Nobiletin enhances the antifungal activity of eugenol nanoemulsion against Penicillium italicum in both in vitro and in vivo settings.

The study prepared and used eugenol nanoemulsion loaded with nobiletin as fungistat to study its antifungal activity and potential mechanism of Penicillium italicum (P. italicum). The results showed that the minimum inhibitory concentration (MIC) of eugenol nanoemulsion loaded with nobiletin (EGN) was lower than that of pure eugenol nanoemulsion (EG), which were 160 µg/mL and 320 µg/mL, respectively. At the same time, the mycelial growth inhibition rate of EGN nanoemulsion (54.68 %) was also higher than that of EG nanoemulsion (9.92 %). This indicates that EGN nanoemulsion is more effective than EG nanoemulsion. Compared with EG nanoemulsion, the treatment of EGN nanoemulsion caused more serious damage to the cell structure of P. italicum. At the same time, in vitro inoculation experiments found that EGN nanoemulsion has better control and delay the growth and reproduction of P. italicum in citrus fruits. And the results reflected that EGN nanoemulsion may be considered as potential resouces of natural antiseptic to inhibit blue mold disease of citrus fruits, because it has good antifungal activity.

Photo-Cross-Linked Polymeric Dispersants of Comb-Shaped Benzophenone-Containing Poly(ether amine).

Efficient dispersion of nanoparticles (NPs) is a crucial challenge in the preparation and application of composites that contain NPs, particularly in coatings, inks, and related materials. Physical adsorption and chemical modification are the two common methods used to disperse NPs. However, the former suffers from desorption, and the latter is more specific and has limited versatility. To address these issues, we developed a novel photo-cross-linked polymeric dispersant, comb-shaped benzophenone-containing poly(ether amine) (bPEA), using a one-pot nucleophilic/cyclic-opening addition reaction. The results demonstrated that the bPEA dispersant forms a dense and stable shell on the surface of pigment NPs through physical adsorption and subsequent chemical photo-cross-linking, which effectively overcome the drawbacks of the desorption occurred in physical adsorption and the specificity of the chemical modification. By means of the dispersing effect of bPEA, the obtained pigment dispersions show high solvent, thermal, and pH stability without flocculation during storage. Moreover, the NPs dispersants show good compatibility with screen printing, coating, and 3D printing, endowing the ornamental products with high uniformity, color fastness, and less color shading. These properties make bPEA dispersants ideal candidates in fabrication dispersions of other NPs.

The Influence of Emotional Experience on Semantic Processing of Concrete Concepts.

Introduction: Parallel distributed processing theory (PDP theory) holds that all brain regions involved in conceptual representation perform a series of activities at the same time. However, the role of emotional experience information in concrete conceptual representation is still unknown. This study further explores whether the emotional experience will also affect the semantic processing of concrete concept representations. Methods: This study used the emotion priming paradigm and semantic judgment task to explore whether emotion priming impacts the processing of animal concepts with different emotional experiences through two experiments. In Experiments 1a and 1b, pleasant or disgusted faces were used as emotional priming stimuli to explore whether the explicit processing of emotions would affect the semantic processing of animal concepts. Experiments 2a and 2b used positive or negative scenery pictures as emotional priming stimuli to explore whether the implicit processing of emotions would affect the semantic processing of animal concepts. Results: The Experiment 1 results showed that the perception of faces promotes the processing of animal words, showing the "word-emotion congruence effect". Experiment 2a did not show the expected results, while Experiment 2b showed that the general negative perception of scenery pictures could significantly promote the processing of disgusted animal words. The results further proved the "word-emotion congruence effect" shown in the results of Experiment 1 from the perspective of implicit emotion processing. Combining the results of two experiments, it can be proven that emotional experience affects the semantic processing process of concrete concepts. Discussion: Both Experiment 1 and Experiment 2b of this study show the "word-emotion congruence effect". PDP theory believes that conceptual representation is represented by the activity patterns of billions of neurons distributed in many areas of the brain, and related semantic processing and sensory processing will occur simultaneously. The results of this experiment well support PDP theory.

The relationship between self-esteem and self-concept clarity is modulated by spontaneous activities of the dACC.

Although it has been found that self-esteem and self-concept clarity are positively correlated, self-determination theory shows that the positive relationship might be lowered for individuals whose basic psychological needs are chronically thwarted. The exact neural mechanisms underlying the relationship between self-esteem and self-concept clarity are still not fully understood. The dorsal anterior cingulate cortex (dACC) plays an important role in monitoring basic psychological needs, considering that it is more active when some basic psychological needs are actually or potentially thwarted. To better understand the neural mechanisms underlying the relationship between self-esteem and self-concept clarity, we investigated the differences in the relationship between self-esteem and self-concept clarity among healthy adults with different levels of spontaneous activities of the dACC using rs-fMRI combined with amplitude of low-frequency fluctuation (ALFF). As expected, the results showed that the positive relationship between self-esteem and self-concept clarity was modulated by the ALFF value of the right dACC, which indicated that the positive relationship was significant when the ALLF value of the right dACC was lower, but the positive relationship was not significant when the ALFF value of the right dACC was higher. The modulating roles of right dACC might also reflect that the individuals with higher ALFF value of dACC might experience chronically thwarted relatedness of basic psychological needs, which means the more disturbed by thwarting relatedness information in individuals, the lower positive relationship emerged.

Neural underpinnings of morality judgment and moral aesthetic judgment.

Morality judgment usually refers to the evaluation of moral behavior`s ability to affect others` interests and welfare, while moral aesthetic judgment often implies the appraisal of moral behavior's capability to provide aesthetic pleasure. Both are based on the behavioral understanding. To our knowledge, no study has directly compared the brain activity of these two types of judgments. The present study recorded and analyzed brain activity involved in the morality and moral aesthetic judgments to reveal whether these two types of judgments differ in their neural underpinnings. Results reveled that morality judgment activated the frontal, parietal and occipital cortex previously reported for motor representations of behavior. Evaluation of goodness and badness showed similar patterns of activation in these brain regions. In contrast, moral aesthetic judgment elicited specific activations in the frontal, parietal and temporal cortex proved to be involved in the behavioral intentions and emotions. Evaluation of beauty and ugliness showed similar patterns of activation in these brain regions. Our findings indicate that morality judgment and moral aesthetic judgment recruit different cortical networks that might decode others' behaviors at different levels. These results contribute to further understanding of the essence of the relationship between morality judgment and aesthetic judgment.

Exploiting bi-directional global transition patterns and personal preferences for missing POI category identification.

Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past information for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance.

[Cloning, expression and characterization of a new endo-ß-N-acetylglucosaminidase from Streptomyces alfalfae].

Endo-ß-N-acetylglucosaminidase is used widely in the glycobiology studies and industries. In this study, a new endo-ß-N-acetylglucosaminidase, designated as Endo SA, was cloned from Streptomyces alfalfae ACCC 40021 and expressed in Escherichia coli BL21 (DE3). The purified recombinant Endo SA exhibited the maximum activity at 35 ºC and pH 6.0, good thermo/pH stability and high specific activity (1.0×106 U/mg). It displayed deglycosylation activity towards different protein substrates. These good properties make EndoSA a potential tool enzyme and industrial biocatalyst.

Data-Aware Retrodiction for Asynchronous Harmonic Measurement in a Cyber-Physical Energy System.

Cyber-physical energy systems provide a networked solution for safety, reliability and efficiency problems in smart grids. On the demand side, the secure and trustworthy energy supply requires real-time supervising and online power quality assessing. Harmonics measurement is necessary in power quality evaluation. However, under the large-scale distributed metering architecture, harmonic measurement faces the out-of-sequence measurement (OOSM) problem, which is the result of latencies in sensing or the communication process and brings deviations in data fusion. This paper depicts a distributed measurement network for large-scale asynchronous harmonic analysis and exploits a nonlinear autoregressive model with exogenous inputs (NARX) network to reorder the out-of-sequence measuring data. The NARX network gets the characteristics of the electrical harmonics from practical data rather than the kinematic equations. Thus, the data-aware network approximates the behavior of the practical electrical parameter with real-time data and improves the retrodiction accuracy. Theoretical analysis demonstrates that the data-aware method maintains a reasonable consumption of computing resources. Experiments on a practical testbed of a cyber-physical system are implemented, and harmonic measurement and analysis accuracy are adopted to evaluate the measuring mechanism under a distributed metering network. Results demonstrate an improvement of the harmonics analysis precision and validate the asynchronous measuring method in cyber-physical energy systems.

Resolution-Enhanced Harmonic and Interharmonic Measurement for Power Quality Analysis in Cyber-Physical Energy System.

Power quality analysis issues, especially the measurement of harmonic and interharmonic in cyber-physical energy systems, are addressed in this paper. As new situations are introduced to the power system, the impact of electric vehicles, distributed generation and renewable energy has introduced extra demands to distributed sensors, waveform-level information and power quality data analytics. Harmonics and interharmonics, as the most significant disturbances, require carefully designed detection methods for an accurate measurement of electric loads whose information is crucial to subsequent analyzing and control. This paper gives a detailed description of the power quality analysis framework in networked environment and presents a fast and resolution-enhanced method for harmonic and interharmonic measurement. The proposed method first extracts harmonic and interharmonic components efficiently using the single-channel version of Robust Independent Component Analysis (RobustICA), then estimates the high-resolution frequency from three discrete Fourier transform (DFT) samples with little additional computation, and finally computes the amplitudes and phases with the adaptive linear neuron network. The experiments show that the proposed method is time-efficient and leads to a better accuracy of the simulated and experimental signals in the presence of noise and fundamental frequency deviation, thus providing a deeper insight into the (inter)harmonic sources or even the whole system.

Understanding and enhancement of internal clustering validation measures.

Clustering validation has long been recognized as one of the vital issues essential to the success of clustering applications. In general, clustering validation can be categorized into two classes, external clustering validation and internal clustering validation. In this paper, we focus on internal clustering validation and present a study of 11 widely used internal clustering validation measures for crisp clustering. The results of this study indicate that these existing measures have certain limitations in different application scenarios. As an alternative choice, we propose a new internal clustering validation measure, named clustering validation index based on nearest neighbors (CVNN), which is based on the notion of nearest neighbors. This measure can dynamically select multiple objects as representatives for different clusters in different situations. Experimental results show that CVNN outperforms the existing measures on both synthetic data and real-world data in different application scenarios.