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Learning outcomes can be predicted with machine learning algorithms that assess students’ online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the blended courses offered at a Chinese university and proposed a new classification method of blended courses, in which students were primarily clustered on the basis of their online learning behaviors in blended courses using the expectation–maximization algorithm. Then, the blended courses were classified on the basis of the cluster of students who were present in the course and had the highest proportion. The advantage of this method is that the criteria used for classification of the blended courses are clearly defined on the basis of students' online behavior data, so it can easily be used by machine learning systems to algorithmically classify blended courses based on log data collected from a learning management system. Drawing on the classification of the blended courses, we also proposed and validated a general model using the random forest algorithm to predict learning outcomes based on students’ online behaviors in blended courses with different disciplines and different cohorts. The findings of this study indicated that after blended courses were classified on the basis of students’ online behavior, prediction accuracy in each category increased. The overall accuracies for Course I (380 courses out of 661 after screening), L (14 courses out of 661 after screening), A (237 courses out of 661 after screening), V (8 courses out of 661 after screening), and H (22 courses out of 661 after screening) were 38.2%, 48.4%, 42.3%, 42.4%, and 74.7%, respectively. According to these results, it was found that a prerequisite for the accurate prediction of students' learning outcomes in a blended course was that most students should be highly engaged in a variety of online learning activities rather than being focused on only one type of activity, such as only watching online videos or submitting online assignments. The prediction model achieved accuracies of 80.6%, 85.3%, 63%, 54.8%, and 14.3% for grades A, B, C, D, and F in Course H, respectively. The results demonstrated the potential of the proposed model for accurately predicting learning outcomes in blended courses. Finally, we found that there was no single online learning behavior that had a dominant effect on the prediction of students' final grades.

Autoimmune diseases are usually complex and multifactorial, characterized by aberrant production of autoreactive immune cells and/or autoantibodies against healthy cells and tissues. However, the pathogenesis of autoimmune diseases has not been clearly elucidated. The activation, differentiation, and development of CD8+ T cells can be affected by numerous inflammatory cytokines, transcription factors, and chemokines. In recent years, epigenetic modifications have been shown to play an important role in the fate of CD8+ T cells. The discovery of these modifications that contribute to the activation or suppression of CD8+ cells has been concurrent with the increasing evidence that CD8+ T cells play a role in autoimmunity. These relationships have been studied in various autoimmune diseases, including multiple sclerosis (MS), systemic sclerosis (SSc), type 1 diabetes (T1D), Grave's disease (GD), systemic lupus erythematosus (SLE), aplastic anemia (AA), and vitiligo. In each of these diseases, genes that play a role in the proliferation or activation of CD8+ T cells have been found to be affected by epigenetic modifications. Various cytokines, transcription factors, and other regulatory molecules have been found to be differentially methylated in CD8+ T cells in autoimmune diseases. These genes are involved in T cell regulation, including interferons, interleukin (IL),tumor necrosis factor (TNF), as well as linker for activation of T cells (LAT), cytotoxic T-lymphocyte-associated antigen 4 (CTLA4), and adapter proteins. MiRNAs also play a role in the pathogenesis of these diseases and several known miRNAs that are involved in these diseases have also been shown to play a role in CD8+ regulation.

Background At present, Astragalus mongholicus products on the market represent two growth patterns: imitative wild A. mongholicus (WAM) and cultivated A. mongholicus (CAM) . The 6-year-old WAM (A6) and 2-year-old CAM (B2) products are often sold as commodities. This study aimed to explore the effects of the abovementioned growth patterns on the biosynthetic mechanisms of isoflavone accumulation in A. mongholicus products. Results In this paper, the content of calycosin-7- O -β-D-glucoside in 6-year-old WAM (A6) was significantly higher than that in 2-year-old CAM (B2) based on high-performance liquid chromatography. Tissue anatomy indicated that A6 has developed phloem fibers, thickened secondary walls, and a more well-developed vascular system than B2. Thirteen differentially accumulated metabolites were found in A6 and B2 by UHPLC-ESI-Q-TOF-MS/MS, of which isoflavones were highly and significantly enriched in A6. By combining transcriptomics and metabolomics analysis, we found that the metabolomics profile was the same as the transcriptomics profile in both A6 and B2. In total, 11 novel isoflavone-related genes were isolated using BLAST and functional annotation through RNA-Seq and Iso-Seq. The results of integrated analysis, Short Time-series Expression Miner analysis, and Pearson correlation analysis showed that the regulation of four key enzymes, cinnamate 4-hydroxylase, 6-deoxychalcone synthase, chalcone reductase, and chalcone isomerase, led to the high accumulation of isoflavones in A6. In addition, Am UFGT (c778119) and Am UCGT (c303354) were predicted to be 7- O -glycosyltransferases by phylogenetic analysis; these genes catalyze formononetin and calycosin, respectively. Conclusions The findings of this work will clarify the differences in the biosynthetic mechanism of isoflavone accumulation between A6 and B2, which will guide the cultivation of A. mongholicus .

Blended learning has emerged as a popular trend in education; however, as students from higher vocational colleges who are often in the lower 50% of national standardized entrance exam achievers, their teachers face unique challenges in implementing blended learning. This study summarized effective blended learning strategies in higher vocational education and training (VET) classrooms by comparing 53 award-winning and 40 daily course videos in China. Firstly, video analysis and Lag sequential analysis were employed to identify effective strategies for implementing blended learning in diverse VET course types. A set of general and specific blended learning strategies was developed to help VET teachers adapt instructional approaches accordingly. Secondly, a questionnaire survey among 215 VET teachers revealed positive perceptions of the strategies in terms of usability, ease of use, perceived behavioral control, and intention to use them. The present research provides valuable guidance for VET teachers to effectively implement blended learning strategies in diverse course types, contributing to the understanding of effective blended learning strategies in VET and addressing the gap in research for this unique teaching stage.

Heteroatom doping effectively tunes the electronic conductivity of transition metal selenides (TMSs) with rapid K+ accessibility in potassium ion batteries (PIBs). Although considerable efforts are dedicated to investigating the relationship between the doping strategy and the resulting electrochemistry, the doping mechanisms, especially in view of the ion and electronic diffusion kinetics upon cycling, are seldom elucidated systematically. Herein, the crystal structure stability, charge/ion state, and bandgap of the active materials are found to be precisely modulated by favorable heteroatom doping, resulting in intrinsically fast kinetics of the electrode materials. Based on the combined mechanisms of intercalation and conversion reactions, electron and K+ ion transfer in Ni‐doped CoSe2 embedded in carbon nanocomposites (Ni‐CoSe2@NC) can be significantly enhanced via electronic engineering. Benefiting from the synthetic controlled Ni grains, the heterointerface formed by the intermediate products of electrochemical reactions in Ni‐CoSe2@NC strengthens the conversion kinetics and interdiffusion process, developing a low‐barrier mesophase with optimized potassium storage. Overall, an electronic tuning strategy can offer deeper atomic insights into the conversion reaction of TMSs in PIBs. Heteroatom doping has a significant impact on boosting the performance of secondary battery systems. By engineering the electrodes with controllable composites, ionic and electronic diffusion kinetics are simultaneously obtained. The underlying electrochemical K storage mechanisms based on the intercalation/deintercalation and conversion reactions are illustrated in detail by electrochemical kinetic analysis, theoretical calculations, and X‐ray absorption spectroscopy.

Astaxanthin is a kind of ketone carotenoid belonging to tetraterpenoids with an excellent antioxidant activity and it is widely used in nutrition and health-care industries. This study aimed to explore the effect of different abiotic stresses on carotenoid production in Schizochytrium sp. Firstly, the characteristics of carotenoid accumulation were studied in Schizochytrium sp. by monitoring the change of carotenoid yields and gene expressions. Then, different abiotic stresses were systematically studied to regulate the carotenoid accumulation. Results showed that low temperature could advance the astaxanthin accumulation, while ferric ion could stimulate the conversion from carotene to astaxanthin. The glucose and monosodium glutamate ratio of 100:5 was helpful for the accumulation of β-carotene. In addition, micro-oxygen supply conditions could increase the yield of β-carotene and astaxanthin by 25.47% and 14.92%, respectively. This study provided the potential regulation strategies for carotenoid production which might be used in different carotenoid-producing strains.

This study develops a green, high-performance, cement-based grout by replacing manufactured sand with iron tailings sand (ITS) at ratios of 0–50% to address resource depletion. Fluidity, mechanical strength, and expansion rates were experimentally evaluated to determine engineering feasibility. The results indicate that while ITS inclusion reduces fluidity due to particle morphology, it significantly enhances compressive strength through a physical filling effect. Specifically, the 30% replacement group achieved a peak 28-day compressive strength of 100.4 MPa. Comprehensive analysis identifies 40% as the optimal replacement rate, where the grout strictly satisfies relevant industry specifications regarding fluidity, early strength, and volume stability. This research demonstrates the practical significance of utilizing industrial solid waste to produce high-performance sleeve grout for prefabricated construction.

Background The aim of this study was to identify the candidate biomarkers and pathways associated with psoriasis. GSE13355 and GSE14905 were extracted from the Gene Expression Omnibus (GEO) database. Then the differentially expressed genes (DEGs) with |logFC| > 2 and adjusted P  < 0.05 were chosen. In addition, the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for DEGs were performed. Then, the GO terms with P <  0.05 and overlap coefficient greater than 0.5 were integrated by EnrichmentMap. Additionally, risk subpathways analysis for DEGs was also conducted by using the iSubpathwayMiner package to obtain more psoriasis-related DEGs and pathways. Finally, protein-protein interaction (PPI) network analysis was performed to identify the hub genes, and the DGIdb database was utilized to search for the candidate drugs for psoriasis. Results A total of 127 DEGs which were mostly associated with keratinization, keratinocyte differentiation, and epidermal cell differentiation biological processes were identified. Based on these GO terms, 3 modules (human skin, epidermis and cuticle differentiation, and enzyme activity) were constructed. Moreover, 9 risk subpathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism were screened. Finally, PPI network analysis demonstrated that CXCL10 was the hub gene with the highest degree, and CXCR2 , CXCL10 , IVL , OASL , and ISG15 were the potential gene targets of the drugs for treating psoriasis. Conclusion Psoriasis may be mostly caused by keratinization, keratinocyte differentiation, and epidermal cell differentiation; the pathogeneses were more related with pathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism. Besides, some psoriasis-related genes such as SPRR genes, HSD11B1 , GGH , CXCR2 , IVL , OASL , ISG15 , and CXCL10 may be important targets in psoriatic therapy.

In this paper, both linear and non-linear dynamics of a slender and uniform pipe conveying pulsating fluid, which is axially moving in an incompressible fluid, are comprehensively studied. The vibration equations of the system are established by considering various factors, including a coordinate conversion system, an “axial added mass coefficient” describing the additional inertia forces caused by the external fluid, the Kelvin–Voigt viscoelastic damping, a kind of non-linear additional axial tension, and the pulsating internal fluid. The vibration equations are discretized by the Galerkin procedure and solved by the Runge–Kutta approach, and the validity of the solution procedure is carefully checked. After that, the linear and non-linear responses of the system are studied when the internal flow velocity and the axially moving speed of the pipe are small. For linear responses, the Kelvin–Voigt viscoelastic damping has great influences on the second and third modes of the system. For the non-linear dynamic, the results are rich and changeful, including the first and second principal parametric resonances, the secondary resonance, the combination resonance, period-1 motion, quasi-periodic motion, and chaotic motion. Finally, the influence of several key system parameters on the non-linear responses is analyzed.

Three-dimensional (3D) printing techniques have received considerable focus in the area of bone engineering due to its precise control in the fabrication of complex structures with customizable shapes, internal and external architectures, mechanical strength, and bioactivity. In this study, we design a new composition biomaterial consisting of polylactic acid (PLA), and halloysite nanotubes (HNTs) loaded with zinc nanoparticles (PLA+H+Zn). The hydrophobic surface of the 3D printed scaffold was coated with two layers of fetal bovine serum (FBS) on the sides and one layer of NaOH in the middle. Additionally, a layer of gentamicin was coated on the outermost layer against bacterial infection. Scaffolds were cultured in standard cell culture medium without the addition of osteogenic medium. This surface modification strategy improved material hydrophilicity and enhanced cell adhesion. Pre-osteoblasts cultured on these scaffolds differentiated into osteoblasts and proceeded to produce a type I collagen matrix and subsequent calcium deposition. The 3D printed scaffolds formed from this composition possessed high mechanical strength and showed an osteoinductive potential. Furthermore, the external coating of antibiotics not only preserved the previous osteogenic properties of the 3D scaffold but also significantly reduced bacterial growth. Our surface modification model enabled the fabrication of a material surface that was hydrophilic and antibacterial, simultaneously, with an osteogenic property. The designed PLA+H+Zn may be a viable candidate for the fabrication of customized bone implants.