From a Chinese kindergarten: A personal journey

How do Chinese kindergarten teachers understand their teaching practices? How are these influenced by cultural, political and economic forces? What do their classrooms look like from the perspective of a Chinese New Zealander

Water Pollution Prediction in the Three Gorges Reservoir Area and Countermeasures for Sustainable Development of the Water Environment

The Three Gorges Project was implemented in 1994 to promote sustainable water resource use and development of the water environment in the Three Gorges Reservoir Area (hereafter “Reservoir Area”). However, massive discharge of wastewater along the river threatens these goals; therefore, this study employs a grey prediction model (GM) to predict the annual emissions of primary pollution sources, including industrial wastewater, domestic wastewater, and oily and domestic wastewater from ships, that influence the Three Gorges Reservoir Area water environment. First, we optimize the initial values of a traditional GM (1,1) model, and build a new GM (1,1) model that minimizes the sum of squares of the relative simulation errors. Second, we use the new GM (1,1) model to simulate historical annual emissions data for the four pollution sources and thereby test the effectiveness of the model. Third, we predict the annual emissions of the four pollution sources in the Three Gorges Reservoir Area for a future period. The prediction results reveal the annual emission trends for the major wastewater types, and indicate the primary sources of water pollution in the Three Gorges Reservoir Area. Based on our predictions, we suggest several countermeasures against water pollution and towards the sustainable development of the water environment in the Three Gorges Reservoir Area

Generating a Long-Term Spatiotemporally Continuous Melt Pond Fraction Dataset for Arctic Sea Ice Using an Artificial Neural Network and a Statistical-Based Temporal Filter

The melt pond fraction (MPF) is an important geophysical parameter of climate and the surface energy budget, and many MPF datasets have been generated from satellite observations. However, the reliability of these datasets suffers from short temporal spans and data gaps. To improve the temporal span and spatiotemporal continuity, we generated a long-term spatiotemporally continuous MPF dataset for Arctic sea ice, which is called the Northeast Normal University-melt pond fraction (NENU-MPF), from Moderate Resolution Imaging Spectroradiometer (MODIS) data. First, the non-linear relationship between the MODIS reflectance/geometries and the MPF was constructed using a genetic algorithm optimized back-propagation neural network (GA-BPNN) model. Then, the data gaps were filled and smoothed using a statistical-based temporal filter. The results show that the GA-BPNN model can provide accurate estimations of the MPF (R2 = 0.76, root mean square error (RMSE) = 0.05) and that the data gaps can be efficiently filled by the statistical-based temporal filter (RMSE = 0.047; bias = −0.022). The newly generated NENU-MPF dataset is consistent with the validation data and with published MPF datasets. Moreover, it has a longer temporal span and is much more spatiotemporally continuous; thus, it improves our knowledge of the long-term dynamics of the MPF over Arctic sea ice surfaces

Blockchain-enabled asynchronous federated learning in edge computing

The fast proliferation of edge computing devices brings an increasing growth of data, which directly promotes machine learning (ML) technology development. However, privacy issues during data collection for ML tasks raise extensive concerns. To solve this issue, synchronous federated learning (FL) is proposed, which enables the central servers and end devices to maintain the same ML models by only exchanging model parameters. However, the diversity of computing power and data sizes leads to a significant difference in local training data consumption, and thereby causes the inefficiency of FL. Besides, the centralized processing of FL is vulnerable to single-point failure and poisoning attacks. Motivated by this, we propose an innovative method, federated learning with asynchronous convergence (FedAC) considering a staleness coefficient, while using a blockchain network instead of the classic central server to aggregate the global model. It avoids real-world issues such as interruption by abnormal local device training failure, dedicated attacks, etc. By comparing with the baseline models, we implement the proposed method on a real-world dataset, MNIST, and achieve accuracy rates of 98.96% and 95.84% in both horizontal and vertical FL modes, respectively. Extensive evaluation results show that FedAC outperforms most existing models

The Optimization of Extraction Process, Antioxidant, Whitening and Antibacterial Effects of Fengdan Peony Flavonoids

Flavonoids have important biological activities, such as anti-inflammatory, antibacterial, antioxidant and whitening, which is a potential functional food raw material. However, the biological activity of Fengdan peony flavonoid is not particularly clear. Therefore, in this study, the peony flavonoid was extracted from Fengdan peony seed meal, and the antioxidant, antibacterial and whitening activities of the peony flavonoid were explored. The optimal extraction conditions were methanol concentration of 90%, solid-to-liquid ratio of 1:35 g:mL, temperature of 55 °C and time of 80 min; under these conditions, the yield of Fengdan peony flavonoid could reach 1.205 ± 0.019% (the ratio of the dry mass of rutin to the dry mass of peony seed meal). The clearance of Fengdan peony total flavonoids to 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical, hydroxyl radical and 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) free radical could reach 75%, 70% and 97%, respectively. Fengdan peony flavonoid could inhibit the growth of the Gram-positive bacteria. The minimal inhibitory concentrations (MICs) of Fengdan peony flavonoid on S. aureus, B. anthracis, B. subtilis and C. perfringens were 0.0293 mg/mL, 0.1172 mg/mL, 0.2344 mg/mL and 7.500 mg/mL, respectively. The inhibition rate of Fengdan peony flavonoid on tyrosinase was 8.53–81.08%. This study intensely illustrated that the antioxidant, whitening and antibacterial activity of Fengdan peony total flavonoids were significant. Fengdan peony total flavonoids have a great possibility of being used as functional food materials

Estimation of Organic Pollution of the Jinyaoshi Lake Based on the Diatom Assemblage of the Lake Sediments

From 2015 to 2019, samples of surface mud from the bottom of the artificial lake in Tianjin Normal University were collected for diatom analysis and identification in May and June. 1296 diatoms were identified and divided into 25 species. DAIpo (Diatom Assemblage Index to organic water pollution) was calculated: 56 in 2015, 76 in 2017, 58 in 2018 and 63 in 2019. In 2017, it was β-oligosaprobic, and in 2015, 2018 and 2019, it belonged to α-oligosaprobic. The Jinyaoshi Lake is an artificial lake where groundwater is exposed by artificial excavation, and its water source mainly comes from groundwater, then by precipitation. It shows that the pollution degree of the Jinyaoshi Lake was not serious since 2015, and its pollution level was α or β-oligosaprobic

MiR-146a regulates SOD2 expression in H2O2 stimulated PC12 cells.

SOD2 (superoxide dismutase 2) is one of the endogenous antioxidant enzymes that protect against reactive oxygen species. While explorations of SOD2 expression regulation are mainly focused on transcriptional and post-translational activation, there are few reports about the post-transcriptional regulation of SOD2. MicroRNAs (miRNAs) are 21nt-25nt (nucleotide) small noncoding RNAs that have emerged as indispensable regulators of gene expression. Here we show that miR-146a, a widely expressed miRNA, is up-regulated by H2O2-induced stress. By sequence analysis we found a binding site for miR-146a in the sod2 mRNA 3'UTR, and a luciferase reporter assay confirmed that miR-146a can interact with this sod2 regulatory region. Our results further show that miR-146a could down-regulate the SOD2 protein expression, and antisense-miR-146a could reverse the decrease of both the SOD2 level and cell viability in H2O2 treated PC12 cells. In conclusion, here we have identified a novel function of miR-146a in the post-transcriptional regulation of SOD2 expression

Expression profiles and functional annotation analysis of mRNAs in suprachiasmatic nucleus of Clock mutant mice

The core circadian clock gene, Clock, is a positive component of the transcription/translation feedback loop in the master pacemaker suprachiasmatic nucleus (SCN) in mammals. The robust daytime peak of some clock genes in the wild-type SCN is absent in Clock mutant mice. However, very little is known about the impact of Clock mutation on the expression of other functional genes in SCN. Here, we performed cDNA microarray and found 799 differentially expressed genes (DEGs) at zeitgeber time 2 (ZT2) and 1289 DEGs at ZT14 in SCN of Clock(Delta 19/Delta 19) mutant mice. KEGG pathway analysis showed that the changed mRNAs were highly associated with hedgehog signaling pathway, retinol metabolism, allograft rejection, drug metabolism, hematopoietic cell lineage and neuroactive ligand-receptor interaction. The top 14 and 71 hub genes were identified from the protein protein interaction (PPI) network at ZT2 and ZT14, respectively. The sub-networks revealed hub genes were involved in olfactory transduction and neuroactive ligand-receptor interaction pathways. These results demonstrate the Clock(Delta 19/Delta 19) mutation alters the expression of various genes involved in a wide spectrum of biological function in mouse SCN, which are helpful for better understanding the function of Clock and potential regulatory mechanisms.</p