Stabilizing a gaseous optical laser

Frequency of gaseous optical laser can be stabilized by sinusoidally modulating the geometry of the cavity. Fabry-Perot dielectric mirrors are mounted in two Invar blocks that are connected by four magnetorestrictive bars. Each bar has three coils to sinusoidally modulate system. Ac establishes frequency, and dc the average value; both are supplied to coil from control system

Method and apparatus for stabilizing a gaseous optical maser Patent

Gas laser frequency stabilized by position of mirrors in resonant cavit

Synthesis of Xylooligosaccharides of Daidzein and Their Anti-Oxidant and Anti-Allergic Activities

The biocatalytic synthesis of xylooligosaccharides of daidzein was investigated using cultured cells of Catharanthus roseus and Aspergillus sp. β-xylosidase. The cultured cells of C. roseus converted daidzein into its 4′-O-β-glucoside, 7-O-β-glucoside, and 7-O-β-primeveroside, which was a new compound. The 7-O-β-primeveroside of daidzein was further xylosylated by Aspergillus sp. β-xylosidase to daidzein trisaccharide, i.e., 7-O-[6-O-(4-O-(β-d-xylopyranosyl))-β-d-xylopyranosyl]-β-d-glucopyranoside, which was a new compound. The 4′-O-β-glucoside, 7-O-β-glucoside, and 7-O-β-primeveroside of daidzein exerted DPPH free-radical scavenging and superoxide radical scavenging activity. On the other hand, 7-O-β-glucoside and 7-O-β-primeveroside of daidzein showed inhibitory effects on IgE antibody production

High expression of FOXC2 is associated with aggressive phenotypes and poor prognosis in clinical hepatocellular carcinoma

学位記番号：医博甲171

Food Ingredients Recognition through Multi-label Learning

Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet. We tackle the problem of food ingredients recognition as a multi-label learning problem. We propose a method for adapting a highly performing state of the art CNN in order to act as a multi-label predictor for learning recipes in terms of their list of ingredients. We prove that our model is able to, given a picture, predict its list of ingredients, even if the recipe corresponding to the picture has never been seen by the model. We make public two new datasets suitable for this purpose. Furthermore, we prove that a model trained with a high variability of recipes and ingredients is able to generalize better on new data, and visualize how it specializes each of its neurons to different ingredients.Comment: 8 page