Industrial discharges of untreated effluents into water bodies and emissions into air have deteriorated the quality of water and air, respectively. The huge amount of pollutants derived from industrial activities represents a threat for the environment and ecologic equilibrium. Phenols and halogenated phenols, polycyclic aromatic hydrocarbons (PAH), endocrine disruptive chemicals (EDC), pesticides, dioxins, polychlorinated biphenyls (PCB), industrial dyes, and other xenobiotics are among the most important pollutants. Peroxidases are enzymes that are able to transform a variety of compounds following a free radical mechanism, thereby yielding oxidized or polymerized products. The peroxidase transformation of these pollutants is accompanied by a reduction in their toxicity, due to loss of biological activity, reduction in the bioavailability, or the removal from aqueous phase, especially when the pollutant is found in water. The review describes the sources of peroxidases, the reactions catalyzed by them, and their applications in the management of pollutants in the environment.

Glucocorticoids can mediate the destruction of thymocytes and T cell-derived leukemia cells through a mechanism known as apoptosis. The characteristic feature of apoptosis is fragmentation of DNA at internucleosomal linkers through the activity of a specific endonuclease. In this study, an attempt was made to compare dexamethasone-induced apoptosis in two T cell-derived human leukemia lines (CEM-C1 and CEM-C7) to the cell killing brought about by selected cytotoxic agents. In the CEM-C7 cell line (dexamethasone-sensitive), apoptosis was induced not only by dexamethasone but by actinomycin D, cycloheximide, and 25-OH cholesterol. In the CEM-C1 cell line (dexamethasone-resistant) cycloheximide, 25-OH cholesterol, or cell starvation could induce apoptosis. It appears that in leukemic cells apoptosis may be induced by a variety of unrelated toxic agents and is not limited to glucocorticoids.

Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! Interestingly, such sensitivity is not reflected in the average explanation accuracy scores over the dataset as commonly reported in the literature. In addition, explanations generated for robust classifiers (i.e. which are trained to be invariant to pixel-wise perturbations) are surprisingly more robust than those generated for regular classifiers.

The present study was conducted on the tongues of six Punjab white quails. The tissues from the apex, body and root of the tongue were processed for paraffin sectioning and scanning electron microscopy studies. The tongue was triangular in shape having an apex, body and root. The dorsal surface of the apex and body was smooth whereas large lingual conical papillae were located symmetrically and converging in median line between the body and the root. There was an additional layer of conical papillae composed of two large papillae behind the main row of papillae. The tongue was lined by stratified squamous epithelium which was keratinised at the apex. The anterior lingual salivary glands were mainly serous type whereas the posterior salivary glands were mucous type. There were topographical differences in the size, shape and appearance of the exfoliated superficial cells of the dorsal surface epithelium in the apex and body of the tongue.

No abstract