A Facile Bifunctional Strategy for Fabrication of Bioactive or Bioinert Functionalized Organic Surfaces via Amides-Initiated Photochemical Reactions

The excellent potential of organic polymeric materials in the biomedical field could be exploited if their interfacial problem could be fully resolved. A necessary prerequisite to this purpose often involves the simple but effective synthesis of a bioactive surface to endow polymer surfaces with high reactivity toward efficient biomolecules conjugation and a bioinert surface to prevent nonspecific adsorption of nontarget biomolecules. Although the corresponding research has been an important topic, actually few strategies could pave the way to comprehensively and simply tackle both of the bioactive and bioinert surfaces preparation issues. Herein we report an extremely simple and integrative bifunctional method that could efficiently tailor an organic material surface toward both bioactive and bioinert functions. This method is based on the use of an amides-initiated photochemical reaction in a confined space, which depending on the type of solutes used, results in the incorporation of primary amine groups or surface carbon radicals on an inert polymer surface. The grafted amine group could be used as a highly reactive site for biomolecule conjugation, and the surface carbon radical could be used to initiate radical graft polymerization of antifouling polymer brushes. We expect this simple but powerful method could provide a general resolution to solve the interfacial problem of organic substrate, offering a low-cost practical approach for real biomedical applications

Current and Potential Tree Locations in Tree Line Ecotone of Changbai Mountains, Northeast China: The Controlling Effects of Topography

<div><p>Tree line ecotone in the Changbai Mountains has undergone large changes in the past decades. Tree locations show variations on the four sides of the mountains, especially on the northern and western sides, which has not been fully explained. Previous studies attributed such variations to the variations in temperature. However, in this study, we hypothesized that topographic controls were responsible for causing the variations in the tree locations in tree line ecotone of the Changbai Mountains. To test the hypothesis, we used IKONOS images and WorldView-1 image to identify the tree locations and developed a logistic regression model using topographical variables to identify the dominant controls of the tree locations. The results showed that aspect, wetness, and slope were dominant controls for tree locations on western side of the mountains, whereas altitude, SPI, and aspect were the dominant factors on northern side. The upmost altitude a tree can currently reach was 2140 m asl on the northern side and 2060 m asl on western side. The model predicted results showed that habitats above the current tree line on the both sides were available for trees. Tree recruitments under the current tree line may take advantage of the available habitats at higher elevations based on the current tree location. Our research confirmed the controlling effects of topography on the tree locations in the tree line ecotone of Changbai Mountains and suggested that it was essential to assess the tree response to topography in the research of tree line ecotone.</p></div

Aerial photograph of the volcanic cone in the Changbai Mountains.

<p>The photo was taken from the northern side on October 2010. Region A is on the northern slope. It can be seen that trees in the northern side gradually move upward to the high elevations. Region B is on the western side. It can be seen that tree line position in western side is relatively stable.</p

Classification tables showing the predictive accuracies of the complete model.

<p>Shown are the numbers of pixels that were observed and predicted in the two classes of tree and non-tree.</p><p>Classification tables showing the predictive accuracies of the complete model.</p

Location of the study areas in Changbai Mountains Nature Reserve in Jilin province, Northeast China.

<p>DEM (Digital elevation model) value ranges from 713 to 2681. Area A shown by the IKONOS image (False color image created by combining the blue band, green band, and red band) is on the northern slope. Area B shown by the WorldView-1 image is on the western slope. Red area represented the training and validation area. Blue area represented the test area which is used for the prediction of tree locations.</p

Classification accuracy of tree locations of IKONOS and WorldView-1 images.

<p>Classification accuracy of tree locations of IKONOS and WorldView-1 images.</p

Topographic variables of the northern side derived from the digital elevation model.

<p>PRR represents potential relative radiation which sums daily values over the growing season. SPI represents snow potential index which indicates the snow accumulation in topography. LST represents the land surface temperature.</p

Classification and prediction maps of tree locations in the western side.

<p>(a) Classification result of WorldView-1 image in the western side. Tree classification was shown in black. Non-tree classification was shown in grey. (b) Predicted tree locations in the training area, validation area, and the test area. Green indicates correctly predicted trees. Blue indicates areas where tree is predicted where in fact tree was not present; red indicates areas where no tree is predicted where in fact tree was present.</p

Topographic variables of the western side derived from the digital elevation model.

<p>PRR represents potential relative radiation which sums daily values over the growing season. SPI represents snow potential index which indicates the snow accumulation in topography. LST represents the land surface temperature.</p

Logistic regression models including the model parameters of the northern and the western sides.

<p>Classification accuracy was shown with the numbers of pixels that were observed and predicted in the two classes of tree and non-tree.</p><p>The variables that have considerable effects on the models are marked in bold. Significance of all variables: <i>P</i> < 0.001.</p><p>PRR represents potential relative radiation which sums daily values over the growing season.</p><p>SPI represents snow potential index which indicates the snow accumulation in topography. LST represents land surface temperature.</p><p>Logistic regression models including the model parameters of the northern and the western sides.</p