DeepFaceEditing: deep face generation and editing with disentangled geometry and appearance control

Recent facial image synthesis methods have been mainly based on conditional generative models. Sketch-based conditions can effectively describe the geometry of faces, including the contours of facial components, hair structures, as well as salient edges (e.g., wrinkles) on face surfaces but lack effective control of appearance, which is influenced by color, material, lighting condition, etc. To have more control of generated results, one possible approach is to apply existing disentangling works to disentangle face images into geometry and appearance representations. However, existing disentangling methods are not optimized for human face editing, and cannot achieve fine control of facial details such as wrinkles. To address this issue, we propose DeepFaceEditing, a structured disentanglement framework specifically designed for face images to support face generation and editing with disentangled control of geometry and appearance. We adopt a local-to-global approach to incorporate the face domain knowledge: local component images are decomposed into geometry and appearance representations, which are fused consistently using a global fusion module to improve generation quality. We exploit sketches to assist in extracting a better geometry representation, which also supports intuitive geometry editing via sketching. The resulting method can either extract the geometry and appearance representations from face images, or directly extract the geometry representation from face sketches. Such representations allow users to easily edit and synthesize face images, with decoupled control of their geometry and appearance. Both qualitative and quantitative evaluations show the superior detail and appearance control abilities of our method compared to state-of-the-art methods

Natural Resources for Human Health: A New Interdisciplinary Journal Dedicated to Natural Sciences

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Demethylation of Alkali Lignin with Halogen Acids and Its Application to Phenolic Resins

Lignin, a byproduct from the chemical processing of lignocellulosic biomass, is a polyphenolic compound that has potential as a partial phenol substitute in phenolic adhesive formulations. In this study, HBr and HI were used as reagents to demethylate an alkali lignin (AL) to increase its hydroxyl content and thereby enhance its reactivity for the preparation of phenolic resins. Analyses by FT-IR, 1H-NMR and 2D-NMR(HSQC) demonstrated both a decrease in methoxyl groups and an increase in hydroxyl groups for each demethylated lignin (DL). In addition, the molar amounts of phenolic hydroxyls, determined by 1H-NMR, increased to 0.67 mmol/g for the HI-DL, and 0.64 mmol/g for the HBr-DL, from 0.52 mmol/g for the AL. These results showed that HI, a stronger nucleophilic reagent than HBr, provided a higher degree of AL demethylation. Lignin-containing resins, prepared by copolymerization, met the bonding strength standard for exterior plywood with DL used to replace as much as 50 wt.% of phenol. The increased hydroxyl contents resulting from the lignin demethylations also imparted faster cure times for the lignin-containing resins and lower formaldehyde emissions. Altogether, the stronger nucleophilicity of HI, compared to HBr, impacted the degree of lignin demethylation, and carried through to measurable differences the thermal properties and performance of the lignin-containing PF resins

Development of a neuroprotective potential algorithm for medicinal plants

Medicinal plants are promising candidates for Alzheimer\u27s disease (AD) research but there is lack of systematic algorithms and procedures to guide their selection and evaluation. Herein, we developed a Neuroprotective Potential Algorithm (NPA) by evaluating twenty-three standardized and chemically characterized Ayurvedic medicinal plant extracts in a panel of bioassays targeting oxidative stress, carbonyl stress, protein glycation, amyloid beta (Aβ) fibrillation, acetylcholinesterase (AChE) inhibition, and neuroinflammation. The twenty-three herbal extracts were initially evaluated for: 1) total polyphenol content (Folin-Ciocalteu assay), 2) free radical scavenging capacity (DPPH assay), 3) ferric reducing antioxidant power (FRAP assay), 4) reactive carbonyl species scavenging capacity (methylglyoxal trapping assay), 5) anti-glycative effects (BSA-fructose, and BSA-methylglyoxal assays) and, 6) anti-Aβ fibrillation effects (thioflavin-T assay). Based on assigned index scores from the initial screening, twelve extracts with a cumulative NPA score ≥40 were selected for further evaluation for their: 1) inhibitory effects on AChE activity, 2) in vitro anti-inflammatory effects on murine BV-2 microglial cells (Griess assay measuring levels of lipopolysaccharide-induced nitric oxide species), and 3) in vivo neuroprotective effects on Caenorhabditis elegans post induction of Aβ1-42 induced neurotoxicity and paralysis. Among these, four extracts had a cumulative NPA score ≥60 including Phyllanthus emblica (amla; Indian gooseberry), Mucuna pruriens (velvet bean), Punica granatum (pomegranate) and Curcuma longa (turmeric; curcumin). These extracts also showed protective effects on H2O2 induced cytotoxicity in differentiated cholinergic human neuronal SH-SY5Y and murine BV-2 microglial cells and reduced tau protein levels in the SH-SY5Y neuronal cells. While published animal data support the neuroprotective effects of several of these Ayurvedic medicinal plant extracts, some remain unexplored for their anti-AD potential. Therefore, the NPA may be utilized, in part, as a strategy to help guide the selection of promising medicinal plant candidates for future AD-based research using animal models