Proteomic variation in Korean ginseng (Panax ginseng C.A. Meyer) isolates from different geographic regions

Korean ginseng is a traditional medicine that is widely used in Korea. In this study, a proteomic approach was used to investigate variations in Korean ginseng isolates that are associated with ecologic and geographic differences. Ginseng samples were collected from four geographically isolated locations in Korea: North gyeonggi, Gochang, Geumsan and Kanghwa. Two-dimensional gel electrophoresis (2-DE) and peptide fingerprinting of tryptic digests by mass spectrometry (MALDI-TOF) revealed primary ginseng root region-specific variations in protein profiles in these distinct areas. Thirty seven (37) major proteins that are common to the main root of ginseng at all four geographic sites and six proteins that are specific to the main root of a local ginseng (Kanghwa) were identified. Most of the major common proteins identified could be classified into the following functional categories: (i) stress response; (ii) transcription and translation; (iii) nucleotide metabolism; (iv) plant hormone response; (v) signal transduction; (vi) protein degradation; (vii) protein destination and storage; and (viii) unassigned. The results show that Korean ginseng species can be distinguished on the basis of classical proteomics.Keywords: Panax ginseng C.A. Meyer, 2-DE, peptide fingerprinting, classical proteomic

Mixed cultures of Kimchi lactic acid bacteria show increased cell density and lactate productivity

This study was carried out to determine the characteristics of cell growth, lactate production and amino acid secretion among four kimchi lactic acid bacteria (Leuconostoc mesenteroides JEI, Leuconostoc kimchi 132, Lactobacillus sakei 171, and Weissella koreensis 521) alone and in selected mixtures. In solo culture, L. sakei 171 was superior in cell growth, lactate production and the release of amino acids to the extracellular medium. In contrast, W. koreensis 521 showed the least cell growth, lactate production and amino acid release among the tested bacteria. W. koreensis 521 consumed essential amino acids for growth, whereas L. sakei 171 released several of the essential amino acids important for the growth of W. koreensis 521. When we mixed L. sakei 171 and W. koreensis 521 at optimal concentrations, the obtained cell growth and lactic acid production were higher than those seen with either strain alone, presumably reflecting mutual effects between the two strains. Mixed culture of two kimchi lactobacilli on batch fermentation increased the cell density and lactic acid production with low nutrients consumption. These results suggest that mixed culturing of kimchi lactobacilli may be more effective than single culturing of kimchi lactic acid bacteria for improving lactic acid production.Keywords: Kimchi lactic acid bacteria, amino acid utilization, nutrients consumption.African Journal of BiotechnologyVol. 12(25), pp. 4000-400

A low-cost Lactobacillus salivarius L29 growth medium containing molasses and corn steep liquor allows the attainment of high levels of cell mass and lactic acid production

The aim of the present work was to formulate a Lactobacillus salivarius L29 industrial fermentation medium. High cell numbers and good levels of lactic acid by a L. salivarius L29 were obtained after shake flask fermentation using molasses as the sole carbon source and corn steep liquor (CSL (industrial grade); an organic source of N) as the principal nitrogen source. The optimum concentrations of molasses and CSL facilitating good cell growth and high-level lactic acid production were found to be 6 and 6% (both v/v), respectively. The maximum cell yield was 2.02 × 109 CFU/mL, thus about 15% lower than that obtained when MRS broth was employed for 5-L fermenters culture. Lactic acid production upon growth in industrial broth was 105 g/L; the total sugar content of the medium was 118 g/L (sucrose: glucose: fructose 68:14:18; w/w/w). Upon growth in De Man, Rogosa and Sharpe (MRS) broth (the total sugar content of which was 127 g/L, all of which was glucose), the lactic acid yield was 120 g/L. The optimized industrial growth medium was significantly more economical than were conventional broths.Keywords: Lactobacillus salivarius L29, molasses, corn steep liquor, culture medium optimization, lactic acidAfrican Journal of Biotechnology Vol. 12(16), pp. 2013-201

DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models

The recent progress in diffusion-based text-to-image generation models has significantly expanded generative capabilities via conditioning the text descriptions. However, since relying solely on text prompts is still restrictive for fine-grained customization, we aim to extend the boundaries of conditional generation to incorporate diverse types of modalities, e.g., sketch, box, and style embedding, simultaneously. We thus design a multimodal text-to-image diffusion model, coined as DiffBlender, that achieves the aforementioned goal in a single model by training only a few small hypernetworks. DiffBlender facilitates a convenient scaling of input modalities, without altering the parameters of an existing large-scale generative model to retain its well-established knowledge. Furthermore, our study sets new standards for multimodal generation by conducting quantitative and qualitative comparisons with existing approaches. By diversifying the channels of conditioning modalities, DiffBlender faithfully reflects the provided information or, in its absence, creates imaginative generation.Comment: 18 pages, 16 figures, and 3 table

DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models

Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristics of an artwork (e.g. brushwork, colortone, or composition) with text prompts alone may encounter limitations due to the inherent constraints of verbal description. To this end, we introduce DreamStyler, a novel framework designed for artistic image synthesis, proficient in both text-to-image synthesis and style transfer. DreamStyler optimizes a multi-stage textual embedding with a context-aware text prompt, resulting in prominent image quality. In addition, with content and style guidance, DreamStyler exhibits flexibility to accommodate a range of style references. Experimental results demonstrate its superior performance across multiple scenarios, suggesting its promising potential in artistic product creation

Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations

Training deep generative models usually requires a large amount of data. To alleviate the data collection cost, the task of zero-shot GAN adaptation aims to reuse well-trained generators to synthesize images of an unseen target domain without any further training samples. Due to the data absence, the textual description of the target domain and the vision-language models, e.g., CLIP, are utilized to effectively guide the generator. However, with only a single representative text feature instead of real images, the synthesized images gradually lose diversity as the model is optimized, which is also known as mode collapse. To tackle the problem, we propose a novel method to find semantic variations of the target text in the CLIP space. Specifically, we explore diverse semantic variations based on the informative text feature of the target domain while regularizing the uncontrolled deviation of the semantic information. With the obtained variations, we design a novel directional moment loss that matches the first and second moments of image and text direction distributions. Moreover, we introduce elastic weight consolidation and a relation consistency loss to effectively preserve valuable content information from the source domain, e.g., appearances. Through extensive experiments, we demonstrate the efficacy of the proposed methods in ensuring sample diversity in various scenarios of zero-shot GAN adaptation. We also conduct ablation studies to validate the effect of each proposed component. Notably, our model achieves a new state-of-the-art on zero-shot GAN adaptation in terms of both diversity and quality.Comment: Accepted to ICCV 2023 (poster

Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback

Diffusion models have recently shown remarkable success in high-quality image generation. Sometimes, however, a pre-trained diffusion model exhibits partial misalignment in the sense that the model can generate good images, but it sometimes outputs undesirable images. If so, we simply need to prevent the generation of the bad images, and we call this task censoring. In this work, we present censored generation with a pre-trained diffusion model using a reward model trained on minimal human feedback. We show that censoring can be accomplished with extreme human feedback efficiency and that labels generated with a mere few minutes of human feedback are sufficient. Code available at: https://github.com/tetrzim/diffusion-human-feedback.Comment: Published in NeurIPS 202

AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks

To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image. However, because of the low semantic correspondence between arbitrary content and artworks, the attention module repeatedly abuses specific local patches from the style image, resulting in disharmonious and evident repetitive artifacts. To overcome this limitation and accomplish impeccable artistic style transfer, we focus on enhancing the attention mechanism and capturing the rhythm of patterns that organize the style. In this paper, we introduce a novel metric, namely pattern repeatability, that quantifies the repetition of patterns in the style image. Based on the pattern repeatability, we propose Aesthetic Pattern-Aware style transfer Networks (AesPA-Net) that discover the sweet spot of local and global style expressions. In addition, we propose a novel self-supervisory task to encourage the attention mechanism to learn precise and meaningful semantic correspondence. Lastly, we introduce the patch-wise style loss to transfer the elaborate rhythm of local patterns. Through qualitative and quantitative evaluations, we verify the reliability of the proposed pattern repeatability that aligns with human perception, and demonstrate the superiority of the proposed framework.Comment: Accepted by ICCV 2023. Code is available at this https://github.com/Kibeom-Hong/AesPA-Ne

Still Misinterpreting Lie Scales: Reply to Feldman’s Rejoinder

Despite convincing counterevidence, misinterpretation of so-called Impression Management, Social Desirability, or Lie scales in low-stakes settings seems to persist. In this reply to an ongoing discussion with Feldman and colleagues (De Vries et al., 2017; Feldman, in press; Feldman et al., 2017), we argue that high scores on Impression Management and Lie scales in low-stakes settings are more likely to reflect honesty than dishonesty. Specifically, we point out (1) that there is no evidence of a relation between Impression Management and (in-)authenticity, (2) that respondents in anonymous online studies have no reason to be inauthentic, and (3) that laypersons’ judgments about Lie scale responses (especially responses that are extremely rare) are uninformative and thus yield no insight on the construct validity of the Lie scale. We finally reiterate the warning that conclusions based on the incorrect interpretation of Impression Management, Social Desirability, or Lie scales in low-stakes settings are invalid