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Artificial intelligence (AI) has experienced exponential growth in recent years, profoundly transforming various aspects of life, particularly in education. As a team of educational technology innovators, we have developed a series of generative AI features within our social learning management system, CourseNetworking, to boost student engagement in online discussions and enhance instructor efficiency. This article introduces three key features: automated hashtagging, automated post reflection, and AI-driven quiz question generation. We examine the goals and technical implementation of each feature, present solutions to challenges encountered during development and deployment, and share the best practices we have accumulated. Additionally, we outline our plans for future advancements. This paper seeks to foster understanding and collaboration among developers, educators, institutional leaders, and researchers, with the aim of using AI technology to improve educational products and services, ultimately elevating learning experiences and outcomes. [ABSTRACT FROM AUTHOR]

As online learning and teaching are becoming an educational trend, online students' engagement will directly impact the learning and teaching effects and outcomes. A scientific application of gamification in online learning, teaching, and online course design will improve online learners' learning experience and help build a better virtual learning context for online learners worldwide. This study focuses on how gamification can engage online students from skills, emotional, participation, and performance perspectives. The gamification components investigated in this study are Points (also called Anar Seeds in the context of this study) and Badges, the most widely used components in gamification design in education, and online instructors have primarily used them to motivate students' learning and acknowledge their achievements by timely rewarding specific learning behavior or performance. A mixed method has been applied to explore further the relationships between gamification components and online students' engagement and how online students perceive the impacts of gamification on their online learning experience. The findings show a significant correlation between Points and online students' skills, emotional, participation, and performance engagement, while it exists only between Badges and online students' participation engagement. The findings also reveal mixed perceptions of online students towards gamification. Study limitations regarding lack of age criterion, limited measurement indicators, and oversimplification of survey responses have also been discussed. It is suggested that future research can be conducted from either the instructors' or the gamification designers' perspectives to determine any other factors that might contribute to the implementation of gamification in the online learning context. [ABSTRACT FROM AUTHOR]

This study investigated a nanostructured lipid carrier (NLC)-gel system containing luteolin (LUT), a potential drug delivery system for the treatment of psoriasis. LUT-NLC was prepared by solvent emulsification ultrasonication method. The particle size was 199.9 ± 2.6 nm, with the encapsulation efficiency of 99.81% and drug loading of 4.06%. X-ray diffractometry (XRD), Fourier-transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) were used to characterize the LUT-NLC. The NLC was dispersed in Carbomer 940 to form the NLC based gel. The rheological characteristics of LUT-NLC-gel showed an excellent shear-thinning behavior (non-Newtonian properties) and coincided with the Herschel-Bulkley model. LUT-NLC-gel (78.89 μg/cm2) exhibited better permeation properties and released over 36 hours than LUT gel (32.17 μg/cm2). The dye-labeled LUT-NLC presented intense fluorescence in the epidermis and dermis by the visualization of fluorescence and confocal microscopy, and it could accumulate in the hair follicles. The effect of LUT-NLC-gel on imiquimod-induced psoriasis mice was evaluated by psoriasis area severity index scoring, spleen index assay, histopathology, and inflammatory cytokines. These results confirmed that LUT-NLC-gel with high dose (80 mg/kg/day) remarkably reduced the level of inflammatory and proliferation factors such as TNF-α, IL-6, IL-17, and IL-23 in both skin lesions and blood. LUT-NLC-gel improved the macroscopic features. Therefore, the LUT-NLC-gel had great potential as an effective delivery system for skin diseases. [ABSTRACT FROM AUTHOR]

Herein, we reported a novel niclosamide (NA) electrochemical sensor based on the multifunctional nanocomposite of palygorskite (Pal) nanofibers and bifunctional multiwalled carbon nanotubes (BMCN) with graphitization and carboxylation. For the BMCN@Pal nanocomposite, BMCN with one-dimensional nanotube structure possessed excellent electrical conductivity and good dispersion performance due to the graphitization and carboxylation; Pal with fibrous structure possessed good electrocatalytic activity due to the high absorption capability, large specific surface area, and prominent stability. Moreover, BMCN with interconnected carbon conductive network promoted the uniform distribution of Pal nanofibers and compensated for the poor electrical conductivity of Pal nanofibers. Thanks to the functional combination of BMCN and Pal, the BMCN@Pal/GCE sensor exhibited good NA electrochemical detection performance with low limit of detection of 7.88 nM in linear NA concentration of 0.01–10 μM. The BMCN@Pal/GCE sensor exhibited good repeatability and reproducibility. In addition, a good practical feasibility can be achieved at the BMCN@Pal/GCE sensor for the detection of NA in tap water and pond water samples (recovery rate: 96.2–100.3%, relative standard deviation: 2.53–4.97). [ABSTRACT FROM AUTHOR]

Ferroptosis is a lipid peroxidation-driven and iron-dependent programmed cell death involved in multiple physical processes and various diseases. Emerging evidence suggests that several pathogens manipulate ferroptosis for their pathogenicity and dissemination, but the underlying molecular mechanisms remain elusive. Here, we identify that protein tyrosine phosphatase A (PtpA), an effector secreted by tuberculosis (TB)-causing pathogen Mycobacterium tuberculosis (Mtb), triggers ferroptosis to promote Mtb pathogenicity and dissemination. Mechanistically, PtpA, through its Cys11 site, interacts with host RanGDP to enter host cell nucleus. Then, the nuclear PtpA enhances asymmetric dimethylation of histone H3 arginine 2 (H3R2me2a) via targeting protein arginine methyltransferase 6 (PRMT6), thus inhibiting glutathione peroxidase 4 (GPX4) expression, eventually inducing ferroptosis to promote Mtb pathogenicity and dissemination. Taken together, our findings provide insights into molecular mechanisms of pathogen-induced ferroptosis, indicating a potential TB treatment via blocking Mtb PtpA-host PRMT6 interface to target GPX4-dependent ferroptosis. Ferroptosis is an iron-dependent form of cell death whose role in infectious diseases is being elucidated. Here, Qiang et al. show that PtpA, an effector secreted by Mycobacterium tuberculosis, induces ferroptosis by hijacking host arginine methyltransferase PRMT6 to promote its pathogenicity and dissemination. [ABSTRACT FROM AUTHOR]

With the development of Internet technology, the phenomenon of information overload is becoming more and more obvious. It takes a lot of time for users to obtain the information they need. However, keyphrases that summarize document information highly are helpful for users to quickly obtain and understand documents. For academic resources, most existing studies extract keyphrases through the title and abstract of papers. We find that title information in references also contains author-assigned keyphrases. Therefore, this article uses reference information and applies two typical methods of unsupervised extraction methods (TF*IDF and TextRank), two representative traditional supervised learning algorithms (Naïve Bayes and Conditional Random Field) and a supervised deep learning model (BiLSTM-CRF), to analyze the specific performance of reference information on keyphrase extraction. It is expected to improve the quality of keyphrase recognition from the perspective of expanding the source text. The experimental results show that reference information can increase precision, recall, and F1 of automatic keyphrase extraction to a certain extent. This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the research on automatic keyphrase extraction. [ABSTRACT FROM AUTHOR]

To significantly improve the electrochemical performance of LiMn2O4, we proposed a simple and low-cost co-modification approach based on the synergistic effect of poly(ethylene glycol) (PEG) and carbon nanotubes (CNT). The CNT@PEG@LiMn2O4 (CNT@PEG@LMO) composite was prepared by ultrasound assisted wet-coating method. PEG is a highly conductive ionic conductor, which can improve the transport efficiency of lithium ions, and CNT is a good electronic conductor, which possesses excellent electrical conductivity. Moreover, the combination of PEG and CNT can effectively inhibit the dissolution of manganese and promote the uniform distribution of LiMn2O4 octahedra. The structure and surface morphology of the CNT@PEG@LMO composite were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The electrochemical test result showed that the initial discharge capacity of the CNT@PEG@LMO composite could reach up to 123.8 mAh g−1 at 1.0 C with capacity retention of 95.2 % after 100 cycles, which is higher than that of the unmodified LiMn2O4 sample. The high-rate capability of the CNT@PEG@LMO composite was significantly improved. When cycled at 55 °C, the CNT@PEG@LMO composite could show good cycling stability and rate performance. Such excellent electrochemical performance is due to close association with the synergistic effect of PEG and CNT. [ABSTRACT FROM AUTHOR]

In order to obtain the stability of pillars in Daqiao Phosphate Mine, the stability of pillars in goaf was studied by theoretical calculation. Based on the results of investigation and the calculation formula of pillar strength, a mathematical formula for calculating pillar stability was established to predict the stability of uninvestigated pillars. The effect of various factors on the stability of the pillar was analyzed by orthogonal test. The research results show that the pillar area, rock column area, and mining depth have the most significant effect on pillar stability. A mathematical formula for predicting pillar stability is constructed by means of dimensional grading method. The critical value of pillar factor is 0.06 in Daqiao Phosphate Mine. When the pillar factor is greater than 0.06, the pillar is stable. When the pillar factor is less than 0.06, the pillar is unstable. The reliability of theoretical calculation method of pillar factor is verified by numerical simulation. A mathematical formula for predicting the stability of pillars is proposed, which can be used as a reference for the stability analysis of goafs in similar mines. [ABSTRACT FROM AUTHOR]

Erythromycin (ERY) molecular imprinted polymer was prepared on the surface of Au electrode modified with Ni and Au nanoclusters (MIP/AuNCs/Ni/Au) via metal-free visible-light-induced atom transfer radical polymerization, which was very easy, controllable, and environmentally benign. MIP/AuNCs/Ni/Au was examined by energy dispersive spectrometer (EDS), scanning electron microscope (SEM), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). Further, MIP/AuNCs/Ni/Au was successfully used for the determination of ERY by differential pulse voltammetry (DPV). Under the optimal conditions, MIP/AuNCs/Ni/Au gave a linear correlation over the ERY concentration from 1.0 × 10−8 to 1.0 × 10−1 mg/L with the detection limit of 3.2 × 10−9 mg/L (S/N = 3). Comparing with the similar sensors, the larger linear range and lower detection limit indicated the promising prospect of this electrochemical sensor. Hence, this study was of great importance for the polymer-modified electrodes and the sensitive detection of ERY. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopadie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The influence of the chemical composition of BiO-BO-SiO-ZnO glass brazes on (i) the microstructure, (ii) the mechanical and (iii) the dielectric properties of Li-Ti ferrite joints was systematically investigated. The Bi(TiFe)O whisker and a white block phase consisting of BiSiO and BiBO were observed in the joints of Li-Ti ferrite/Bi25-Ba and Li-Ti ferrite/glass brazes, respectively, containing a higher content of BiO. No crystalline phase was detected in the Li-Ti ferrite/Bi25 and Li-Ti ferrite/Bi20 joints. The joint strength reached the maximum of 48 MPa in the Li-Ti ferrite/Bi25-Ba couples. It is assumed that this is mainly due to the strengthening effect of Bi(TiFe)O whiskers. The bonding temperature (700°C) had little effect on the dielectric properties of Li-Ti ferrite. Moreover, compared to the Bi25-Ba glass brazes, the Bi25 and Bi20 glass brazes had a less pronounced influence on the dielectric properties of joints. Different glass brazes can be tailored to different requirements depending on specific application and joint property requirements. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopadie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Interactions among neurons are a key component of neural signal processing. Rich neural data sets potentially containing evidence of interactions can now be collected readily in the laboratory, but existing analysis methods are often not sufficiently sensitive and specific to reveal these interactions. Generalized linear models offer a platform for analyzing multi-electrode recordings of neuronal spike train data. Here we suggest an L-regularized logistic regression model ( L L method) to detect short-term (order of 3 ms) neuronal interactions. We estimate the parameters in this model using a coordinate descent algorithm, and determine the optimal tuning parameter using a Bayesian Information Criterion. Simulation studies show that in general the L L method has better sensitivities and specificities than those of the traditional shuffle-corrected cross-correlogram (covariogram) method. The L L method is able to detect excitatory interactions with both high sensitivity and specificity with reasonably large recordings, even when the magnitude of the interactions is small; similar results hold for inhibition given sufficiently high baseline firing rates. Our study also suggests that the false positives can be further removed by thresholding, because their magnitudes are typically smaller than true interactions. Simulations also show that the L L method is somewhat robust to partially observed networks. We apply the method to multi-electrode recordings collected in the monkey dorsal premotor cortex (PMd) while the animal prepares to make reaching arm movements. The results show that some neurons interact differently depending on task conditions. The stronger interactions detected with our L L method were also visible using the covariogram method. [ABSTRACT FROM AUTHOR]