Key words: Triatoma infestans -nutritional conditions -cold-shock tolerance -survival -molting rateThe survival, molting incidence and chromatin supraorganization of Triatoma infestans and Panstrongylus megistus, blood-sucking hemipteran vectors of Chagas disease, are affected by stressing agents, including heat and cold shocks (Rodrigues et al. 1991, Dantas & Mello 1992, Mello et al. 1995, Garcia et al. 1999, 2000. These insect species differ in nuclear characteristics (Mello 1971, 1975, Mello et al. 1986) and in their response to heat and cold shocks (Rodrigues et al. 1991, Garcia et al. 1999, with P. megistus being less resistant to prolonged heat (40 o C) and cold (0 o C) shocks than T. infestans (Rodrigues et al. 1991, Garcia et al. 1999.Thermotolerance following sequential heat shocks and tolerance to sequential cold shocks (i.e., an acquired increase in survival and molting incidence) have been reported in P. megistus (Garcia et al. 2001a,b). Cold-shock tolerance in insects has been suggested to involve the synthesis of heat-shock or other proteins, as well as the presence of cryoprotectants and improved use of metabolic energy resources (Clark & Fucito 1998). There is no evidence of cryoprotectant use in blood-sucking hemipterans. However, the synthesis of heat-shock proteins during the cold-shock tolerance response has been suggested for P. megistus (Garcia et al. 2001a Since no generalization can be made about the responses of different species of the same family to temperature shocks (Clark & Fucito 1998), and since T. infestans and P. megistus differ from each other in several aspects, including their responses to heat and cold shocks, it is possible that T. infestans may respond differently to sequential cold shocks compared to P. megistus. This response may be affected by the state of nourishment of the insects (Garcia et al. 1999).In the present study, survival and molting incidence were investigated in T. infestans after sequential cold shocks under different conditions of nourishment and the responses compared to those of P. megistus (Garcia et al. 2001a). MATERIALS AND METHODSFifth instar nymphs of T. infestans Klug (Hemiptera, Reduviidae) reared at 30 o C and 80% relative humidity in the laboratory at Sucen (Mogi-Guaçu, SP) were used. The insects fed once a week on hen blood. Some specimens were fasted for up to 15 days before and up to 30 days after the shocks.Immediately after a shock at 0 o C for 1 h, the nymphs were returned to control conditions and 8 h and 24 h later were subjected to a second shock at 0 o C which lasted 12 h. The temperature of 0 o C was chosen based on a previous report (Rodrigues et al. 1991).Nymphs maintained at 30 o C, a temperature traditionally used for rearing T. infestans in the insect facilities at Sucen since 1980 (Rodrigues et al. 1991)(control #1), as well as nymphs subjected to a single shock at 0 o C for 1 h (control #2) and 12 h (control #3) were used as controls.After the shocks, the nymphs were returned to the control temperature (30 o C) and monitored ...
Efficient automated print defect mapping is valuable to the printing industry since such defects directly influence customer-perceived printer quality and manually mapping them is cost-ineffective. Conventional methods consist of complicated and hand-crafted feature engineering techniques, usually targeting only one type of defect. In this paper, we propose the first end-to-end framework to map print defects at pixel level, adopting an approach based on semantic segmentation. Our framework uses Convolutional Neural Networks, specifically DeepLab-v3+, and achieves promising results in the identification of defects in printed images. We use synthetic training data by simulating two types of print defects and a print-scan effect with image processing and computer graphic techniques. Compared with conventional methods, our framework is versatile, allowing two inference strategies, one being near real-time and providing coarser results, and the other focusing on offline processing with more fine-grained detection. Our model is evaluated on a dataset of real printed images.
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