In order to enhance the hybrid teaching of physical education online and offline, a quality evaluation method for online and offline hybrid teaching based on mobile edge computing is proposed. ...Through the evaluation target, index, weight of index and evaluation standard, this paper first constructs the quality evaluation index system of hybrid physical education online and offline. Then, factor and cluster analysis on the importance evaluation grade data of each index to simplify the index items are carried out. By combined with the mobile edge calculation, the weights of quality evaluation indexes for hybrid teaching are calculated respectively, and the fuzzy comprehensive evaluation model is used to effectively realize the quality evaluation of hybrid physical education online and offline. The simulation results show that the proposed method can effectively reduce the cost and error of quality evaluation for hybrid teaching online and offline, improve the evaluation efficiency, and enhance the quality of online and offline hybrid teaching.
As one of the most important transportation in international logistics, marine transportation generate vast amount of data. At present, we have been able to collect ship navigation data from many ...approaches such as AIS data. And extracting accurate and valuable potential information from these data for further application is a new and important topic in related areas. In this paper, AIS data is selected as the experimental data and a Region of Interest mining algorithm based on historical AIS trajectory data is proposed. Unlike the clustering based approaches, this algorithm locates the ROI from location-time data in AIS history trajectories, which can detect those ROI where ships didn't stay long, but visited frequently. Experiments and analysis on real AIS data shows that the proposed approach is effective and correct.
Hypoxia-inducible factors (HIFs) mediate metabolic reprogramming in response to hypoxia. However, the role of HIFs in branched-chain amino acid (BCAA) metabolism remains unknown. Here we show that ...hypoxia upregulates mRNA and protein levels of the BCAA transporter LAT1 and the BCAA metabolic enzyme BCAT1, but not their paralogs LAT2-4 and BCAT2, in human glioblastoma (GBM) cell lines as well as primary GBM cells. Hypoxia-induced LAT1 protein upregulation is mediated by both HIF-1 and HIF-2 in GBM cells. Although both HIF-1α and HIF-2α directly bind to the hypoxia response element at the first intron of the human
BCAT1
gene, HIF-1α is exclusively responsible for hypoxia-induced BCAT1 expression in GBM cells. Knockout of HIF-1α and HIF-2α significantly reduces glutamate labeling from BCAAs in GBM cells under hypoxia, which provides functional evidence for HIF-mediated reprogramming of BCAA metabolism. Genetic or pharmacological inhibition of BCAT1 inhibits GBM cell growth under hypoxia. Together, these findings uncover a previously unrecognized HIF-dependent metabolic pathway that increases GBM cell growth under conditions of hypoxic stress.
Trajectory classification is a hot topic in the field of spatiotemporal data mining. Existing models exert spatial or temporal computation on trajectory data, which require huge efforts and are often ...time consuming and lack of efficiency. This article proposes a model to classify unknown ship trajectories through a syntax recognition approach. By using the background semantic information in the rasterized sea chart, the model transforms the ship trajectories into symbolic sentences containing both spatiotemporal and semantic information, and reduces their scale. The class feature is expressed as a context-free grammar and the data classification is implemented through syntax parsing. The parsing requires less computation and is more efficient. Experiments are carried out to verify the model's practicability, and the results show that it is valid and effective.
The evolution of increased competitive ability (EICA) hypothesis and the novel weapons hypothesis (NWH) are two non-mutually exclusive mechanisms for exotic plant invasions, but few studies have ...simultaneously tested these hypotheses. Here we aimed to integrate them in the context of Chromolaena odorata invasion.
We conducted two common garden experiments in order to test the EICA hypothesis, and two laboratory experiments in order to test the NWH.
In common conditions, C. odorata plants from the nonnative range were better competitors but not larger than plants from the native range, either with or without the experimental manipulation of consumers. Chromolaena odorata plants from the nonnative range were more poorly defended against aboveground herbivores but better defended against soilborne enemies. Chromolaena odorata plants from the nonnative range produced more odoratin (Eupatorium) (a unique compound of C. odorata with both allelopathic and defensive activities) and elicited stronger allelopathic effects on species native to China, the nonnative range of the invader, than on natives of Mexico, the native range of the invader.
Our results suggest that invasive plants may evolve increased competitive ability after being introduced by increasing the production of novel allelochemicals, potentially in response to naïve competitors and new enemy regimes.
Background:
Long noncoding RNAs (lncRNAs) are a new class of cancer regulators. Here, we aimed to investigate the diagnostic and therapeutic values of an lncRNA, differentiation antagonizing ...noncoding RNA (DANCR), in lung cancer.
Methods:
Real-time polymerase chain reaction was used to compare DANCR levels in normal and cancerous lung tissues as well as lung cancer cells. Lentiviral transduction was used to induce DANCR overexpression or silencing in vitro, followed by monitoring cell proliferation, colony formation, and changes in microRNA-216a (miR-216a) expression. DANCR-specific small hairpin RNA transduction was used to establish cells with stable DANCR knockdown, and silenced cells were used to initiate lung tumor xenografts, followed by monitoring tumor growth.
Results:
DANCR upregulation was seen in lung cancer, particularly in high-grade lung cancer tissues and aggressive cancer cells. Ectopic DANCR expression induced lung cancer cell proliferation and colony formation, whereas DANCR silencing induced opposing effects. The miR-216a level in cancer cells was negatively correlated with DANCR expression. The DANCR knockdown reduced the growth of tumor xenografts in vivo.
Conclusion:
DANCR upregulation is a potential indicator of aggressive lung cancer. Silencing of DANCR has great potential as a potent therapeutic strategy in lung cancer.
This work develops a site‐specific duplexed luminescence resonance energy transfer system on cell surface for simultaneous imaging of two kinds of monosaccharides on a specific protein by single ...near‐infrared excitation. The single excitation‐duplexed imaging system utilizes aptamer modified upconversion luminescent nanoparticles as an energy donor to target the protein, and two fluorescent dye acceptors to tag two kinds of cell surface monosaccharides by a dual metabolic labeling technique. Upon excitation at 980 nm, only the dyes linked to protein‐specific glycans can be lit up by the donor by two parallel energy transfer processes, for in situ duplexed imaging of glycoforms on specific protein. Using MUC1 as the model, this strategy can visualize distinct glycoforms of MUC1 on various cell types and quantitatively track terminal monosaccharide pattern. This approach provides a versatile platform for profiling protein‐specific glycoforms, thus contributing to the study of the regulation mechanisms of protein functions by glycosylation.
Using upconversion luminescent nanoparticles (UNPs) as a polychromatic emission donor, a single‐excited duplexed luminescence resonance energy transfer (D‐LRET) system was developed on a cell surface for in situ duplexed imaging and relative quantification of two kinds of sugars on a specific protein using mucin 1 (MUC1) as the model. The strategy also enables monitoring of the expression of the fucosylated and sialylated terminal on MUC1.
•A novel discrete grey polynomial model is proposed.•The proposed model unifies the univariate discrete grey models.•An algorithm is presented to select the optimal model structure adaptively.•Matrix ...decomposition technique is adopted to provide a simpler paradigm for property analysis.
Grey models have been reported to be promising for time series prediction with small samples, but the diversity kinds of model structures and modelling assumptions restrains their further applications and developments. In this paper, a novel grey prediction model, named discrete grey polynomial model, is proposed to unify a family of univariate discrete grey models. The proposed model has the capacity to represent most popular homogeneous and non-homogeneous discrete grey models and furthermore, it can induce some other novel models, thereby highlighting the relationship between the models and their structures and assumptions. Based on the proposed model, a data-based algorithm is put forward to select the model structure adaptively. It reduces the requirement for modeler’s knowledge from an expert system perspective. Two numerical experiments with large-scale simulations are conducted and the results show its effectiveness. In the end, two real case tests show that the proposed model benefits from its adaptive structure and produces reliable multi-step ahead predictions.
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