Learning Diverse Tone Styles for Image Retouching

Image retouching, aiming to regenerate the visually pleasing renditions of given images, is a subjective task where the users are with different aesthetic sensations. Most existing methods deploy a deterministic model to learn the retouching style from a specific expert, making it less flexible to meet diverse subjective preferences. Besides, the intrinsic diversity of an expert due to the targeted processing on different images is also deficiently described. To circumvent such issues, we propose to learn diverse image retouching with normalizing flow-based architectures. Unlike current flow-based methods which directly generate the output image, we argue that learning in a style domain could (i) disentangle the retouching styles from the image content, (ii) lead to a stable style presentation form, and (iii) avoid the spatial disharmony effects. For obtaining meaningful image tone style representations, a joint-training pipeline is delicately designed, which is composed of a style encoder, a conditional RetouchNet, and the image tone style normalizing flow (TSFlow) module. In particular, the style encoder predicts the target style representation of an input image, which serves as the conditional information in the RetouchNet for retouching, while the TSFlow maps the style representation vector into a Gaussian distribution in the forward pass. After training, the TSFlow can generate diverse image tone style vectors by sampling from the Gaussian distribution. Extensive experiments on MIT-Adobe FiveK and PPR10K datasets show that our proposed method performs favorably against state-of-the-art methods and is effective in generating diverse results to satisfy different human aesthetic preferences. Source code and pre-trained models are publicly available at https://github.com/SSRHeart/TSFlow

Melatonin protects against ovarian damage by inhibiting autophagy in granulosa cells in rats

Objectives: This study sought to further verify the protective mechanism of Melatonin (MT) against ovarian damage through animal model experiments and to lay a theoretical and experimental foundation for exploring new approaches for ovarian damage treatment. Method: The wet weight and ovarian index of rat ovaries were weighted, and the morphology of ovarian tissues and the number of follicles in the pathological sections of collected ovarian tissues were recorded. And the serum sex hormone levels, the key proteins of the autophagy pathway (PI3K, AKT, mTOR, LC3II, LC3I, and Agt5) in rat ovarian tissues, as well as the viability and mortality of ovarian granulosa cells in each group were measured by ELISA, western blotting, CCK8 kit and LDH kit, respectively. Results: The results showed that MT increased ovarian weight and improved the ovarian index in ovarian damage rats. Also, MT could improve autophagy-induced ovarian tissue injury, increase the number of primordial follicles, primary follicles, and sinus follicles, and decrease the number of atretic follicles. Furthermore, MT upregulated serum AMH, INH-B, and E2 levels downregulated serum FSH and LH levels in ovarian damage rats and activated the PI3K/AKT/mTOR signaling pathway. Besides, MT inhibited autophagic apoptosis of ovarian granulosa cells and repressed the expression of key proteins in the autophagic pathway and reduced the expression levels of Agt5 and LC3II/I. Conclusions: MT inhibits granulosa cell autophagy by activating the PI3K/Akt/mTOR signaling pathway, thereby exerting a protective effect against ovarian damage

Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution

Video super-resolution (VSR) aiming to reconstruct a high-resolution (HR) video from its low-resolution (LR) counterpart has made tremendous progress in recent years. However, it remains challenging to deploy existing VSR methods to real-world data with complex degradations. On the one hand, there are few well-aligned real-world VSR datasets, especially with large super-resolution scale factors, which limits the development of real-world VSR tasks. On the other hand, alignment algorithms in existing VSR methods perform poorly for real-world videos, leading to unsatisfactory results. As an attempt to address the aforementioned issues, we build a real-world 4 VSR dataset, namely MVSR4$\times$, where low- and high-resolution videos are captured with different focal length lenses of a smartphone, respectively. Moreover, we propose an effective alignment method for real-world VSR, namely EAVSR. EAVSR takes the proposed multi-layer adaptive spatial transform network (MultiAdaSTN) to refine the offsets provided by the pre-trained optical flow estimation network. Experimental results on RealVSR and MVSR4$\times$ datasets show the effectiveness and practicality of our method, and we achieve state-of-the-art performance in real-world VSR task. The dataset and code will be publicly available

Synthesis and fungicidal activity of pyrazole derivatives containing 1,2,3,4-tetrahydroquinoline

Additional file 3. Structural information (CIF) for Compound 10g

Establishment of an Inducible HBV Stable Cell Line that Expresses cccDNA-dependent Epitope-tagged HBeAg for Screening of cccDNA Modulators

Hepatitis B virus (HBV) covalently closed circular (ccc) DNA is essential to the virus life cycle, its elimination during chronic infection is considered critical to a durable therapy but has not been achieved by current antivirals. Despite being essential, cccDNA has not been the major target of high throughput screening (HTS), largely because of the limitations of current HBV tissue culture systems, including the impracticality of detecting cccDNA itself. In response to this need, we have previously developed a proof-of-concept HepDE19 cell line in which the production of wildtype e antigen (HBeAg) is dependent upon cccDNA. However, the existing assay system is not ideal for HTS because the HBeAg ELISA cross reacts with a viral HBeAg homologue, which is the core antigen (HBcAg) expressed largely in a cccDNA-independent fashion in HepDE19 cells. To further improve the assay specificity, we report herein a “second-generation” cccDNA reporter cell line, termed HepBHAe82. In the similar principle of HepDE19 line, an in-frame HA epitope tag was introduced into the precore domain of HBeAg open reading frame in the transgene of HepBHAe82 cells without disrupting any cis-element critical for HBV replication and HBeAg secretion. A chemiluminescence ELISA assay (CLIA) for the detection of HA-tagged HBeAg with HA antibody serving as capture antibody and HBeAb serving as detection antibody has been developed to eliminate the confounding signal from HBcAg. The miniaturized HepBHAe82 cell based assay system exhibits high level of cccDNA-dependent HA-HBeAg production and high specific readout signals with low background. We have also established a HepHA-HBe4 cell line expressing transgene-dependent HA-HBeAg as a counter screen to identify HBeAg inhibitors. The HepBHAe82 system is amenable to antiviral HTS development, and can be used to identify host factors that regulate cccDNA metabolism and transcription