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The purpose of this report is to develop a model that gives the best daily trading strategy based only on the price data before the day. We want to provide traders with strategies that will enable them to maximize their returns.we first utilize the LSTM time series neural network model using the first 956 data sets are used for training , and the subsequent 300 test sets are used to evaluate the model accuracy, calculate the prediction error and word comparison plots, and assess the difference between the prediction performance of the model and the actual value,This method has a better credibility. Finally, based on this model, we construct the optimal trading strategy model with the maximum value of total assets held as the objective function, add constraints, and finally arrive at an investment value of $1000.

Papillary thyroid cancer (PTC) is the most common endocrine system tumor. FOXK2 is involved in the development of different types of cancers, however, its function has not been investigated in papillary thyroid cancer. In the present study, we demonstrated that FOXK2 expression was up-regulated in papillary thyroid carcinoma tissues compared with matched normal tissues. Importantly, we found that FOXK2 expression was significantly associated with the tumor size, T stage, and TNM stage. Furthermore, stable knockdown of FOXK2 markedly inhibited PTC cell proliferation, significantly increased the ratio of LC3-II/LC3-I, and reduced p62 expression, whereas overexpression of FOXK2 showed opposite effects. In FOXK2 knockdown cell lines, mCherry-GFP-LC3 immunofluorescence demonstrated increased punctate aggregates of mCherry-GFP-LC3, and transmission electron microscopy revealed increased numbers of autophagosomes. Autophagy-related protein ULK1, VPS34, and FOXO3 were markedly up-regulated by FOXK2 knockdown and down-regulated by FOXK2 overexpression. Finally, autophagy inhibitor 3-MA attenuated autophagy activation and rescued the inhibition of cell proliferation caused by FOXK2 knockdown, suggesting that FOXK2 silencing inhibits cell proliferation through up-regulating autophagy. These findings revealed an important role of FOXK2 in PTC progression and suggested that FOXK2 might be a potential new target for the diagnosis and treatment of PTC.

Spatiotemporally continuous long-term leaf area index (LAI) products are urgently needed to monitor environmental changes. The current filter- or curve-fitting-based time series reconstructive algorithms fail to reconstruct the LAI time series with many continuous missing values or missing values in key phenological periods, which are common issues in high-spatial-resolution LAI time series. In this article, a meteorological data-driven backpropagation neural network (MBPNN) was proposed to reconstruct discontinuous LAI profiles with a two-step process using vegetation phenological information. As the basis of the strong dependence of vegetation growth on meteorological conditions, a reasonable growth trajectory of reconstructed LAI can be guaranteed by the algorithm even though if many observed values are missing. Validations for reconstructed LAI were conducted both spatially and temporally based on reference maps and field-measured long-term observations. The results showed that the LAI predicted by the MBPNN had a similar accuracy (RMSE = 0.4076) as the Landsat LAI inversions (RMSE = 0.4083) and a similar reconstructed trajectory as the field-measured LAI series even though over 100 days of continuous data were missing (RMSE = 0.1620). A comparison with the Harmonic ANalysis of Time Series (HANTS) algorithm showed that the accuracy of MBPNN was more stable regardless of the size/position of the missing data, and the proposed method performed much better when the data were continuously missing for 50 days or more.