and mortality rates. Common primary spinal tumors include chordoma, chondrosarcoma, osteosarcoma, Ewing’s sarcoma, and multiple myeloma. Spinal malignancies are not only locally invasive and destructive to adjacent structures, such as bone, neural, and vascular structures, but also disruptive to distant organs (e.g., lung). Current treatments for spinal malignancies, including wide resection, radiotherapy, and chemotherapy, have made significant progress like improving patients’ quality of life. Among them, chemotherapy plays an important role, but its potential for clinical application is limited by severe side effects and drug resistance. To ameliorate the current situation, various polymer nanoparticles have been developed as promising excipients to facilitate the effective treatment of spinal malignancies by utilizing their potent advantages, for example, targeting, stimuli response, and synergetic effect. This review overviews the development of polymer nanoparticles for antineoplastic delivery in the treatment of spinal malignancies and discusses future prospects of polymer nanoparticle-based treatment methods

High Density Lipoprotein Protects Mesenchymal Stem Cells from Oxidative Stress-Induced Apoptosis via Activation of the PI3K/Akt Pathway and Suppression of Reactive Oxygen Species

The therapeutic effect of transplantation of mesenchymal stem cells (MSCs) in myocardial infarction (MI) appears to be limited by poor cell viability in the injured tissue, which is a consequence of oxidative stress and pro-apoptotic factors. High density lipoprotein (HDL) reverses cholesterol transport and has anti-oxidative and anti-apoptotic properties. We, therefore, investigated whether HDL could protect MSCs from oxidative stress-induced apoptosis. MSCs derived from the bone marrow of rats were pre-incubated with or without HDL, and then were exposed to hydrogen peroxide (H2O2) in vitro, or were transplanted into experimentally infarcted hearts of rats in vivo. Pre-incubation of MSCs with HDL increased cell viability, reduced apoptotic indices and resulted in parallel decreases in reactive oxygen species (ROS) in comparison with control MSCs. Each of the beneficial effects of HDL on MSCs was attenuated by inhibiting the PI3K/Akt pathway. Preconditioning with HDL resulted in higher MSC survival rates, improved cardiac remodeling and better myocardial function than in the MSC control group. Collectively, these results suggest that HDL may protect against H2O2-induced apoptosis in MSCs through activation of a PI3K/Akt pathway, and by suppressing the production of ROS