Hormesis - The Stimulation Of Growth By Low-Levels Of Inhibitors

Hormesis is the name given to the stimulatory effects caused by low levels of potentially toxic agents. When this phenomenon was first identified it was called the Arndt-Schulz Law or Hueppe's Rule, because it was thought to occur generally. Although this generalisation is not accepted today, there has never been more evidence in its support, justifying a re-examination of the phenomenon. Evidence from the literature shows that not only has growth hormesis been observed in a range of taxa after exposure to a variety of agents, but also that the dose-response data have a consistent form. While there are a number of separate hypotheses to explain specific instances of hormesis, the evidence presented here suggests that different examples might have a common explanation, and the possibility of a general theory is considered.

Hormesis - Stimulation Of Colony Growth In Campanularia-Flexuosa (Hydrozoa) By Copper Cadmium And Other Toxicants

It is shown experimentally that subinhibitory concentrations of a number of toxic, or other agents that are typically inhibitory (copper, cadmium, tributyl tin fluoride, reduced salinity), may stimulate the growth of colonies of the hydroid Campanularia flexuosa, exhibiting a phenomenon known as hormesis. It is suggested that the stimulation of growth is not due to the specific properties of the different toxicants, but to an adaptive response of the hydroid to the inhibitory effect that they have in common. Growth is regulated by a control mechanism and it is proposed that the increased growth is a consequence of overcorrections to low levels of an inhibitory challenge. Examination of the toxicological literature shows that hormesis is a more common occurrence that is generally supposed, and it is suggested that the explanation given here might apply in other cases of hormesis.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.