Oxidative stress is an imbalance between free radicals and antioxidants, and is an important etiological factor in the development of hypertension. Recent experimental evidence suggests that subpressor doses of angiotensin II elevate oxidative stress and blood pressure. We aimed to investigate the oxidative stress related mechanism by which a subpressor dose of angiotensin II induces hypertension in a normotensive rat model. Normotensive male Wistar rats were infused with a subpressor dose of angiotensin II for 28 days. The control group was sham operated and infused with saline only. Plasma angiotensin II and H2O2 levels, whole-blood glutathione peroxidase, and AT-1a, Cu/Zn SOD, and p22phox mRNA expression in the aorta was assessed. Systolic and diastolic blood pressures were elevated in the experimental group. There was no change in angiotensin II levels, but a significant increase in AT-1a mRNA expression was found in the experimental group. mRNA expression of p22phox was increased significantly and Cu/Zn SOD decreased significantly in the experimental group. There was no significant change to the H2O2 and GPx levels. Angiotensin II manipulates the free radical-antioxidant balance in the vasculature by selectively increasing O2 - production and decreasing SOD activity and causes an oxidative stress induced elevation in blood pressure in the Wistar rat., M. M. Govender, A. Nadar., and Obsahuje bibliografii
Monogeneans rely on firm attachment to often flexible and uneven surfaces and are renowned for their effective posterior attachment structures in the form of adhesives, clamps, hamuli and suckers. Polystomatids do not secrete adhesives and do not have clamps. While only some have hamuli, all have suckers in the adult form. Three different types of haptoral suckers have been described based on basic morphology but have never been studied in depth. Using enzyme digestion and light (differential interference contrast), confocal and scanning electron microscopy, we examined representatives and propose four sucker types. Haptoral sucker Type I are symmetrical soft, flexible, cup- to disk-shaped suckers and are found in all polystomes infecting frogs and salamanders. Type II suckers are symmetrical soft, flexible, cup-shaped suckers with a hollow continuous skeletal ring and no other skeletal elements. They are found in species of Nanopolystoma Du Preez, Wilkinson et Huyse, 2008 infecting caecilians. Type III suckers are symmetrical firm, cup-shaped suckers with elaborate skeletal elements that contribute to a secure grip on the host tissue. This type of sucker is found in all polystomes infecting freshwater turtles and the common hippopotamus. Type IV suckers are asymmetrical with an elaborate series of long, thin sclerites with terminal spines or hooks. This type of sucker is only known from Concinnocotyla australensis (Reichenbach-Klinke, 1966) infecting the Australian lungfish. These different sucker types are crucial for the survival of polystomatid flatworms within their respective microhabitats.
Several thousands of the seven-spot ladybird Coccinella septempunctata L., descended upon a cruise ship over several hours in daylight while in port in Morocco in April 2009. The ship had recently arrived from South America. Despite a treatment of fumigation beetles were found living after fourteen days following the inoculation event. This observation indicates an ocean transmission of large numbers of this species could take place and might have happened in the past.
Swarm intelligence is an emerging field with wide-reaching application opportunities in problems of optimization, analysis and machine learning. While swarm systems have proved very effective when applied to a variety of problems, swarm-based methods for computer vision have received little attention. This paper proposes a swarm system capable of extracting and exploiting the geometric properties of objects in images for fast and accurate recognition. In this approach, computational agents move over an image and affix themselves to relevant features, such as edges and corners. The resulting feature profile is then processed by a classification subsystem to categorize the object. The system has been tested with images containing several simple geometric shapes at a variety of noise levels, and evaluated based upon the accuracy of the system's predictions. The swarm system is able to accurately classify shapes even with high image noise levels, proving this approach to object recognition to be robust and reliable.