Coarse-to-Fine: Facial Structure Editing of Portrait Images via Latent Space Classifications

Facial structure editing of portrait images is challenging given the facial variety, the lack of ground-truth, the necessity of jointly adjusting color and shape, and the requirement of no visual artifacts. In this paper, we investigate how to perform chin editing as a case study of editing facial structures. We present a novel method that can automatically remove the double chin effect in portrait images. Our core idea is to train a fine classification boundary in the latent space of the portrait images. This can be used to edit the chin appearance by manipulating the latent code of the input portrait image while preserving the original portrait features. To achieve such a fine separation boundary, we employ a carefully designed training stage based on latent codes of paired synthetic images with and without a double chin. In the testing stage, our method can automatically handle portrait images with only a refinement to subtle misalignment before and after double chin editing. Our model enables alteration to the neck region of the input portrait image while keeping other regions unchanged, and guarantees the rationality of neck structure and the consistency of facial characteristics. To the best of our knowledge, this presents the first effort towards an effective application for editing double chins. We validate the efficacy and efficiency of our approach through extensive experiments and user studies

Coarse-to-Fine: Facial Structure Editing of Portrait Images via Latent Space Classifications

Facial structure editing of portrait images is challenging given the facial variety, the lack of ground-truth, the necessity of jointly adjusting color and shape, and the requirement of no visual artifacts. In this paper, we investigate how to perform chin editing as a case study of editing facial structures. We present a novel method that can automatically remove the double chin effect in portrait images. Our core idea is to train a fine classification boundary in the latent space of the portrait images. This can be used to edit the chin appearance by manipulating the latent code of the input portrait image while preserving the original portrait features. To achieve such a fine separation boundary, we employ a carefully designed training stage based on latent codes of paired synthetic images with and without a double chin. In the testing stage, our method can automatically handle portrait images with only a refinement to subtle misalignment before and after double chin editing. Our model enables alteration to the neck region of the input portrait image while keeping other regions unchanged, and guarantees the rationality of neck structure and the consistency of facial characteristics. To the best of our knowledge, this presents the first effort towards an effective application for editing double chins. We validate the efficacy and efficiency of our approach through extensive experiments and user studies

Beauty3DFaceNet:Deep geometry and texture fusion for 3D facial attractiveness prediction

We present Beauty3DFaceNet, the first deep convolutional neural network to predict attractiveness on 3D faces with both geometry and texture information. The proposed network can learn discriminative and complementary 2D and 3D facial features, allowing accurate attractiveness prediction for 3D faces. The main component of our network is a fusion module that fuses geometric features and texture features. We further employ a novel sampling strategy for our network based on a prior of facial landmarks, which improves the performance of learning aesthetic features from a face point cloud. Comparing to previous work, our approach takes full advantage of 3D geometry and 2D texture and does not rely on handcrafted features based on highly accurate facial characteristics such as feature points. To facilitate 3D facial attractiveness research, we also construct the first 3D face dataset ShadowFace3D, which contains 6,000 high-quality 3D faces with attractiveness labeled by human annotators. Extensive quantitative and qualitative evaluations show that Beauty3DFaceNet achieves a significant correlation with the average human ratings. This validates that a deep learning network can effectively learn and predict 3D facial attractiveness.</p

EyelashNet: A Dataset and A Baseline Method for Eyelash Matting

Eyelashes play a crucial part in the human facial structure and largely affect the facial attractiveness in modern cosmetic design. However, the appearance and structure of eyelashes can easily induce severe artifacts in high-fidelity multi-view 3D face reconstruction. Unfortunately it is highly challenging to remove eyelashes from portrait images using both traditional and learning-based matting methods due to the delicate nature of eyelashes and the lack of eyelash matting dataset. To this end, we present EyelashNet, the first eyelash matting dataset which contains 5,400 high-quality eyelash matting data captured from real world and 5,272 virtual eyelash matting data created by rendering avatars. Our work consists of a capture stage and an inference stage to automatically capture and annotate eyelashes instead of tedious manual efforts. The capture is based on a specifically-designed fluorescent labeling system. By coloring the eyelashes with a safe and invisible fluorescent substance, our system takes paired photos with colored and normal eyelashes by turning the equipped ultraviolet (UVA) flash on and off. We further correct the alignment between each pair of photos and use a novel alpha matte inference network to extract the eyelash alpha matte. As there is no prior eyelash dataset, we propose a progressive training strategy that progressively fuses captured eyelash data with virtual eyelash data to learn the latent semantics of real eyelashes. As a result, our method can accurately extract eyelash alpha mattes from fuzzy and self-shadow regions such as pupils, which is almost impossible by manual annotations. To validate the advantage of EyelashNet, we present a baseline method based on deep learning that achieves state-of-the-art eyelash matting performance with RGB portrait images as input. We also demonstrate that our work can largely benefit important real applications including high-fidelity personalized avatar and cosmetic design

DOI:10.2298/ABS1003669X DETERMINATION OF TEA POLYSACCHARIDES IN CAMELLIA SINENSIS BY A MODIFIED PHENOL-SULFURIC ACID METHOD

Abstract- A direct procedure for the determination of total polysaccharides (TPS) in Camellia sinensis was set up based on the modified phenol-sulfuric acid method. The monosaccharide composition of TPS was analyzed by GC. Based on the results of GC, model monosaccharide mixtures were made which provided an adequate standard for this procedure. Through single-factor and orthogonal (L93 4) experiments, the experimental conditions such as the volume of phenol, the volume of concentrated sulfuric acid, the reaction time, and the incubation temperature, were optimized. The highest sensitivity of absorbance was obtained when the volume of concentrated sulfuric acid, the volume of phenol (6%), and the incubation temperature were 2.5 ml, 0.2 ml, and 50°C, respectively. Under optimum conditions, the prepared samples were determined satisfactorily, with the recovery from 100.2 % to 103.7%, and a relative standard deviation (RSD) of 2.1%. Overall, the modified method is easily operated, rapid, sensitive and accurate. A similar procedure can be applied to the determination of other plant polysaccharides as well

Programmable Polyproteams of Tyrosine Ammonia Lyases as Cross-Linked Enzymes for Synthesizing p-Coumaric Acid

Ideal immobilization with enhanced biocatalyst activity and thermostability enables natural enzymes to serve as a powerful tool to yield synthetically useful chemicals in industry. Such an enzymatic method strategy becomes easier and more convenient with the use of genetic and protein engineering. Here, we developed a covalent programmable polyproteam of tyrosine ammonia lyases (TAL-CLEs) by fusing SpyTag and SpyCatcher peptides into the N-terminal and C-terminal of the TAL, respectively. The resulting circular enzymes were clear after the spontaneous isopeptide bonds formed between the SpyTag and SpyCatcher. Furthermore, the catalytic performance of the TAL-CLEs was measured via a synthesis sample of p-Coumaric acid. Our TAL-CLEs showed excellent catalytic efficiency, with 98.31 &plusmn; 1.14% yield of the target product&mdash;which is 4.15 &plusmn; 0.08 times higher than that of traditional glutaraldehyde-mediated enzyme aggregates. They also showed over four times as much enzyme-activity as wild-type TAL does and demonstrated good reusability, and so may become a good candidate for industrial enzymes

Putting precision and elegance in enzyme immobilisation with bio-orthogonal chemistry

The covalent immobilisation of enzymes generally involves the use of highly reactive crosslinkers, such as glutaraldehyde, to couple enzyme molecules to each other or to carriers through, for example, the free amino groups of lysine residues, on the enzyme surface. Unfortunately, such methods suffer from a lack of precision. Random formation of covalent linkages with reactive functional groups in the enzyme leads to disruption of the three dimensional structure and accompanying activity losses. This review focuses on recent advances in the use of bio-orthogonal chemistry in conjunction with rec-DNA to affect highly precise immobilisation of enzymes. In this way, cost-effective combination of production, purification and immobilisation of an enzyme is achieved, in a single unit operation with a high degree of precision. Various bio-orthogonal techniques for putting this precision and elegance into enzyme immobilisation are elaborated. These include, for example, fusing (grafting) peptide or protein tags to the target enzyme that enable its immobilisation in cell lysate or incorporating non-standard amino acids that enable the application of bio-orthogonal chemistry.BT/Biocatalysi

Single-cell landscape of long and short glandular trichomes in Nicotiana tabacum leaves

Summary: Glandular trichomes (GTs) play a crucial role in plant defenses and the synthesis of secondary metabolites. Understanding the developmental trajectory of GTs is essential for unraveling their functional significance and potential applications. Here we established a comprehensive single-cell atlas of Nicotiana tabacum leaves, a model plant for GT studies. The atlas included a total of 40,433 cells and successfully captured both long GTs (LGTs) and short GTs (SGTs) from Nicotiana leaves. The developmental trajectories of these trichomes were delineated, revealing potential disparities in epidermal development. Comparative analysis of Arabidopsis and Nicotiana trichome development indicated limited similarity between Arabidopsis epidermal non-glandular trichomes and Nicotiana LGTs and SGTs, implying the essentiality of studying the genes directly involved in the development of Nicotiana GTs for a proper and comprehensive understanding of GT biology. Overall, our results provide profound insights into the developmental intricacies of the specialized GTs

Clinical and genetic analysis of lipoprotein glomerulopathy patients caused by APOE

Abstract Background Lipoprotein glomerulopathy (LPG) is a rare kidney disease caused by APOE mutations. The aim of this study was to correlate the genetic and clinical features of LPG. Methods Totally eight LPG patients were recruited in this study and Sanger sequencing of APOE was performed for all available family members. Clinical and histological features were analyzed. A literature review of LPG was also conducted. Results Genetic analysis revealed five patients with APOE‐Kyoto, two with APOE‐Osaka/Kurashiki, and one with APOE‐Chicago mutations. LPG patients with urine protein reduced more than 50% had a slower decrease in renal function than those with less urine protein reduction (estimated glomerular filtration rate reduction rate −5.0 ± 0.8 vs. 1.5 ± 0.7 ml/min per 1.73 m2⋅month−1, p = .03). We then enrolled 95 LPG patients from previous studies and this study. LPG patients had higher blood pressure (mean arterial pressure: 109.4 ± 19.4 vs. 94.4 ± 11.1 mmHg, p < .001) than the control group. Interestingly, patients with APOE mutations in the LDL receptor binding region had higher serum apolipoprotein E (apoE) levels [ln(apoE): 2.7 ± 0.4 vs. 2.0 ± 0.5 mg/dl, p < .001] in comparison to other domains. Conclusion Here, we report for the first time APOE‐Osaka/Kurashiki and APOE‐Chicago mutations in the Chinese population. LPG was associated with higher blood pressure and serum apoE levels were higher in patients with mutations in LDL receptor binding region. In addition, the findings further indicated that treatment of proteinuria might slow down renal function progression in these patients