From rule-based to learning-based image-conditional image generation

Visual contents, such as movies, animations, computer games, videos and photos, are massively produced and consumed nowadays. Most of these contents are the combination of materials captured from real-world and contents synthesized by computers. Particularly, computer-generated visual contents are increasingly indispensable in modern entertainment and production. The generation of visual contents by computers is typically conditioned on real-world materials, driven by the imagination of designers and artists, or a combination of both. However, creating visual contents manually are both challenging and labor intensive. Therefore, enabling computers to automatically or semi-automatically synthesize needed visual contents becomes essential. Among all these efforts, a stream of research is to generate novel images based on given image priors, e.g., photos and sketches. This research direction is known as image-conditional image generation, which covers a wide range of topics such as image stylization, image completion, image fusion, sketch-to-image generation, and extracting image label maps. In this thesis, a set of novel approaches for image-conditional image generation are presented. The thesis starts with an exemplar-based method for facial image stylization in Chapter 2. This method involves a unified framework for facial image stylization based on a single style exemplar. A two-phase procedure is employed, where the first phase searches a dense and semantic-aware correspondence between the input and the exemplar images, and the second phase conducts edge-preserving texture transfer. While this algorithm has the merit of requiring only a single exemplar, it is constrained to face photos. To perform generalized image-to-image translation, Chapter 3 presents a data-driven and learning-based method. Inspired by the dual learning paradigm designed for natural language translation [115], a novel dual Generative Adversarial Network (DualGAN) mechanism is developed, which enables image translators to be trained from two sets of unlabeled images from two domains. This is followed by another data-driven method in Chapter 4, which learns multiscale manifolds from a set of images and then enables synthesizing novel images that mimic the appearance of the target image dataset. The method is named as Branched Generative Adversarial Network (BranchGAN) and employs a novel training method that enables unconditioned generative adversarial networks (GANs) to learn image manifolds at multiple scales. As a result, we can directly manipulate and even combine latent manifold codes that are associated with specific feature scales. Finally, to provide users more control over image generation results, Chapter 5 discusses an upgraded version of iGAN [126] (iGANHD) that significantly improves the art of manipulating high-resolution images through utilizing the multi-scale manifold learned with BranchGAN

BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis

We introduce BSD-GAN, a novel multi-branch and scale-disentangled training method which enables unconditional Generative Adversarial Networks (GANs) to learn image representations at multiple scales, benefiting a wide range of generation and editing tasks. The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network of BSD-GAN, is deliberately split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. During training, we progressively "de-freeze" the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes which model different feature scales.Extensive experiments demonstrate the effectiveness of our training method in scale-disentangled learning of image representations and synthesis of novel image contents, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate several image generation and manipulation applications enabled or improved by BSD-GAN. Source codes are available at https://github.com/duxingren14/BSD-GAN.Comment: 12 pages, 20 figures, accepted to IEEE Transaction on Image Processin

Comparison of lignocellulose composition in four major species of Miscanthus

Miscanthus is a perennial grass rich in lignocellulose that has attracted interest as a non-food crop for renewable bioenergy with major environmental and economic benefits for China. The lignocellulose composition of whole stems of four major species of Miscanthus was assessed. The average values of total moisture content (TMC) (61.90%) and hemicelluloses (34.86%) were the highest while cellulose (32.71%) and acid detergent lignin (ADL) (8.90%) were the lowest in Miscanthus floridulus. On the contrary, the contents of cellulose (42.11%) and ADL (13.64%) were the highest and total ash (TA) (2.89%) was the lowest in Miscanthus lutarioriparius. The Shannon–Weaver diversity indices of components for the four species showed that hemicellulose content (H’= 2.00±0.11) was the most variable trait followed by cellulose (H’= 1.84±0.07), then ADL (H’= 1.84±0.07). The variational range of each component was relatively higher in Miscanthus sacchariflorus. In M. lutarioriparius, the diversity indices of each component were moderate. The diversity of cellulose was the highest and hemicellulose, ADL, TA and TMC were low in Miscanthus sinensis. By correlation analysis, neutral detergent fiber (NDF) significantly and positively correlated with ADF, cellulose and ADL at P<0.01 as well as the relationship of cellulose and ADL in the four species. Hemicellulose showed significant (P<0.01) but negative correlation with cellulose and ADL in M. floridulus, M. lutarioriparius and M. sacchariflorus. By principal component analysis (PCA), the components ADF and cellulose were the PC1 that were considered the foremost for the evaluation and selection of resource in the four species. The conclusions show that lignocellulose composition contents of Miscanthus culms were different. M. floridulus was more fit to ethanol fermentation. Though the components contents in M. sinensis and M. sacchariflorus were moderate, the range of choice was large. It provided a possible means to screen the appropriate materials according to different utilization. M. lutarioriparius had more superiorities relatively. So the four species of Miscanthus were appropriate for extension as excellent herbaceous energy plants, though, reasonable species choice should be employed according to the conversion approach and the growth characteristics, productivity levels and biomass quality characteristics of these tall grasses.Keywords: Miscanthus, bioenergy, lignocellulose compositions, detergent fiber, diversity analysis, PC

Targeted suppression of heme oxygenase-1 by small interference RNAs inhibits the production of bilirubin in neonatal rat with hyperbilirubinemia

<p>Abstract</p> <p>Background</p> <p>Excessive accumulation of bilirubin contributes to neonatal hyperbilirubinemia in rats. Heme oxygenase (HO) is one of the rate-limiting enzymes in catabolizing heme to bilirubin. In the present study, we investigated whether suppression of rat HO-1 (rHO-1) expression by small interference RNAs (siRNAs) reduces bilirubin levels in hyperbilirubinemic rats.</p> <p>Results</p> <p>Four pairs of siRNA targeting rHO-1 mRNA were introduced into BRL cells and compared for their inhibitory effect on the expression of <it>rHO-1 </it>gene and production of rHO-1 protein. The siRNA exhibiting the most potent effect on HO-1 expression and activity was then administered intraperitoneally to 7 to 9-day-old rats with hyperbilirubinemia. The siRNA distributed mostly in the liver and spleen of neonatal rat. Serum bilirubin levels and hepatic HO-1 expression were further evaluated. Systemic treatment of siRNA targeting rHO-1 reduced hepatic HO-1 expression and decreased the serum bilirubin levels in a time- and dose-dependent manner, and siRNA decreased the indirect bilirubin levels more effectively than Sn-protoporphyrin (SnPP), an HO-1 inhibitor.</p> <p>Conclusion</p> <p>siRNA targeting rHO-l attenuates hepatic HO-1 expression and serum bilirubin levels. Thus this study provides a novel therapeutic rationale for the prevention and treatment of neonatal hyperbilirubinemia.</p

Enhancement of Innate and Adaptive Immune Functions by Multiple Echinacea Species

Echinacea preparations are commonly used as nonspecific immunomodulatory agents. Alcohol extracts from three widely used Echinacea species, Echinacea angustifolia, Echinacea pallida, and Echinacea purpurea, were investigated for immunomodulating properties. The three Echinacea species demonstrated a broad difference in concentrations of individual lipophilic amides and hydrophilic caffeic acid derivatives. Mice were gavaged once a day (for 7 days) with one of the Echinacea extracts (130 mg/kg) or vehicle and immunized with sheep red blood cells (sRBC) 4 days prior to collection of immune cells for multiple immunological assays. The three herb extracts induced similar, but differential, changes in the percentage of immune cell populations and their biological functions, including increased percentages of CD49+ and CD19+ lymphocytes in spleen and natural killer cell cytotoxicity. Antibody response to sRBC was significantly increased equally by extracts of all three Echinacea species. Concanavalin A-stimulated splenocytes from E. angustifolia- and E. pallida-treated mice demonstrated significantly higher T cell proliferation. In addition, the Echinacea treatment significantly altered the cytokine production by mitogenstimulated splenic cells. The three herbal extracts significantly increased interferon-γ production, but inhibited the release of tumor necrosis factor-α and interleukin (IL)-1β. Only E. angustifolia- and E. pallida-treated mice demonstrated significantly higher production of IL-4 and increased IL-10 production. Taken together, these findings demonstrated that Echinacea is a wide-spectrum immunomodulator that modulates both innate and adaptive immune responses. In particular, E. angustifolia or E. pallida may have more anti-inflammatory potential