Measurement of Oxygen Partial Pressure, its Control During Hypoxia and Hyperoxia, and its Effect upon Light Emission in a Bioluminescent Elaterid Larva

This study investigates the respiratory physiology of bioluminescent larvae of Pyrearinus termitilluminans in relation to their tolerance to hypoxia and hyperoxia and to the supply of oxygen for bioluminescence. The partial pressure of oxygen (P(O2)) was measured within the bioluminescent prothorax by in vivo electron paramagnetic resonance (EPR) oximetry following acclimation of larvae to hypoxic, normoxic and hyperoxic (normobaric) atmospheres and during periods of bioluminescence (during normoxia). The P(O2) in the prothorax during exposure to an external P(O2) of 15.2, 160 and 760 mmHg was 10.3+/-2.6, 134+/-0.9 and 725+/-73 mmHg respectively (mean +/- s.d., N=5; 1 mmHg=0.1333 kPa). Oxygen supply to the larvae via gas exchange through the spiracles, measured by determining the rate of water loss, was also studied in the above atmospheres and was found not to be dependent upon P(O2). The data indicated that there is little to no active control of extracellular tissue P(O2) within the prothorax of these larvae. The reduction in prothorax P(O2) observed during either attack-response-provoked bioluminescence or sustained feeding-related bioluminescence in a normoxic atmosphere was variable, but fell within the range 10-25 mmHg. The effect of hypoxic atmospheres on bioluminescence was measured to estimate the intracellular P(O2) within the photocytes of the prothorax. Above a threshold value of 50-80 mmHg, bioluminescence was unaffected by P(O2). Below this threshold, an approximately linear relationship between P(O2) and bioluminescence was observed. Taken together with the extracellular P(O2) measurements, this suggests that control of P(O2) within the photocyte may occur. This work establishes that EPR oximetry is a valuable technique for long-term measurement of tissue P(O2) in insects and can provide valuable insights into their respiratory physiology. It also raises questions regarding the hypothesis that bioluminescence can have a significant antioxidative effect by reduction of prothorax P(O2 )through oxygen consumption

Exploiting ConvNet Diversity for Flooding Identification

Flooding is the world's most costly type of natural disaster in terms of both economic losses and human causalities. A first and essential procedure toward flood monitoring is based on identifying the area most vulnerable to flooding, which gives authorities relevant regions to focus. In this letter, we propose several methods to perform flooding identification in high-resolution remote sensing images using deep learning. Specifically, some proposed techniques are based upon unique networks, such as dilated and deconvolutional ones, whereas others were conceived to exploit diversity of distinct networks in order to extract the maximum performance of each classifier. The evaluation of the proposed methods was conducted in a high-resolution remote sensing data set. Results show that the proposed algorithms outperformed the state-of-the-art baselines, providing improvements ranging from 1% to 4% in terms of the Jaccard Index

An Effective Visual Descriptor Based on Color and Shape Features for Image Retrieval

International audienceIn this paper we present a Content-Based Image Retrieval (CBIR) system which extracts color features using Dominant Color Correlogram Descriptor (DCCD) and shape features using Pyramid Histogram of Oriented Gradients (PHOG). The DCCD is a descriptor which extracts global and local color features, whereas the PHOG descriptor extracts spatial information of shape in the image. In order to evaluate the image retrieval effectiveness of the proposed scheme, we used some metrics commonly used in the image retrieval task such as, the Average Retrieval Precision (ARP), the Average Retrieval Rate (ARR) and the Average Normalized Modified Retrieval Rank (ANMRR) and the Average Recall (R)-Average Precision (P) curve. The performance of the proposed algorithm is compared with some other methods which combine more than one visual feature (color, texture, shape). The results show a better performance of the proposed method compared with other methods previously reported in the literature