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.     

Robust Linear Interpolation and Extrapolation of Stationary Time Series in Lp

To deal with uncertainty of the spectral distribution, we consider minimax interpolation and extrapolation problems in L^p for stationary processes. The interpolation and extrapolation problems can be regarded as a linear approximation problem on the unit disk in the complex plane. Although the robust one-step-ahead predictor and interpolator has already been considered separately in the previous literature, we give two conditions for the uncertainty class to find the minimax interpolator and extrapolator in the general framework from both the point of view of the observation set and the point of view of evaluation on the interpolation and extrapolation error under the L^p-norm. We show that there exists a minimax interpolator and extrapolator for the class of spectral densities ε-contaminated by unknown spectral densities under our conditions. When the uncertainty class contains spectral distribution functions which are not absolutely continuous to the Lebesgue measure, we show that there exists an approximate interpolator and extrapolator in L^p such that its maximal interpolation and extrapolation error is arbitrarily close to the minimax error when the spectral distributions have densities. Our results are applicable to the stationary harmonizable stable processes.     

Iron Loss Calculation of AC Filter Inductor for Three‐Phase PWM Inverters

In recent years, remarkable advancement of new power semiconductor devices, such as SiC and GaN, enables the increase of switching frequency of power converters, and hence the volume of passive components, such as ac filters and transformers, can be reduced. However, temperature rise caused by the inductor loss is increasing, and hence iron loss evaluation of the inductor is one of the most important issues to realize high power density converters. Conventionally, an improved generalized Steinmetz equation (iGSE) has proposed in order to calculate the iron loss under a pulse voltage magnetizing condition. However, accurate iron loss calculation of the ac filter inductor used in a PWM inverter cannot be realized. The authors have proposed two methods of iron loss evaluation of ac filter inductors. The first one is a loss map method which can calculate the iron loss without using a real PWM inverter. Another one is an ILA (Inductor Loss Analyzer) which can measure the iron loss in every switching period in a real PWM inverter. In this paper, comparisons of the iron loss between the ILA and the loss map method on both the single‐phase and three‐phase inverters are studied. It is found that iron loss of the ac filter inductor in the three‐phase PWM inverter which is calculated by the loss map method cause a large error on a specific condition. In order to prevent the calculation error, the authors proposed a revised loss map method and proved the effectiveness of the method.     

Direct Growth of Germanene at Interfaces between Van der Waals Materials and Ag(111)

Germanene, a 2D honeycomb germanium crystal, is grown at graphene/Ag(111) and hexagonal boron nitride (h-BN)/Ag(111) interfaces by segregating germanium atoms. A simple annealing process in N₂ or H₂/Ar at ambient pressure leads to the formation of germanene, indicating that an ultrahigh-vacuum condition is not necessary. The grown germanene is stable in air and uniform over the entire area covered with a van der Waals (vdW) material. As an important finding, it is necessary to use a vdW material as a cap layer for the present germanene growth method since the use of an Al₂O₃ cap layer results in no germanene formation. The present study also proves that Raman spectroscopy in air is a powerful tool for characterizing germanene at the interfaces, which is concluded by multiple analyses including first-principles density functional theory calculations. The direct growth of h-BN-capped germanene on Ag(111), which is demonstrated in the present study, is considered to be a promising technique for the fabrication of future germanene-based electronic devices.     

Exploring the Minimal RNA Substrate of Flexizymes

Flexizymes are tRNA acylation ribozymes that have been successfully used to facilitate genetic code reprogramming. They are capable of charging acid substrates onto various tRNAs and tRNA analogues. However, their minimal RNA substrate has not been investigated. Here we have designed fluorescently labeled short RNAs corresponding to the four, three, and two bases (4bRNA, 3bRNA, 2bRNA) at the tRNA 3′-end and explored the minimal RNA substrate of flexizymes, dFx and eFx. 3bRNA was the observed minimal RNA substrate of the flexizymes, but the efficiency of acylation of this short RNA was two to three times lower than that of 4bRNA. The efficiency of acylation of 4bRNA was comparable with that of the microhelix, a 22-base RNA conventionally used as a tRNA analogue for analyzing acylation efficiency. We also compared the efficiencies of acylation of the microhelix and 4bRNA with various acid substrates. Thanks to the short length of 4bRNA, its acyl-4bRNA products exhibited larger mobility shifts in gel electrophoresis than those exhibited by acyl-microhelix products with every substrate tested. This indicated that 4bRNA was an ideal RNA substrate for analyzing the efficiency of acylation by flexizymes.     

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.     

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.     

A Method for Systematic Assessment of Intrinsically Disordered Protein Regions by NMR

Intrinsically disordered proteins (IDPs) that lack stable conformations and are highly flexible have attracted the attention of biologists. Therefore, the development of a systematic method to identify polypeptide regions that are unstructured in solution is important. We have designed an “indirect/reflected” detection system for evaluating the physicochemical properties of IDPs using nuclear magnetic resonance (NMR). This approach employs a “chimeric membrane protein”-based method using the thermostable membrane protein PH0471. This protein contains two domains, a transmembrane helical region and a C-terminal OB (oligonucleotide/oligosaccharide binding)-fold domain (named NfeDC domain), connected by a flexible linker. NMR signals of the OB-fold domain of detergent-solubilized PH0471 are observed because of the flexibility of the linker region. In this study, the linker region was substituted with target IDPs. Fifty-three candidates were selected using the prediction tool POODLE and 35 expression vectors were constructed. Subsequently, we obtained ¹⁵N-labeled chimeric PH0471 proteins with 25 IDPs as linkers. The NMR spectra allowed us to classify IDPs into three categories: flexible, moderately flexible, and inflexible. The inflexible IDPs contain membrane-associating or aggregation-prone sequences. This is the first attempt to use an indirect/reflected NMR method to evaluate IDPs and can verify the predictions derived from our computational tools.