Dietary Isothiocyanates: Novel Insights into the Potential for Cancer Prevention and Therapy

Diet plays an important role in health. A high intake of plant chemicals such as glucosinolates/isothiocyanates can promote optimal health and decrease the risk of cancer. Recent research has discovered more novel mechanisms of action for the effects of isothiocyanates including the modulation of tumor microenvironment, the inhibition of the self-renewal of stem cells, the rearrangement of multiple pathways of energy metabolism, the modulation of microbiota, and protection against Helicobacter pylori. However, the hormetic/biphasic effects of isothiocyanates may make the recommendations complicated. Isothiocyanates possess potent anti-cancer activities based on up-to-date evidence from in vitro and in vivo studies. The nature of hormesis suggests that the benefits or risks of isothiocyanates largely depend on the dose and endpoint of interest. Isothiocyanates are a promising class of cancer-preventative phytochemicals, but researchers should be aware of the potential adverse (and hormetic) effects. In the authors’ opinion, dietary isothiocyanates are better used as adjunctive treatments in combination with known anti-cancer drugs. The application of nano-formulations and the delivery of isothiocyanates are also discussed in this review

Harnessing the Endogenous 2μ Plasmid of Saccharomyces cerevisiae for Pathway Construction

pRS episomal plasmids are widely used in Saccharomyces cerevisiae, owing to their easy genetic manipulations and high plasmid copy numbers (PCNs). Nevertheless, their broader application is hampered by the instability of the pRS plasmids. In this study, we designed an episomal plasmid based on the endogenous 2μ plasmid with both improved stability and increased PCN, naming it p2μM, a 2μ-modified plasmid. In the p2μM plasmid, an insertion site between the REP1 promoter and RAF1 promoter was identified, where the replication (ori) of Escherichia coli and a selection marker gene of S. cerevisiae were inserted. As a proof of concept, the tyrosol biosynthetic pathway was constructed in the p2μM plasmid and in a pRS plasmid (pRS423). As a result, the p2μM plasmid presented lower plasmid loss rate than that of pRS423. Furthermore, higher tyrosol titers were achieved in S. cerevisiae harboring p2μM plasmid carrying the tyrosol pathway-related genes. Our study provided an improved genetic manipulation tool in S. cerevisiae for metabolic engineering applications, which may be widely applied for valuable product biosynthesis in yeast

Some results on the eigenvalue problem for a fractional elliptic equation

Abstract This paper deals with the eigenvalue problem for a fractional variable coefficients elliptic equation defined on a bounded domain. Compared to the previous work, we prove a quite different variational formulation of the first eigenvalue for the above problem. This allows us to give a variational proof of the fractional Faber–Krahn inequality by employing suitable rearrangement techniques

Existence of solutions for perturbed fractional equations with two competing weighted nonlinear terms

Abstract This paper deals with a perturbed nonlocal equation of fractional Laplacian type involving two competing nonlinear terms with weights f and h. Under two kinds of integrability conditions on the ratio of f to h, we show some different existence results in this setting by using variational methods. Our results are extension of some problems studied by Carboni and Mugnai (J. Differ. Equ. 262(3):2393–2413, 2017) and Xiang et al. (J. Differ. Equ. 260(2):1392–1413, 2016)

Finite Time Stability of Finance Systems with or without Market Confidence Using Less Control Input

In this paper, we make an exploration of a technique to control a class of finance chaotic systems. This technique allows one to achieve the finite time stability of the finance system more effectively with less control input energy. First, the finite time stability of three dimension finance system without market confidence is analyzed by using a single controller. Then, two controllers are designed to stabilize the four-dimension finance system with market confidence. Moreover, the finite time stability of the three-dimension and four-dimension finance system with unknown parameter is also studied. Finally, simulation results are presented to show the chaotic behaviour of the finance systems, verify the effectiveness of the proposed control method, and illustrate its advantages compared with other methods

Nonlinear elliptic equations with general growth in the gradient related to Gauss measure

In this article, we establish a comparison result through symmetrization for solutions to some problems with general growth in the gradient. This allows to get sharp estimates for the solutions, obtained by comparing them with solutions of simpler problems whose data depend only on the first variable. Furthermore, we use such result to prove the existence of bounded solutions. All the above results are based on the study of a class of nonlinear integral operator of Volterra type

Impulsive Control of Complex-Valued Neural Networks with Mixed Time Delays and Uncertainties

This paper investigates the global exponential stability of uncertain delayed complex-valued neural networks (CVNNs) under an impulsive controller. Both discrete and distributed time-varying delays are considered, which makes our model more general than previous works. Unlike most existing research methods of decomposing CVNNs into real and imaginary parts, some stability criteria in terms of complex-valued linear matrix inequalities (LMIs) are obtained by employing the complex Lyapunov function method, which is valid regardless of whether the activation functions can be decomposed. Moreover, a new impulsive differential inequality is applied to resolve the difficulties caused by the mixed time delays and delayed impulse effects. Finally, an illustrative example is provided to back up our theoretical results

Method for Evaluating Degradation of Battery Capacity Based on Partial Charging Segments for Multi-Type Batteries

Accurately estimating the capacity degradation of lithium-ion batteries (LIBs) is crucial for evaluating the status of battery health. However, existing data-driven battery state estimation methods suffer from fixed input structures, high dependence on data quality, and limitations in scenarios where only early charge–discharge cycle data are available. To address these challenges, we propose a capacity degradation estimation method that utilizes shorter charging segments for multiple battery types. A learning-based model called GateCNN-BiLSTM is developed. To improve the accuracy of the basic model in small-sample scenarios, we integrate a single-source domain feature transfer learning framework based on maximum mean difference (MMD) and a multi-source domain framework using the meta-learning MAML algorithm. We validate the proposed algorithm using various LIB cell and battery pack datasets. Comparing the results with other models, we find that the GateCNN-BiLSTM algorithm achieves the lowest root mean square error (RMSE) and mean absolute error (MAE) for cell charging capacity estimation, and can accurately estimate battery capacity degradation based on actual charging data from electric vehicles. Moreover, the proposed method exhibits low dependence on the size of the dataset, improving the accuracy of capacity degradation estimation for multi-type batteries with limited data

Charging Behavior Analysis Based on Operation Data of Private BEV Customers in Beijing

Charging behavior is essential to understanding the real performance and evaluating the sustainability of battery electric vehicle (BEV) development and providing the basis for optimal infrastructure deployment. However, it is very hard to obtain the rules, due to lack of the data support, etc. In this research, analyzing the charging behavior of users with private charging piles (PCPs) is carried out based on the real vehicle data of 168 BEV users in Beijing, covering 8825 charging events for a one-year duration. In this study, the charging behaviors are defined by five indexes: the starting state of charge (SOC) of batteries, charging location selection, charging start time, driving distance, and duration between two charging events. To further find the influencing rules of the PCPs owning state, we setup a method to divide the data into two categories to process further analysis and comparison. Meanwhile, in order to better observe the impact of electric vehicle charging on the power grid, we use a Monte Carlo (MC) simulation to predict the charging load of different users based on the analysis. In addition, an agent-based trip chain model (ABTCM), a multinomial logistic regression (MLR), and a machine learning algorithm (MLA) approach are proposed to analyze the charging behavior. The results show that with 40% or lower charging start SOC, the proportion of users without PCPs (weekday: 55.9%; weekend: 59.9%) is larger than users with PCPs (weekday: 45.5%; weekend: 42.6%). Meanwhile, users without PCPs have a certain decrease in the range of 60–80% charging start SOC. The median charging time duration is 51.44 h for users with PCPs and is 17.25 h for users without PCPs. The charging peak effect is evident, and the two types of users have different power consumption distributions. Due to the existence of PCPs, users have lower mileage anxiety and more diverse charging time choices. The analysis results and method can provide a basis for optimal deployment and allocation of charging infrastructure, and to make suitable incentive policies for changing the charging behavior, targeting the carbon neutral objectives