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UNLABELLED: In an effort to identify novel compounds with potent inhibition against Toxoplasma gondii, a phenotypic screen was performed utilizing a library of 683 pure compounds derived primarily from terrestrial and marine fungi. An initial screen with a fixed concentration of 5 µM yielded 91 hits with inhibition comparable to an equal concentration of artemisinin. These compounds were then triaged based on known biological and chemical concerns and liabilities. From these, 49 prioritized compounds were tested in a dose response format with T. gondii and human foreskin fibroblasts (HFFs) for cytotoxicity. Ten compounds were identified with an IC50 less than 150 nM and a selectivity index (SI) greater than 100. An additional eight compounds demonstrated submicromolar IC50 and SI values equal to or greater than 35. While the majority of these scaffolds have been previously implicated against apicomplexan parasites, their activities in T. gondii were largely unknown. Herein, we report the T. gondii activity of these compounds with chemotypes including xanthoquinodins, peptaibols, heptelidic acid analogs, and fumagillin analogs, with multiple compounds demonstrating exceptional potency in T. gondii and limited toxicity to HFFs at the highest concentrations tested. IMPORTANCE: Current therapeutics for treating toxoplasmosis remain insufficient, demonstrating high cytotoxicity, poor bioavailability, limited efficacy, and drug resistance. Additional research is needed to develop novel compounds with high efficacy and low cytotoxicity. The success of artemisinin and other natural products in treating malaria highlights the potential of natural products as anti-protozoan therapeutics. However, the exploration of natural products in T. gondii drug discovery has been less comprehensive, leaving untapped potential. By leveraging the resources available for the malaria drug discovery campaign, we conducted a phenotypic screen utilizing a set of natural products previously screened against Plasmodium falciparum. Our study revealed 18 compounds with high potency and low cytotoxicity in T. gondii, including four novel scaffolds with no previously reported activity in T. gondii. These new scaffolds may serve as starting points for the development of toxoplasmosis therapeutics but could also serve as tool compounds for target identification studies using chemogenomic approach.

Our previous study identified 52 antiplasmodial peptaibols isolated from fungi. To understand their antiplasmodial mechanism of action, we conducted phenotypic assays, assessed the in vitro evolution of resistance, and performed a transcriptome analysis of the most potent peptaibol, HZ NPDG-I. HZ NPDG-I and 2 additional peptaibols were compared for their killing action and stage dependency, each showing a loss of digestive vacuole (DV) content via ultrastructural analysis. HZ NPDG-I demonstrated a stepwise increase in DV pH, impaired DV membrane permeability, and the ability to form ion channels upon reconstitution in planar membranes. This compound showed no signs of cross resistance to targets of current clinical candidates, and 3 independent lines evolved to resist HZ NPDG-I acquired nonsynonymous changes in the P. falciparum multidrug resistance transporter, pfmdr1. Conditional knockdown of PfMDR1 showed varying effects to other peptaibol analogs, suggesting differing sensitivity.

In order to improve the enthusiasm in English-medium college students, so as to improve students’ reading performance in English, the GLE teaching mode is integrated into the curriculum. Firstly, a planning model is constructed, and the secondary planning is realised through the transition probability matrix; then, the training vocabulary related data are clustered through the membership function; the weights of neurons in each layer are modified by adjusting the membership function again so that the error signal is flagged. Finally, through the knowledge ontology calculation, a reinforcement learning model is obtained after obtaining the vocabulary set for training; the experiment shows that after adopting the teaching mode of this paper and the traditional teaching mode, the average score of the experimental class is improved by 22 points compared with the control class. And the significance level is 0.000

The present research conducted two experiments with an intelligent tutoring system to investigate the overall and dynamic impact of emotional support from a pedagogical agent (PA). In Experiment 1, a single factor intergroup design was used to explore the impact of PA's emotional support (supportive vs. non-supportive) on learners' emotions, intrinsic motivation, and learning gain. Sixty participants were recruited and randomly assigned to one of the two conditions. Experiment 2 also conducted a single factor between-subjects design to investigated the dynamic patterns between emotions and learning strategies among 30 participants using lag sequential analysis. Results showed that: Compared with the non-supportive pedagogical agent, the supportive pedagogical agent reduced frustration and improved learning gain, but did not increase intrinsic motivation. In addition, learners with the supportive pedagogical agent used more appropriate strategies after frustration and surprise, and use less ineffective strategies after confusion and enjoyment to avoid reaching a wrong answer. If learners did not receive emotional support in such cases, learning strategies following these emotions were more likely lead to negative cognitive results, or negative emotions tended to appear repeatedly. Instructors or PAs should identify learners' emotions in time and provide the appropriate emotional support according to learners' emotions. [ABSTRACT FROM AUTHOR]

This study aims to refine the ratio of alkali-activated steel slag (SS) to granulated blast furnace slag (GBFS)–cement-based grouting materials, with the dual objectives of cost reduction and performance enhancement. By employing single-factor experiments and response surface methodology (RSM), we have pinpointed the critical factors that influence the slurry's performance and developed a regression model to assess the impact of these factors and their interplay. Our findings indicate that the compressive strength initially increases with higher SS content but subsequently declines. Additionally, an increase in alkali content and activator modulus is beneficial for strength improvement. However, beyond an alkali content of 8%, the 28-day strength is observed to decrease. Through meticulous model analysis, we have determined the optimal ratio to be 7.07% SS content, 7.82% alkali content, and an activator modulus of 1.8. The material's performance at this ratio satisfies construction specifications. This research not only offers a cost-effective and high-performance grouting solution for geotechnical applications but also pioneers a novel approach to the resourceful utilization of solid waste materials, such as SS. [ABSTRACT FROM AUTHOR]

The burgeoning advancement of information technology has engendered a discernible surge in the examination of neuromorphic devices, notably drawing broader attention to artificial vision systems endowed with sensory recognition capabilities. Current photoelectric synapse devices employed in artificial vision systems are generally well‐suited for well‐illuminated conditions, yet exhibit diminished sensitivity in weak‐light scenarios, resulting in a pronounced deterioration of recognition accuracy. Here, an ultrasensitive photoelectric synaptic transistor based on negative quantum capacitance effect resulted from the 2D semi‐metallic graphene layer that partially enclosed within the gate dielectric layer, which manifests a noteworthy reduction in device control voltage and exhibits perception and storage capabilities for weak light of 39.4 nW cm−2 with detectivity above 1016 cm Hz1/2 W−1 is demonstrated. The voltage amplification effect and the concomitant formation of an equivalent local electrostatic field induced by the negative quantum capacitance effect engenders a robust programmable synaptic plasticity for extremely weak light by modifying the control gate. These results represent the inaugural integration of the negative quantum capacitance effect into optoelectronic devices and furnish a robust hardware foundation for developing vision systems in weak‐light environments. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, the importance of risk management in underground construction projects has become increasingly prominent. In the process of risk assessment for underground construction projects, the uncertainty of subjective factors from experts poses a significant challenge to the accuracy of assessment outcomes. This paper takes a section of the Nanchang Metro Line 2 as the research object, aiming to address the subjectivity issues in the risk assessment of underground construction projects and to enhance the scientific rigor and accuracy of the assessment. The study initially conducts a comprehensive identification and analysis of risk factors in underground engineering through a literature review and expert consultation method. Based on this, this paper introduces the theory of Pythagorean fuzzy sets to improve the Delphi method in order to reduce the impact of subjectivity in expert assessments. Furthermore, this paper constructs a Bayesian network model, incorporating risk factors into the network, and quantifies the construction risks through a probabilistic inference mechanism. The research findings indicate a total of 12 key risk factors that have been identified across four dimensions: geological and groundwater conditions, tunnel construction technical risks, construction management measures, and the surrounding environment. The Bayesian network assessment results indicate that the effectiveness of engineering quality management and the state of safety management at the construction site are the two most influential factors. Based on the assessment results, this paper further conducts a risk control analysis and proposes targeted risk management measures. [ABSTRACT FROM AUTHOR]