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Synthesis and purification of three carbon dots and the application of R-CDs in microalgae lipid imaging.

With global warming, plants often suffer damage from high temperatures during the growth process, which inhibits their growth. In this work, carbon dots (CDs), synthesized by Salvia miltiorrhiza (S. miltiorrhiza) with a one-step hydrothermal method, were selected as heat-resistant enhancement agents for plants. Inspired by this background, this work studied Italian lettuce grown at 25, 35, and 45 °C and treated with CD and deionized water control (sprayed on leaves). The results showed that the biomass, chlorophyll content, net photosynthetic rate, activities of SOD (superoxide dismutase), POD (peroxidase), CAT (catalase), soluble sugar, and soluble protein contents of lettuce treated by CDs were increased while the contents of malondialdehyde (MDA) and proline (Pro) were decreased at 35 and 45 °C. The application of CDs at 35 and 45 °C could maintain the growth of plants by reducing oxidative damage and lipid peroxidation especially at the temperature of 35 °C, the growth status of lettuce treated by CDs was no different from that of lettuce grown naturally at the optimal temperature of 25 °C, or even better than the latter. This finding verified that the CDs could significantly improve the high-temperature tolerance of lettuce, thus alleviating the heat stress of plants.

TiC/Fe0.6MnNi1.4 medium entropy alloy matrix composites with different volume fractions of TiC particles (i.e., 5, 10 and 15 vol%) were prepared by induction melting method. The experimental results showed that both the base alloy and the composite matrix possess face-centered cubic structure. The in-situ TiC reinforcement phase can significantly increase the mechanical properties of the composites at room temperature. In particular, the hardness, yield strength and ultimate tensile strength of the 10 vol%TiC/Fe0.6MnNi1.4 composite are found respectively to be 528 HV, 345.1 MPa and 716.7 MPa, representing a marked increase by 51.7%, 41.1% and 30.7% in comparison with the Fe0.6MnNi1.4 medium entropy alloy. The ductility of the composite in terms of the elongation to failure is reduced from 65.8% to 36.6% with the increase of the TiC content, which remains attractive for load-bearing structural applications. The strength improvement is underpinned by a combination of the load-bearing effect, Orowan process, dislocation interactions and solid solution effect.

Pulmonary hypertension (PH) is mainly characterized by abnormal pulmonary vascular hyperplasia and vascular remodeling, but its mechanism is complicated and currently unclear. Cysteine and glycine-rich protein 2 (Csrp2) has been reported to promote cell proliferation and migration, and affect cell cycle progression. As a new invasive actin-binding factor, Csrp2 increased the invasion and even metastasis of some cancer cells. It was associated with tumor recurrence and chemotherapy resistance. However, the role of Csrp2 in PH remains unknown. We found that Csrp2 expression was increased both in pulmonary arteries (PAs) and smooth muscle cells (PASMCs) in PH. Csrp2 enhanced PASMC proliferation and phenotypic transition. The Wnt3α-β-catenin/lymphoid enhancer-binding factor 1 (LEF1) pathway is involved in cell proliferation and phenotypic transition regulated by Csrp2 expression. These results suggest that hypoxia downregulates YinYang-1 (YY1) and then increases Csrp2 expression. Increased Csrp2 promotes PASMC proliferation and phenotypic transition by activating the Wnt3α-β-catenin/LEF1 pathways, which leads to pulmonary vascular remodeling and even provides a new theoretical basis for studying the pathogenesis and therapeutic targets of PH.

Corpse management is essential for social animals to maintain colony health. In the eastern subterranean termite, Reticulitermes flavipes, workers carry out undertaking behaviors to mitigate the risks associated with the dead. In this study, we hypothesized that termites would respond differently to the corpses from different castes based on their postmortem chemical signatures. To test this hypothesis, we 1) documented the behavioral responses of the workers toward corpses from different castes, and 2) profile the chemical signatures of these corpses. Corpses from all castes were retrieved inside the nests and cannibalized when they were decomposed

The effect of Cr content on the microstructure and mechanical properties of CrxFeNiCu high entropy alloys (HEAs) was firstly studied by first-principles calculations. The calculated results show that the hardness of the alloys increased with the expense of its plasticity decrease, if the content of Cr in the alloy increased. In order to verify the calculated results, CrxFeNiCu (x = 0.8, 1, 1.5 and 2) high entropy alloys were synthesized by vacuum induction melting in the present study. The results show that as the value of x increased from 0.8 to 2, the crystal structure changed from single phase face centered cubic (FCC) phase to a mixture of FCC and body centered cubic (BCC) phases. For the single phase FCC (x = 0.8) structure, both the tensile strength and hardness values were low, which were 491.6 MPa and 322.2 HV respectively, however, the plasticity was high, reaching 33.2%. With the formation and growth of BCC phase (x = 2) the tensile strength and hardness of the alloy were significantly improved, which were 872.6 MPa and 808 HV, respectively.

Pulmonary hypertension (PH) is a serious cardiovascular disease with a poor prognosis and high mortality. The pathogenesis of PH is complex, and the main pathological changes in PH are abnormal hypertrophy and vessel stiffness. Cysteine and glycine rich protein 2 (Csrp2), a member of the LIM-only family plays a key role in the response to vascular injury. However, its roles in vascular fibrosis and PH have not been clarified. Therefore, this study aimed to investigate whether Csrp2 can promote vascular fibrosis and to further explore the possible mechanisms. Csrp2 expression was increased in both the pulmonary vasculature of rats with PH and hypoxic pulmonary vascular smooth muscle cells (PASMCs). Hypoxia activated TGF-β1 and its downstream effector, SP1. Additionally, hypoxia activated the ROCK pathway and inhibited KLF4 expression. Silencing SP1 and overexpressing KLF4 reversed the hypoxia-induced increase in Csrp2 expression. Csrp2 knockdown decreased the expression of extracellular matrix (ECM) proteins and inhibited the nuclear translocation and expression of YAP/TAZ in hypoxic PASMCs. These results indicate that hypoxia induces Csrp2 expression through the TGF-β1/SP1 and ROCK/KLF4 pathways. Elevated Csrp2 promoted the nuclear translocation and expression of YAP/TAZ, leading to vascular fibrosis and the development of PH.

Ergosterol is an important precursor in the pharmaceutical industry for the production of numerous drugs. In this study, Kluyveromyces marxianus that showed more potential for ergosterol production than some other yeasts was reported. The effects of transcription factors UPC2, MOT3, and ROX1 of K. marxianus on ergosterol synthesis were explored, and a Upc2-overexpressing strain produced 1.78 times more ergosterol (167.33 mg/L) than the wild-type strain (60.04 mg/L). A total of 239.98 mg/L ergosterol was produced when glucose was replaced with fructose to limit ethanol production. Enhanced aeration increased ergosterol titer from 63.09 mg/L to 128.46 mg/L at 42 °C. The ergosterol titer reached 304.37 mg/L in a shake flask at 37 °C, or 1124.38 and 948.32 mg/L at 37 °C and 42 °C, respectively, in a 5 L bioreactor, using Jerusalem artichoke tubers as the sole carbon source. This study establishes a platform for ergosterol biosynthesis using inexpensive materials.

With the rapid development of artificial intelligence education, it is of great significance to establish a scientific and effective model of learners' knowledge mastery state. Knowledge tracking (KT) as a modeling method for learners to infer knowledge level based on their learning achievements, plays a key role in intelligent education system. There are some problems in the existing knowledge tracking models, such as incomplete input features and unstable model. In order to improve the accuracy of the prediction of learners' learning effect, this paper proposes a deep knowledge tracking model (DDKT) based on the comprehensive difficulty of the topic. The model mainly includes two parts: on the one hand, the construction of the comprehensive difficulty model, on the other hand, the improvement of the model. The model combines topic difficulty with deep knowledge tracking model, enriches learners' learning process dimensions and improves the generalization ability of the model. Through comparative experiments, it is proved that the DDKT model has achieved better results.

MOOC attracts students with its unique teaching mode and high-quality curriculum resources, but it also faces the problem of high dropout rate, which affects the long development of MOOC. In order to solve the problem of high dropout rate faced by MOOC platform, this paper proposes the method of combining deep learning and integrated learning to construct the prediction model of students' withdrawal behavior. The experimental data were collected from MOOCCube2020 dataset. The convolution neural network is used to extract hidden features from the original data, and the output features are used as the input of ensemble learning model. Then, various traditional classification methods are used for training and prediction, and the prediction results of various models are fused to obtain the final result. Experiments show that the model can well fit the correlation between students' learning performance and class quitting behavior, so as to accurately predict whether students will quit the course, which is helpful to the in-depth study of MOOC learning mode.

Background Recently, an increasing number of studies have revealed that N6-methyladenosine (m6A) functions as a significant post-transcriptional modification which plays a critical role in the occurrence and progression of enriched tumors by regulating mRNA and non-coding RNA biogenesis. However, the biological function of m6A in breast cancer remains largely unclear. Materials and Methods In this study, we used a serious of bioinformatic databases and tools to jointly analyze the expression of m6A methylation transferases (METTL13, METTL14, WTAP, RBM15, RBM15B and ZC3H13) and investigate the prognostic value of METTL14 and ZC3H13 in breast cancer. Besides, we analyzed the downstream carcinogenic molecular mechanisms related to METTL14 and ZC3H13, and their relationship with immune infiltration in breast tumor tissues. Results The results showed that METTL14 and ZC3H13 were the down-regulated m6A “writers” in breast cancer. Survival outcomes analysis suggested that abnormally low expression of METTL14 and ZC3H13 could predict unfavorable prognosis in four breast cancer subtypes. Moreover, the down-regulation of them was associated with ER-, PR- and basal-like, TNBC patients, as well as tumor progression (increased Scarff, Bloom and Richardson grade status and Nottingham Prognostic Index classification). Co-expression analysis revealed that METTL14 and ZC3H13 had a strong positive correlation with APC, an antagonist of the Wnt signaling pathway, indicating they might cooperate in regulating proliferation, invasion and metastasis of tumor cells. METTL14, ZC3H13 and APC expression levels had significant positive correlation with infiltrating levels of CD4 + T cells, CD8 + T cells, neutrophils, macrophages and Dendritic cells, and negatively with Treg cells in breast cancer. Conclusions This study demonstrated that down-regulation of METTL14 and ZC3H13 which act as two tumor suppressor genes was found in breast cancer and predicted poor prognosis. Their abnormal expression promoted breast cancer invasion by affecting pathways related to tumor progression and mediating immunosuppression.

China suffers frequent and severe floods that result in significant annual casualties and economic losses. However, as flood vulnerability and resilience varies in different regions and periods, it is vital that the temporal and spatial vulnerability and resilience characteristics are known to enable the government to make suitable arrangements for the distribution of relief funds and assist the regions to improve their disaster capabilities. Therefore, to reveal the relative vulnerability and resilience of the research object, this paper developed Chinese provincial scale flood vulnerability and resilience assessment models from an input-output perspective that accounted for socioeconomic, flood damage, flood driving and environmental factors. The Super-Efficiency Data Envelopment Analysis method was applied to the vulnerability assessment research and the Super-Efficiency Slacks-Based Measure Data Envelopment Analysis method was applied to the resilience assessment research. It was found that high vulnerability, high resilience provinces were mainly distributed in the eastern coastal areas, high vulnerability, low resilience provinces were mainly concentrated around the middle reaches of the Yangtze River, low vulnerability, high resilience provinces were mainly distributed in the western region, and low vulnerability, low resilience provinces were mainly in the southwest region. From 2012 to 2018, the relative flood vulnerability was “declining” in 36% of the provinces, even though they had been under pressure from rising GDP and population densities. It was also found that the annual precipitation as the driving factor had a significant impact on provincial flood vulnerabilities. The relative flood resilience from 2012 to 2018 was “rising-declining” in 39% of the provinces, had “almost no change” in 29%, and was rising in only 11%, which indicated that the relative flood resilience in many Chinese provinces still needs to be improved, with the recovery capacity of the power infrastructure in particular needing to be strengthened. Based on the spatiotemporal relative flood vulnerability and resilience variations in each province, some flood prevention and mitigation suggestions are given.

The combined effects of vanadium additions and in-situ TiC particle formation upon the microstructure and mechanical properties of FeCrNiCu high entropy alloys were investigated. The addition of V changed the FeCrNiCu matrix structure from FCC (i.e., face-centered cubic) to FCC+BCC (i.e., body-centered cubic). The as-cast 10 vol.%TiC/FeCrNiCuV0.1 high entropy alloy matrix composite showed excellent mechanical properties, with the yield strength and ultimate tensile strength determined to be 653.5 MPa and 1006.5 MPa, respectively. The extra strength generated by V addition and in-situ TiC particles in FeCrNiCu was evaluated, and the theoretical predictions match the experimental results well.

FeCrNiCu based high entropy alloy matrix composites were fabricated with addition of Si and C by vacuum electromagnetic induction melting. The primary goal of this research was to analyze the reaction mechanism, microstructure, mechanical properties at room temperature and strengthening mechanism of the composites with addition of Si and C. The reaction mechanism of powders containing (Si, Ni and C) was analyzed, only one reaction occurred (i.e., Si + C → SiC) and its activation energy is 1302.8 kJ/mol. The new composites consist of a face centered cubic (FCC) structured matrix reinforced by submicron sized SiC particles. The addition of Si and C enhances the hardness from 351.4 HV to 626.4 HV and the tensile strength from 565.5 MPa to 846.0 MPa, accompanied by a slight decrease in the plasticity. The main strengthening mechanisms of SiC/FeCrNiCu composites were discussed based on dislocation strengthening, load bearing effect, Orowan mechanism and solid solution hardening, whose contributions to the tensile strength increase are 58.6%, 6.3%, 14.3% and 20.8%, respectively.

Nb-doped CrMnFeCoNix (x = 0.2, 0.4, 0.6, 0.8 and 1) high entropy alloys were prepared by vacuum induction melting. The effects of Ni and Nb content on the microstructure evolution and tensile properties of the alloys were investigated. With increasing Ni content, the CrMnFeCoNix alloy changed its structure from a combination of face-centered cubic phase and σ phase to a single face-centered cubic phase. The yield strength, ultimate tensile strength and ductility of CrMnFeCoNix alloys first increased and then decreased with the increase of Ni content. Compared with other CrMnFeCoNix alloys, CrMnFeCoNi0.8 HEA has shown superior mechanical properties, exhibiting a high yield strength (240.9 MPa), ultimate tensile strength (527.5 MPa) and an impressive elongation-to-failure (62.5%). The addition of Nb significantly increased the volume fraction of Laves phase in the CrMnFeCoNi0.8 alloy, which further enhanced the tensile properties of CrMnFeCoNi0.8 high entropy alloys. The yield strength and ultimate tensile strength of the CrMnFeCoNi0.8Nb0.2 alloy were determined to be 406.9 MPa and 662.8 MPa, representing a 68.9% and 25.6% increase over CrMnFeCoNi0.8 alloy.

The high entropy alloy matrix composite FeCrNiCu on the basis of the face-centerd cubic structure and reinforced with 5 vol% NbTiC2 particles was synthesized by vacuum induction melting. NbTiC2 particles are found to be densely distributed on the substrate in polygonal shapes. The tensile strength of the NbTiC2/FeCrNiCu high entropy alloy matrix increased phenomenally, from 517.5 MPa to 1148.8 MPa. Though the ductility of composite is lower than the matrix, but it still presents an acceptable value of 7.1%. It is found that dislocation strengthening, loading bearing effect and Orowan mechanism are positive for enhancing the tensile properties of the high entropy alloy.

The accurate exploration of the sentiment information in comments for Massive Open Online Courses (MOOC) courses plays an important role in improving its curricular quality and promoting MOOC platform’s sustainable development. At present, most of the sentiment analyses of comments for MOOC courses are actually studies in the extensive sense, while relatively less attention is paid to such intensive issues as the polysemous word and the familiar word with an upgraded significance, which results in a low accuracy rate of the sentiment analysis model that is used to identify the genuine sentiment tendency of course comments. For this reason, this paper proposed an ALBERT-BiLSTM model for sentiment analysis of comments for MOOC courses. Firstly, ALBERT was used to dynamically generate word vectors. Secondly, the contextual feature vectors were obtained through BiLSTM pre-sequence and post-sequence, and the attention mechanism that could calculate the weight of different words in a sentence was applied together. Finally, the BiLSTM output vectors were input into Softmax for the classification of sentiments and prediction of the sentimental tendency. The experiment was performed based on the genuine data set of comments for MOOC courses. It was proved in the result that the proposed model was higher in accuracy rate than the already existing models.

A series of NbC/FeCrNiCu high entropy alloy matrix composites (2.5, 5, 7.5 and 10 vol% of NbC) were prepared by vacuum induction melting. The microstructures and mechanical properties of the composites were investigated using X-ray electron diffraction, scanning electron microscopy, transmission electron microscopy, tensile and hardness tests. The results show that the FeCrNiCu high entropy alloy matrix composites are composed of FCC solid solution embedded with NbC particles. A coherent relationship was identified between the alloy matrix and reinforcement phase. The formation of NbC particles increases the hardness from 337.6 HV to 591.3 HV. Moreover, compared with the base alloy, the tensile yield strength, ultimate tensile strength and ductility of the new composite first increases and then decreases with the increase of NbC content. The tensile yield strength, ultimate tensile strength and ductility reaches maximum for the composites containing 5 vol% of NbC, which is 458.2 MPa, 691.6 MPa and 24.7%, respectively. The strengthening mechanisms of the composites are discussed and the contributions of dislocation strengthening, load bearing effect and Orowan mechanism are quantified.

In the classification of toxic comment text, the minority classes are often surrounded by the majority classes. With classification through traditional methods, the accuracy of the majority class is high while that of the minority classes is low. Furthermore, traditional classification methods cannot detect the toxic comment on the minority classes. Therefore, a modified toxic behaviour detection model is proposed in this paper, which combined an improved SMOTE algorithm bi-LSTM, we named it AS-BL. In the first step, the dataset was pre-processed, and the features were extracted. Then, increased the minority classes in the toxic comment text via the improved SMOTE (AD-SMOTE) algorithm, using KNN to calculate the average sampling density and increase the number of minority comment samples from the data level. Finally, the text vector was introduced to the trained bi-LSTM model for detection. The results of the experiments showed that the model proposed in this paper outperformed the other existing models in the classification accuracy and improved the overall detection; therefore, the model is suitable for the actual network environment.

It remains a challenge to enhance strength without sacrificing ductility for high entropy alloys. Here, we prepared in-situ TiC/FeCrNiCuV0.1 high entropy alloy matrix composites using vacuum induction smelting. The new composites exhibit an excellent combination of high tensile strength (1006.5 MPa) and good ductility (15.6%). The improved mechanical properties, as compared to the V-free composites, are attributed to a synergy of solid solution strengthening and grain size refinement.

Many attempts on improving entity grid model have been made and boost the research on text coherence. However, only a few of them applied it to noisy data domain and students' essay. Thus, we proposed a novel discourse coherence model, which is based on entity grid model, to evaluate coherence on Chinese students' essay. In allusion to the feature of Chinese students' essay, frequently using lexical repetition and coreference methods in coherence essay, we designed a coreference module, instead of clustering algorithm or knowledge base search methods, to integrate with the enhanced coherence model. In addition, we fully merged coreference feature into similarity assessment of adjacent sentences, and the semantic coherence. Experiments show that our model outperforms entity-based model and LSA methods and has an ideal effect on students' essay automatic assessment.

In English writing, English learners will inevitably make a variety of grammatical mistakes, especially in English subordinate clause connective. To alleviate high error rate of connective in subordinate clauses of Chinese students' English writing, an automatic error correction model for English subordinate clause connective is studied and implemented from the perspective of machine learning — genetic algorithm (GA) and k-nearest neighbor (KNN) algorithm combination model. Firstly, an automatic feature selection algorithm based on GA is adopted to reduce time consuming and space cost, and to improve the accuracy of connective error correction. Secondly, through comparing the Naive Bayes, decision tree, maximum entropy and KNN algorithm, KNN algorithm is found better while classifying the connectives. Finally, we compared the performance of several hybrid models, which combine different machine learning algorithms with GA. This proves that the combination of GA and KNN algorithm is optimal.