Xanthohumol for Human Malignancies: Chemistry,Pharmacokinetics and Molecular Targets

Xanthohumol (XH) is an important prenylated flavonoid that is found within the inflorescence of Humulus lupulus L. (Hop plant). XH is an important ingredient in beer and is considered a significant bioactive agent due to its diverse medicinal applications, which include anti-inflammatory, antimicrobial, antioxidant, immunomodulatory, antiviral, antifungal, antigenotoxic, antiangiogenic, and antimalarial effects as well as strong anticancer activity towards various types of cancer cells. XH acts as a wide ranging chemopreventive and anticancer agent, and its isomer, 8-prenylnaringenin, is a phytoestrogen with strong estrogenic activity. The present review focuses on the bioactivity of XH on various types of cancers and its pharmacokinetics. In this paper, we first highlight, in brief, the history and use of hops and then the chemistry and structure–activity relationship of XH. Lastly, we focus on its prominent effects and mechanisms of action on various cancers and its possible use in cancer prevention and treatment. Considering the limited number of available reviews on this subject, our goal is to provide a complete and detailed understanding of the anticancer effects of XH against different cancers.     

Preparation of a Near-Infrared Ray Absorption Film from N-Phenylthiocarbamoyl Chitosan Derivative

We recently observed that the decanoylation of N-phenylthiocarbamoyl chitosan (2) with a mixture of decanoic anhydride and pyridine at 60 °C for 24 h afforded N,N-(decanoyl)phenythiocarbamoyl-/2-isothiocynato chitosan decanoate (3b) rather than the expected product N,N-(decanoyl)phenylthiocarbamoyl chitosan decanoate (3a). This result suggested that some of the N,N-(decanoyl)phenylthiocarmbamoyl groups had been converted to isothiocyanate groups during the decanoylation process. The subsequent reaction of compound 3b with aniline gave N,N-(decanoyl)phenylthiocarbamoyl/N-phenylthiocarbamoyl chitosan decanoate (4) in high yield. A solution of compound 4 in CHCl₃ was then added to a solution of copper decanoate (5) in the same solvent, and the resulting mixture was cast onto a glass plate to give a cast film. The film was annealed at 200 °C in an oven to give a greenish film, which showed good near-infrared absorption characteristic in the range of 800–2200 nm.     

Models for circular data from time series spectra

Circular data are those for which the natural support is the unit circle and its toroidal extensions. Numerous constructions have been proposed which can be used to generate models for such data. We propose a new, very general, one based on the normalization of the spectra of complex-valued stationary processes. As illustrations of the new construction's application, we study models for univariate circular data obtained from the spectra of autoregressive moving average models and relate them to existing models in the literature. We also propose and investigate multivariate circular models obtained from the high-order spectra of stationary stochastic processes generated using linear filtering with an autoregressive moving average response function. A new family of distributions for a Markov process on the circle is also introduced. Results for asymptotically optimal inference for dependent observations on the circle are presented which provide a new paradigm for inference with circular models. The application of one of the new families of spectra-generated models is illustrated in an analysis of wind direction data.     

Detection of Multiple Blockages in Parallelized Microreactors

In a plant consisting of parallelized microreactors (MRs), the product quality is lowered because of a lack of flow uniformity among them when blockage occurs. It is not practical to install sensors in every MR from the viewpoint of cost when detecting the blocked MRs. In the previous study, the multiple blockage detection (MBD) method using a small number of sensors was proposed, but its performance became low when the number of sensors decreased. Here, the conventional algorithm for MBD is improved by considering the process behavior on blockage occurrence, and the effectiveness of the improved algorithm is demonstrated through a numerical case study. The effects of flow distributor types and sensor types on the MBD performance are numerically investigated.     

Analysis and optimization of high-power pulse transformer for SXFEL

Longitudinal injection is a promising on-axis injection scheme for diffraction-limited storage rings. In the latest version of the Hefei advanced light source(HALS), both the dynamic aperture and momentum aperture have been optimized. A longitudinal injection scheme was investigated on the HALS using a doublefrequency radio frequency system. To evaluate the injection performance, various errors were considered. A series of tracking simulations were conducted, and the injection efficiency was obtained under different error levels.     

Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model

The integration of reinforcement learning (RL) and imitation learning (IL) is an important problem that has long been studied in the field of intelligent robotics. RL optimizes policies to maximize the cumulative reward, whereas IL attempts to extract general knowledge about the trajectories demonstrated by experts, i.e, demonstrators. Because each has its own drawbacks, many methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of these methods are heuristic and do not have a solid theoretical basis. This paper presents a new theory for integrating RL and IL by extending the probabilistic graphical model (PGM) framework for RL, control as inference. We develop a new PGM for RL with multiple types of rewards, called probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of RL and IL can be formulated as a probabilistic inference of policies on pMDP-MO by considering the discriminator in generative adversarial imitation learning (GAIL) as an additional optimality emission. We adapt the GAIL and task-achievement reward to our proposed framework, achieving significantly better performance than policies trained with baseline methods.     

Dynamic Stabilization of DNA Assembly by Using Pyrrole-Imidazole Polyamide

We used N-methylpyrrole (Py)-N-methylimidazole-(Im) polyamide as an exogenous agent to modulate the formation of DNA assemblies at specific double-stranded sequences. The concept was demonstrated on the hybridization chain reaction that forms linear DNA. Through a series of melting curve analyses, we demonstrated that the binding of Py−Im polyamide positively influenced both the HCR initiation and elongation steps. In particular, Py−Im polyamide was found to drastically stabilize the DNA duplex such that its thermal stability approached that of an equivalent hairpin structure. Also, the polyamide served as an anchor between hairpin pairs in the HCR assembly, thus improving the originally weak interstrand stability. We hope that these proof-of-concept results can inspire future use of Py−Im polyamide as a molecular tool to modulate the formation of DNA assemblies.