DNA Methylation Influences Chlorogenic Acid Biosynthesis in Lonicera japonica by Mediating LjbZIP8 to Regulate Phenylalanine Ammonia-Lyase 2 Expression

The content of active compounds differ in buds and flowers of Lonicera japonica (FLJ) and L. japonica var. chinensis (rFLJ). Chlorogenic acid (CGAs) were major active compounds of L. japonica and regarded as measurements for quality evaluation. However, little is known concerning the formation of active compounds at the molecular level. We quantified the major CGAs in FLJ and rFLJ, and found the concentrations of CGAs were higher in the buds of rFLJ than those of FLJ. Further analysis of CpG methylation of CGAs biosynthesis genes showed differences between FLJ and rFLJ in the 5′-UTR of phenylalanine ammonia-lyase 2 (PAL2). We identified 11 LjbZIP proteins and 24 rLjbZIP proteins with conserved basic leucine zipper domains, subcellular localization, and electrophoretic mobility shift assay showed that the transcription factor LjbZIP8 is a nuclear-localized protein that specifically binds to the G-box element of the LjPAL2 5′-UTR. Additionally, a transactivation assay and LjbZIP8 overexpression in transgenic tobacco indicated that LjbZIP8 could function as a repressor of transcription. Finally, treatment with 5-azacytidine decreased the transcription level of LjPAL2 and CGAs content in FLJ leaves. These results raise the possibility that DNA methylation might influence the recruitment of LjbZIP8, regulating PAL2 expression level and CGAs content in L. japonica

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

Abstract Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics. We propose a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. This model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Furthermore, we also develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. We experimented with both synthetic data and data gathered from Twitter, and show that our model provides a good fit to the data as well as more accurate predictions than alternatives

Storage of multiple single-photon pulses emitted from a quantum dot in a solid-state quantum memory

Quantum repeaters are critical components for distributing entanglement over long distances in presence of unavoidable optical losses during transmission. Stimulated by Duan-Lukin-Cirac-Zoller protocol, many improved quantum-repeater protocols based on quantum memories have been proposed, which commonly focus on the entanglement-distribution rate. Among these protocols, the elimination of multi-photons (multi-photon-pairs) and the use of multimode quantum memory are demonstrated to have the ability to greatly improve the entanglement-distribution rate. Here, we demonstrate the storage of deterministic single photons emitted from a quantum dot in a polarization-maintaining solid-state quantum memory; in addition, multi-temporal-mode memory with $1$, $20$ and $100$ narrow single-photon pulses is also demonstrated. Multi-photons are eliminated, and only one photon at most is contained in each pulse. Moreover, the solid-state properties of both sub-systems make this configuration more stable and easier to be scalable. Our work will be helpful in the construction of efficient quantum repeaters based on all-solid-state devicesComment: Published version, including supplementary materia

A Small Amount of Dietary Carbohydrate Can Promote the HFD-Induced Insulin Resistance to a Maximal Level

Both dietary fat and carbohydrates (Carbs) may play important roles in the development of insulin resistance. The main goal of this study was to further define the roles for fat and dietary carbs in insulin resistance. C57BL/6 mice were fed normal chow diet (CD) or HFD containing 0.1–25.5% carbs for 5 weeks, followed by evaluations of calorie consumption, body weight and fat gains, insulin sensitivity, intratissue insulin signaling, ectopic fat, and oxidative stress in liver and skeletal muscle. The role of hepatic gluconeogenesis in the HFD-induced insulin resistance was determined in mice. The role of fat in insulin resistance was also examined in cultured cells. HFD with little carbs (0.1%) induced severe insulin resistance. Addition of 5% carbs to HFD dramatically elevated insulin resistance and 10% carbs in HFD was sufficient to induce a maximal level of insulin resistance. HFD with little carbs induced ectopic fat accumulation and oxidative stress in liver and skeletal muscle and addition of carbs to HFD dramatically enhanced ectopic fat and oxidative stress. HFD increased hepatic expression of key gluconeogenic genes and the increase was most dramatic by HFD with little carbs, and inhibition of hepatic gluconeogenesis prevented the HFD-induced insulin resistance. In cultured cells, development of insulin resistance induced by a pathological level of insulin was prevented in the absence of fat. Together, fat is essential for development of insulin resistance and dietary carb is not necessary for HFD-induced insulin resistance due to the presence of hepatic gluconeogenesis but a very small amount of it can promote HFD-induced insulin resistance to a maximal level

Legislative History: An Act To Allow Municipalities To Set Speed Limits on Certain Roads (HP937)(LD 1329)

https://digitalmaine.com/legishist123/2328/thumbnail.jp