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Rapid, accurate extraction of terraces from high-resolution images is of great significance for promoting the application of remote-sensing information in soil and water conservation planning and monitoring. To solve the problem of how deep learning requires a large number of labeled samples to achieve good accuracy, this article proposes an automatic identification method for terraces that can obtain high precision through small sample datasets. Firstly, a terrace identification source model adapted to multiple data sources is trained based on the WorldView-1 dataset. The model can be migrated to other types of images for terracing extraction as a pre-trained model. Secondly, to solve the small sample problem, a deep transfer learning method for accurate pixel-level extraction of high-resolution remote-sensing image terraces is proposed. Finally, to solve the problem of insufficient boundary information and splicing traces during prediction, a strategy of ignoring edges is proposed, and a prediction model is constructed to further improve the accuracy of terrace identification. In this paper, three regions outside the sample area are randomly selected, and the OA, F1 score, and MIoU averages reach 93.12%, 91.40%, and 89.90%, respectively. The experimental results show that this method, based on deep transfer learning, can accurately extract terraced field surfaces and segment terraced field boundaries. [ABSTRACT FROM AUTHOR]

Pyrroloquinoline quinone (PQQ), an essential nutrient, antioxidant, redox modulator and nerve growth factor found in a class of enzymes called quinoproteins, was labeled with Tc by using stannous fluoride (SnF) method. Radiolabeling qualification, quality control and characterization of Tc-PQQ and its biodistribution studies in mice were performed and discussed. Effects of pH values, temperature, time and reducing agents concentration on the radiolabeling yield were investigated. The quality control procedure of Tc-PQQ was determined by thin layer chromatography (TLC), radio high-performance liquid chromatography (RHPLC) and paper electrophoresis methods. The average radiolabeling yield was 94 ± 1% under optimum conditions of 0.99 mg of PQQ, 30 μg of SnF, 0.5 mg of ethylenediaminetetraacetic acid disodium salt (EDTA-2Na) and 18.5 MBq of NaTcO at pH 6 and 25 °C with a response volume of 1 ± 0.1 mL. Tc-PQQ was stable and anionic. Lipid-water partition coefficient of Tc-PQQ was −1.49 ± 0.16. The pharmacokinetics parameters of Tc-PQQ were t = 18.16 min, t = 100.45 min, K = 0.013 min, K = 0.017 min, K = 0.016 min, AUC (area under the curve) = 1040.78 ID% g min and CL (plasma clearance) = 0.096 mL min. The dual-exponential equation was Y = 10.88e + 5.21e. The biodistribution of Tc-PQQ was studied in ICR (Institute for Cancer Research 7701 Burhelme Are., Fox Chase, Philadelphia, PA 1911 USA) mice. In vitro autoradiographic studies clearly showed that the Tc-PQQ radioactivity accumulated predominantly in the hippocampus and cortex, which had a high density of N-methyl- d-aspartate Receptor (NMDAR). The enrichment can be blocked by NMDAR redox modulatory site antagonists-ebselen (EB) and Tc-PQQ is therefore a promising candidate for the molecular imaging of NMDAR. To date, however, there have been no studies characterizing Tc-PQQ. [ABSTRACT FROM AUTHOR]

The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic aperture radar (SAR) images to monitor marine oil spills can help control the spread of oil spill pollution over time and reduce the economic losses and environmental pollution caused by such spills. However, it is a significant challenge to distinguish between oil-spilled areas and oil-spill-like in SAR images. Semantic segmentation models based on deep learning have been used in this field to address this issue. In addition, this study is dedicated to improving the accuracy of the U-Shape Network (UNet) model in identifying oil spill areas and oil-spill-like areas and alleviating the overfitting problem of the model; a feature merge network (FMNet) is proposed for image segmentation. The global features of SAR image, which are high-frequency component in the frequency domain and represents the boundary between categories, are obtained by a threshold segmentation method. This can weaken the impact of spot noise in SAR image. Then high-dimensional features are extracted from the threshold segmentation results using convolution operation. These features are superimposed with to the down sampling and combined with the high-dimensional features of original image. The proposed model obtains more features, which allows the model to make more accurate decisions. The overall accuracy of the proposed method increased by 1.82% and reached 61.90% compared with the UNet. The recognition accuracy of oil spill areas and oil-spill-like areas increased by approximately 3% and reached 56.33%. The method proposed in this paper not only improves the recognition accuracy of the original model, but also alleviates the overfitting problem of the original model and provides a more effective monitoring method for marine oil spill monitoring. More importantly, the proposed method provides a design principle that opens up new development ideas for the optimization of other deep learning network models. [ABSTRACT FROM AUTHOR]

Owing to the importance of coalbed methane (CBM) as a source of energy, it is necessary to predict its future production. However, the production process of CBM is the result of the interaction of many factors, making it difficult to perform accurate simulations through mathematical models. We must therefore rely on the historical data of CBM production to understand its inherent features and predict its future performance. The objective of this paper is to establish a deep learning prediction method for coalbed methane production without considering complex geological factors. In this paper, we propose a multivariate long short-term memory neural network (M-LSTM NN) model to predict CBM production. We tested the performance of this model using the production data of CBM wells in the Panhe Demonstration Area in the Qinshui Basin of China. The production of different CBM wells has similar characteristics in time. We can use the symmetric similarity of the data to transfer the model to the production forecasting of different CBM wells. Our results demonstrate that the M-LSTM NN model, utilizing the historical yield data of CBM as well as other auxiliary information such as casing pressures, water production levels, and bottom hole temperatures (including the highest and lowest temperatures), can predict CBM production successfully while obtaining a mean absolute percentage error (MAPE) of 0.91%. This is an improvement when compared with the traditional LSTM NN model, which has an MAPE of 1.14%. In addition to this, we conducted multi-step predictions at a daily and monthly scale and obtained similar results. It should be noted that with an increase in time lag, the prediction performance became less accurate. At the daily level, the MAPE value increased from 0.24% to 2.09% over 10 successive days. The predictions on the monthly scale also saw an increase in the MAPE value from 2.68% to 5.95% over three months. This tendency suggests that long-term forecasts are more difficult than short-term ones, and more historical data are required to produce more accurate results. [ABSTRACT FROM AUTHOR]

Accurately forecasting the daily production of coalbed methane (CBM) is important forformulating associated drainage parameters and evaluating the economic benefit of CBM mining. Daily production of CBM depends on many factors, making it difficult to predict using conventional mathematical models. Because traditional methods do not reflect the long-term time series characteristics of CBM production, this study first used a long short-term memory neural network (LSTM) and transfer learning (TL) method for time series forecasting of CBM daily production. Based on the LSTM model, we introduced the idea of transfer learning and proposed a Transfer-LSTM (T-LSTM) CBM production forecasting model. This approach first uses a large amount of data similar to the target to pretrain the weights of the LSTM network, then uses transfer learning to fine-tune LSTM network parameters a second time, so as to obtain the final T-LSTM model. Experiments were carried out using daily CBM production data for the Panhe Demonstration Zone at southern Qinshui basin in China. Based on the results, the idea of transfer learning can solve the problem of insufficient samples during LSTM training. Prediction results for wells that entered the stable period earlier were more accurate, whereas results for types with unstable production in the early stage require further exploration. Because CBM wells daily production data have symmetrical similarities, which can provide a reference for the prediction of other wells, so our proposed T-LSTM network can achieve good results for the production forecast and can provide guidance for forecasting production of CBM wells. [ABSTRACT FROM AUTHOR]

Dehydroepiandrosterone (DHEA), a steroid hormone that is secreted by the adrenal cortex in mammals, has an array of biological actions, including inhibition of fat synthesis, decreasing the number of adipocytes, and a reduction in mammalian metabolic efficiency. Recent studies showed that DHEA may decrease fat deposition in poultry, but the mechanism of action is unclear. In the present study, we demonstrate that DHEA stimulates intracellular cyclic adenosine 3',5'-monophosphate (cAMP) accumulation in chicken hepatocytes during a 30 min incubation period. Increases in intracellular cAMP are evoked by as low as 0.1 microm-DHEA. The cAMP induced by DHEA, while suppressing cAMP-specific phosphodiesterase activity, also activates cAMP-dependent protein kinase A (PKA) in chicken hepatocytes. In addition, the activation of PKA leads to down-regulation of sterol regulatory element-binding protein-1 (SREBP-1). These findings demonstrate that direct action by DHEA leads to activation of the cAMP/PKA signalling system in the modulation of lipid metabolism by repressing SREBP-1, thereby providing a novel explanation for some of the underlying effects proposed for DHEA in the prevention of fat deposition in poultry.