An essential feature of a healed wound is the restoration of an intact epidermal barrier through wound epithelialization, also known as re-epithelialization. The directed migration of keratinocytes is critical to wound epithelialization and defects in this function are associated with the clinical phenotype of chronic non-healing wounds. A complex balance of signaling factors and surface proteins are expressed and regulated in a temporospatial manner that promote keratinocyte motility and survival to activate wound re-epithelialization. The majority of this review focuses on the mechanisms that regulate keratinocyte migration in the re-epithelialization process. This includes a review of cell attachments via desmosomes, hemidesmosomes, and integrins, the expression of keratins, the role of growth factors, cytokines and chemokines, eicosanoids, oxygen tension, antimicrobial peptides, and matrix metalloproteinases. Also reviewed are recently emerging novel mediators of keratinocyte motility including the role of electric fields, and signaling via the acetylcholine and beta-adrenergic receptors. These multiple regulators impact the ability of keratinocytes to migrate from the wound edge or other epidermal reservoirs to efficiently re-epithelialize a breach in the integrity of the epidermis. New discoveries will continue to uncover the elegant network of events that result in restoration of epidermal integrity and complete the wound repair process.

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In this report we explore the guidance of circumnutation of climbing bean stems under the light of general rho/tau theory, a theory that aims to explain how living organisms guide goal-directed movements ecologically. We present some preliminary results on the control of circumnutation by climbing beans, and explore the possibility that the power of movement in plants, more generally, is controlled under ecological principles.

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Texture analysis is one of the important as well as useful tasks in image processing applications. Many texture models have been developed over the past few years and Local Binary Patterns (LBP) is one of the simple and efficient approach among them. A number of extensions to the LBP method have been also presented but the problem remains challenging in feature vector generation and comparison. As textures are oriented and scaled differently, a texture model should effectively handle grey-scale variation, rotation variation, illumination variation and noise. The length of the feature vector in a texture model also plays an important role in deciding the time complexity of the texture analysis. This study proposes a new texture model, called Optimized Local Ternary Patterns (OLTP) in the spatial methods of texture analysis. The proposed texture model is based on Local Ternary Patterns (LTP), which in turn is based on LBP. A new concept called "Level of Optimality" to select the optimal set of patterns is discussed in this study. This proposed texture model uses only optimal patterns to extract the textural information from the digital images and thereby reducing the length of the feature vector. This proposed model is robust to image rotation, grey-scale transformation, histogram equalization and noise. The results are compared with other widely used texture models by applying classification tests to variety of texture images from the standard Brodatz texture database. Experimental results prove that the proposed texture model is robust to grey-scale variation, image rotation, histogram equalization and noise. Experimental results also show that the proposed texture model improves the classification accuracy and the speed of the classification process. In all tested tasks, the proposed method outperforms the earlier methods.