Leaf photosynthetic function duration during yield formation of large-spike wheat in rainfed cropping systems

Improving photosynthetic capacity significantly affects the yield of wheat (Triticum aestivum L.) in rainfed regions. In this study, the physiological characteristics of eight large-spike wheat lines were compared with a multiple-spike cultivar as a control (CK) in a field over two consecutive seasons: 2010–2012. The tillering peak was 7–21 d after returning green for line 2040, the average rate of decline of relative water content was slower, and the average duration time of photosynthetic rate was longer than CK in vitro. There was a strong linear and positive correlation between photosynthetic rate and root activity at jointing, flowering, and grain-filling stages. In addition, average yields were higher in large-spike lines than CK (multiple-spike cultivar). The results suggest that large-spike lines might have greater water retaining capacity during yield formation under rainfed conditions

A Blow-Up Criterion for Classical Solutions to the Compressible Navier-Stokes Equations

In this paper, we obtain a blow up criterion for classical solutions to the 3-D compressible Naiver-Stokes equations just in terms of the gradient of the velocity, similar to the Beal-Kato-Majda criterion for the ideal incompressible flow. In addition, initial vacuum is allowed in our case.Comment: 25 page

Ultra-high pressure balloon angioplasty for pulmonary artery stenosis in children with congenital heart defects: Short- to mid-term follow-up results from a retrospective cohort in a single tertiary center

ObjectiveBalloon angioplasty (BA) has been the treatment of choice for pulmonary artery stenosis (PAS) in children. There remains, however, a significant proportion of resistant lesions. The ultra-high pressure (UHP) balloons might be effective in a subset of these lesions. In this study, we analyzed the safety and efficacy with short- to mid-term follow-up results of UHP BA for PAS in children with congenital heart defects (CHD) in our center.MethodsThis is a retrospective cohort study in a single tertiary heart center. Children diagnosed with PAS associated with CHD were referred for UHP BA. All data with these children were collected for analysis with updated follow-up.ResultsA total of 37 UHP BAs were performed consecutively in 28 children. The success rate was 78.4%. A significantly (P = 0.005) larger ratio of the balloon to the minimal luminal diameter at the stenotic waist (balloon/waist ratio) was present in the success group (median 3.00, 1.64–8.33) compared to that in the failure group (median 1.94, 1.41 ± 4.00). Stepwise logistic regression analysis further identified that the balloon/waist ratio and the presence of therapeutic tears were two independent predictors of procedural success. The receiver operating characteristic curve revealed a cut-off value of 2.57 for the balloon/waist ratio to best differentiate success from failure cases. Signs of therapeutic tears were present in eight cases, all of whom were in the success group. Perioperative acute adverse events were recorded in 16 patients, including 11 pulmonary artery injuries, three pulmonary hemorrhages, and two pulmonary artery aneurysms. During a median follow-up period of 10.4 (0.1–21.0) months, nine cases experienced restenosis at a median time of 40 (4–325) days after angioplasty.ConclusionsThe UHP BA is safe and effective for the primary treatment of PAS in infants and children with CHD. The success rate is high with a low incidence of severe complications. The predictors of success are a larger balloon/waist ratio and the presence of therapeutic tears. The occurrence of restenosis during follow-up, however, remains a problem. A larger number of cases and longer periods of follow-up are needed for further study

Evaluating a new method to estimate the rate of leaf respiration in the light by analysis of combined gas exchange and chlorophyll fluorescence measurements

Day respiration (Rd) is an important parameter in leaf ecophysiology. It is difficult to measure directly and is indirectly estimated from gas exchange (GE) measurements of the net photosynthetic rate (A), commonly using the Laisk method or the Kok method. Recently a new method was proposed to estimate Rd indirectly from combined GE and chlorophyll fluorescence (CF) measurements across a range of low irradiances. Here this method is tested for estimating Rd in five C3 and one C4 crop species. Values estimated by this new method agreed with those by the Laisk method for the C3 species. The Laisk method, however, is only valid for C3 species and requires measurements at very low CO2 levels. In contrast, the new method can be applied to both C3 and C4 plants and at any CO2 level. The Rd estimates by the new method were consistently somewhat higher than those by the Kok method, because using CF data corrects for errors due to any non-linearity between A and irradiance of the used data range. Like the Kok and Laisk methods, the new method is based on the assumption that Rd varies little with light intensity, which is still subject to debate. Theoretically, the new method, like the Kok method, works best for non-photorespiratory conditions. As CF information is required, data for the new method are usually collected using a small leaf chamber, whereas the Kok and Laisk methods use only GE data, allowing the use of a larger chamber to reduce the noise-to-signal ratio of GE measurements

Fundus Image Classification Using VGG-19 Architecture with PCA and SVD

Automated medical image analysis is an emerging field of research that identifies the disease with the help of imaging technology. Diabetic retinopathy (DR) is a retinal disease that is diagnosed in diabetic patients. Deep neural network (DNN) is widely used to classify diabetic retinopathy from fundus images collected from suspected persons. The proposed DR classification system achieves a symmetrically optimized solution through the combination of a Gaussian mixture model (GMM), visual geometry group network (VGGNet), singular value decomposition (SVD) and principle component analysis (PCA), and softmax, for region segmentation, high dimensional feature extraction, feature selection and fundus image classification, respectively. The experiments were performed using a standard KAGGLE dataset containing 35,126 images. The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time. Utilization of PCA and SVD feature selection with fully connected (FC) layers demonstrated the classification accuracies of 92.21%, 98.34%, 97.96%, and 98.13% for FC7-PCA, FC7-SVD, FC8-PCA, and FC8-SVD, respectively

Estimation of Solar Radiation for Tomato Water Requirement Calculation in Chinese-Style Solar Greenhouses Based on Least Mean Squares Filter

The area covered by Chinese-style solar greenhouses (CSGs) has been increasing rapidly. However, only a few pyranometers, which are fundamental for solar radiation sensing, have been installed inside CSGs. The lack of solar radiation sensing will bring negative effects in greenhouse cultivation such as over irrigation or under irrigation, and unnecessary power consumption. We aim to provide accurate and low-cost solar radiation estimation methods that are urgently needed. In this paper, a method of estimation of solar radiation inside CSGs based on a least mean squares (LMS) filter is proposed. The water required for tomato growth was also calculated based on the estimated solar radiation. Then, we compared the accuracy of this method to methods based on knowledge of astronomy and geometry for both solar radiation estimation and tomato water requirement. The results showed that the fitting function of estimation data based on the LMS filter and data collected from sensors inside the greenhouse was y = 0.7634x + 50.58, with the evaluation parameters of R2 = 0.8384, rRMSE = 23.1%, RMSE = 37.6 Wm−2, and MAE = 25.4 Wm−2. The fitting function of the water requirement calculated according to the proposed method and data collected from sensors inside the greenhouse was y = 0.8550x + 99.10 with the evaluation parameters of R2 = 0.9123, rRMSE = 8.8%, RMSE = 40.4 mL plant−1, and MAE = 31.5 mL plant−1. The results also indicate that this method is more effective. Additionally, its accuracy decreases as cloud cover increases. The performance is due to the LMS filter’s low pass characteristic that smooth the fluctuations. Furthermore, the LMS filter can be easily implemented on low cost processors. Therefore, the adoption of the proposed method is useful to improve the solar radiation sensing in CSGs with more accuracy and less expense

Prediction of abovegroundgrassland biomass on the LoessPlateau, China, using a randomforest algorithm

Grasslands are an important component of terrestrial ecosystems that play a crucial role in the carbon cycle and climate change. In this study, we collected aboveground biomass (AGB) data from 223 grassland quadrats distributed across the Loess Plateau from 2011 to 2013 and predicted the spatial distribution of the grassland AGB at a 100-m resolution from both meteorological station and remote sensing data (TM and MODIS) using a Random Forest (RF) algorithm. The results showed that the predicted grassland AGB on the Loess Plateau decreased from east to west. Vegetation indexes were positively correlated with grassland AGB, and the normalized difference vegetation index (NDVI) acquired from TM data was the most important predictive factor. Tussock and shrub tussock had the highest AGB, and desert steppe had the lowest. Rainfall higher than 400 m might have benefitted the grassland AGB. Compared with those obtained for the bagging, mboost and the support vector machine (SVM) models, higher values for the mean Pearson coefficient (R) and the symmetric index of agreement (λ) were obtained for the RF model, indicating that this RF model could reasonably estimate the grassland AGB (65.01%) on the Loess Plateau

Utilization of Surplus Air Thermal Energy by a Water Cycle System in a Chinese-Type Solar Greenhouse

Solar greenhouses are commonly overheated during the day, and the remaining air heat can only be dissipated through ventilation, which is a severe energy waste problem. In order to improve the energy utilization of the greenhouse, this study proposes a water cycle system using surplus air thermal energy, which consists of an air-water heat exchanger, supply and return pipes, a submersible pump, a water tank, and an automatic control system. The proposed system stores the surplus air thermal energy in the greenhouse in the water tank. It releases it into the greenhouse using water circulation, and experimental analyses were carried out using a solar greenhouse in the Shenyang area. The effects of different air and water flow rates on the performance of the surplus air thermal energy water recycling system and the environment inside the greenhouse were analyzed by establishing a CFD model and model validation, and the average difference between the experimental data and the simulated data was 6.98%. The results show that the circulating air flow rate significantly affects the system performance and the environment inside the greenhouse. In the heat collection stage, the water circulation system with an airflow rate of 9 m/s has a minor average temperature difference in the vertical plane of the greenhouse. The water circulation system with an airflow rate of 6.0 m/s collects and releases the most significant heat. The temperature cloud between the vertical and horizontal planes is more uniform. This research provides new ideas for efficient energy use in solar greenhouses

The Effects of Rice Straw and Biochar Applications on the Microbial Community in a Soil with a History of Continuous Tomato Planting History

Soil microbial abundance and diversity change constantly in continuous cropping systems, resulting in the prevalence of soil-borne pathogens and a decline in crop yield in solar greenhouses. To investigate the effects of rice straw and biochar on soil microbial abundance and diversity in soils with a history of continuous planting, three treatments were examined: mixed rice straw and biochar addition (RC), rice straw addition (R), and biochar addition (C). The amount of C added in each treatment group was 3.78 g kg−1 soil. Soil without rice straw and biochar addition was treated as a control (CK). Results showed that RC treatment significantly increased soil pH, available nitrogen (AN), available phosphorus (AP), and potassium (AK) by 40.3%, 157.2%, and 24.2%, respectively, as compared to the CK soil. The amount of soil labile organic carbon (LOC), including readily oxidizable organic carbon (ROC), dissolved organic carbon (DOC), and light fraction organic carbon (LFOC), was significantly greater in the RC, R, and C treatment groups as compared to CK soil. LOC levels with RC treatment were higher than with the other treatments. Both rice straw and biochar addition significantly increased bacterial and total microbial abundance, whereas rice straw but not biochar addition improved soil microbial carbon metabolism and diversity. Thus, the significant effects of rice straw and biochar on soil microbial carbon metabolism and diversity were attributed to the quantity of DOC in the treatments. Therefore, our results indicated that soil microbial diversity is directly associated with DOC. Based on the results of this study, mixed rice straw and biochar addition, rather than their application individually, might be key to restoring degraded soil