Effects of phosphite (Phi) on phosphate (Pi) starvation responses were determined in Ulva lactuca L. by incubation in Pi-limited (1 lM NaH 2 PO 4 ) or Pisufficient (100 lM NaH 2 PO 4 ) seawater containing 0-3 mM Phi. Exposure to 1 lM NaH 2 PO 4 decreased the growth rate and the content of free Pi and esterified-P but increased the activities of extracellular alkaline phosphatase (EC 3.1.2.1) and intracellular acid phosphatase (ACP; EC 3.1.2.2); two ACP isozymes observed by activity staining on isoelectric focussing (IEF) gel were induced. The K m value of Pi uptake rate was decreased by incubation with 1 lM NaH 2 PO 4 and the decrease in K m value was inhibited by 2 mM Phi, reflecting the operation of a high-affinity Pi uptake system at low Pi concentrations. In the presence of Phi, the growth rate of Pi-sufficient and Pi-starved thalli decreased as Phi concentrations were increased from 0 to 2 mM. As Phi concentrations were increased from 0 to 2 mM, the free Pi contents in both Pi-sufficient and Pi-starved thalli decreased, but the esterified-P contents in Pi-starved thalli increased, whereas those in Pi-sufficient thalli increased at 1 mM Phi and decreased at 2 mM Phi. Cell wall localized AP activity in both Pi-sufficient and Pi-starved thalli decreased as Phi concentrations were increased from 0 to 2 mM. Intracellular ACP activity in Pistarved thalli decreased as Phi concentrations were increased from 0 to 2 mM but was not affected in Pi-sufficient thalli. The induction of ACP isozyme activity and high-affinity Pi uptake system in Pistarved thalli was inhibited by Phi. The present investigation shows that Phi interrupts the sensing mechanisms of U. lactuca to Pi-limiting conditions.
In this study, we propose using a thermal imaging camera (TIC) with a deep learning model as an intelligent human detection approach during emergency evacuations in a low-visibility smoky fire scenarios. We use low-wavelength infrared (LWIR) images taken by a TIC qualified with the National Fire Protection Association (NFPA) 1801 standards as input to the YOLOv4 model for real-time object detection. The model trained with a single Nvidia GeForce 2070 can achieve >95% precision for the location of people in a low-visibility smoky scenario with 30.1 frames per second (FPS). This real-time result can be reported to control centers as useful information to help provide timely rescue and provide protection to firefighters before entering dangerous smoky fire situations.
To improve data transmission efficiency, image compression is a commonly used method with the disadvantage of accompanying image distortion. There are many image restoration (IR) algorithms, and one of the most advanced algorithms is the generative adversarial network (GAN)-based method with a high correlation to the human visual system (HVS). To evaluate the performance of GAN-based IR algorithms, we proposed an ensemble image quality assessment (IQA) called ATDIQA (Auxiliary Transformer with DISTS IQA) to give weights on multiscale features global self-attention transformers and local features of convolutional neural network (CNN) IQA of DISTS. The result not only performed better on the perceptual image processing algorithms (PIPAL) dataset with images by GAN IR algorithms but also has good model generalization over LIVE and TID2013 as traditional distorted image datasets. The ATDIQA ensemble successfully demonstrates its performance with a high correlation with the human judgment score of distorted images.
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