The feasibility of obtaining and keeping stable nitrite accumulation in Sequencing Batch Reactors (SBRs) treating domestic wastewater is studied. The final product of ammonium oxidation is either reproducible nitrate or nitrite depending on the aeration strategy. With the aerobic-anoxic sequence, two SBRs fed with domestic wastewater are operated in parallel. One SBR (SBR1) is controlled by the aeration control strategy, and the other SBR (SBR2) by alternate aeration control strategy. Based on the on-line indirect measurements of DO and pH, the relationship between pH (or DO) and nitrogen concentration (NH4+-N, NO3−-N and NO2−-N) is investigated. The result indicates that pH and DO can be used as control parameters for the real-time aeration control strategy to obtain nitritation in SBR treating domestic wastewater. The result of SBR1 indicates that long-term stable nitritation is possible at 32 ± 1°C. The result of SBR2 indicates that the aeration control strategy is necessary for nitritation during the acclimation period, because the nitrite accumulation disappears when the aeration is extended.
A new mathematical model for a flexible blade coater is proposed and analysed for slip and magnetohydrodynamic (MHD) effects in blade coating process. The slip is considered at the blade surface and magnetic field is imposed normal to the flow. To obtain the velocity profile, pressure, pressure gradient, volumetric flow rate and maximum pressure both exact and numerical solutions are utilized. In order to obtain the numerical solution shooting technique is applied. The interesting physical quantities like load and deflection are calculated and presented in graphical and tabulated form. The influence of the Hartman number the slip parameter and normalized coating thickness parameter on the flow and deflection are discussed graphically. In the presence of magnetic field and slip the fluid velocity and hence blade deflection can be controlled.
ABSTRACT. Ascorbate peroxidase (APX) plays a central role in the ascorbate-glutathione cycle and is a key enzyme in cellular H 2 O 2 metabolism. It includes a family of isoenzymes with different characteristics, which are identified in many higher plants. In the present study, we isolated the APX gene from Jatropha curcas L, which is similar with other previously characterized APXs as revealed by alignment and phylogenetic analysis of its deduced amino acid sequence. Real-time qPCR analysis showed that the expression level of JcAPX transcript significantly increased under NaCl stress. Subsequently, to elucidate the contribution of JcAPX to the protection against salt-induced oxidative stress, the expression construct p35S: JcAPX was created and transformed into Arabidopsis and transcribed. Under 150-mM NaCl stress, compared with wild type (WT), the overexpression of JcAPX in Arabidopsis increased the germination rate, the number of leaves, Y. Chen et al. 4880©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (2): 4879-4889 (2015) and the rosette area. In addition, the transgenic plants had longer roots, higher total chlorophyll content, higher total APX activity, and lower H 2 O 2 content than the WT under NaCl stress conditions. These results suggested that higher APX activity in transgenic lines increases the salt tolerance by enhancing scavenging capacity for reactive oxygen species under NaCl stress conditions.
Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve the accuracy and speed of image classification, thus reducing human error and workload. Here we developed a custom-made deep learning model to assist pathologists in detecting OSCC from histopathology images. We collected and analyzed a total of 2,025 images, among which 1,925 images were included in the training set and 100 images were included in the testing set. Our model was able to automatically evaluate these images and arrive at a diagnosis with a sensitivity of 0.98, specificity of 0.92, positive predictive value of 0.924, negative predictive value of 0.978, and F1 score of 0.951. Using a subset of 100 images, we examined whether our model could improve the diagnostic performance of junior and senior pathologists. We found that junior pathologists were able to delineate OSCC in these images 6.26 min faster when assisted by the model than when working alone. When the clinicians were assisted by the model, their average F1 score improved from 0.9221 to 0.9566 in the case of junior pathologists and from 0.9361 to 0.9463 in the case of senior pathologists. Our findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images.
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