Rapidly decreasing water availability as a consequence of climate change is likely to endanger the range of long-lived tree species. A pressing question is, therefore, whether adaptation to drought exists in important temperate tree species like European beech (Fagus sylvatica L.), a wide-spread, dominant forest tree in Central Europe. Here, five beech stands were selected along a precipitation gradient from moist to dry conditions. Neutral genetic markers revealed strong variation within and little differentiation between the populations. Natural regeneration from these stands was transferred to a common garden and used to investigate the expression of genes for abscisic acid (ABA)-related drought signaling [9-cis-epoxy-dioxygenase (NCED), protein phosphatase 2C (PP2C), early responsive to dehydration (ERD)] and stress protection [ascorbate peroxidase (APX), superoxide dismutase (SOD), aldehyde dehydrogenase (ALDH), glutamine amidotransferase (GAT)] that are involved in drought acclimation. We hypothesized that progenies from dry sites exhibit constitutively higher expression levels of ABA- and stress-related genes and are less drought responsive than progenies from moist sites. Transcript levels and stress responses (leaf area loss, membrane integrity) of well-irrigated and drought-stressed plants were measured during the early, mid- and late growing season. Principal component (PC) analysis ordered the beech progenies according to the mean annual precipitation at tree origin by the transcript levels of SOD, ALDH, GAT and ERD as major loadings along PC1. PC2 separated moist and drought treatments with PP2C levels as important loading. These results suggest that phosphatase-mediated signaling is flexibly acclimated to the current requirements, whereas stress compensatory measures exhibited genotypic variation, apparently underlying climate selection. In contrast to expectation, the drought responses were less pronounced than the progeny-related differences and the transcript levels were constitutively lower in beeches from dry than from moist sites. These results imply that beeches from dry origins may have evolved mechanisms to avoid oxidative stress.

The estimation of correspondences between two images resp. point sets is a core problem in computer vision. One way to formulate the problem is graph matching leading to the quadratic assignment problem which is NP-hard. Several so called second order methods have been proposed to solve this problem. In recent years hypergraph matching leading to a third order problem became popular as it allows for better integration of geometric information. For most of these third order algorithms no theoretical guarantees are known. In this paper we propose a general framework for tensor block coordinate ascent methods for hypergraph matching. We propose two algorithms which both come along with the guarantee of monotonic ascent in the matching score on the set of discrete assignment matrices. In the experiments we show that our new algorithms outperform previous work both in terms of achieving better matching scores and matching accuracy. This holds in particular for very challenging settings where one has a high number of outliers and other forms of noise.

Cervical cancer is one of the two most common gynecological cancers in the world, including breast cancer. Signs of cervical disease are usually the presence of atypical epithelium, superficial bleeding or abnormal vascular proliferation. Most of these signs are directly related to cervical intraepithelial neoplasia (CIN) and cervical cancer. Currently, to detect epithelial lesions as well as to observe the shape of blood vessels, the main diagnostic methods used are colposcopy and visual examination. This method has low sensitivity and specificity because subjective factors still exist and the method does not clearly distinguish the shape of proliferating blood vessels. Therefore, in order to improve the efficiency of disease diagnosis, many studies applying image processing techniques to support auto-diagnosis have become topics of interest. However, studies that support automatic identify abnormal blood vessel shape and density are very limited. In this study, colposcopy images were recorded by digital colposcopes. These images are taken under polarized light to help reduce reflections from the surface and support for better image processing steps. Then, Sauvola threshold method is used to separate blood vessels on the surface of the cervix. It is combined with three different image preprocessing methods to enhance the contrast between the blood and the background. Finally, the sensitivity and specificity of these methods were calculated and evaluated. The results of the study set the stage for cervical blood vessel identification studies as well as cervical cancer assessment.

Collagen provides tissue strength and structural integrity. Quantification of the orientated dispersion of collagen fibers is an important factor when studying the mechanical properties of the cervix. In this study, for the first time, a new method for rapid characterization of the collagen fiber orientations of the cervix using linearly polarized light colposcopy is presented. A total of 24 colposcopic images were captured using a cross-polarized imaging system with white LED light sources. In the preprocessing stage, the Red channel of the RGB image was chosen, which contains no information of the blood vessels because of the low-absorption of blood cells in the red region. OrientationJ, which is an ImageJ plug-in, was used to estimate the local orientation of the collagen fibers. The result shows that in the nonpregnant cervix, the middle zone (Zone 2) has circumferentially aligned collagen fibers while the inner zone (Zone 1) has randomly arranged. The collagen fiber dispersion in Zone 2 is much smaller than that in Zone 1 at all four quadrants region (anterior, posterior, left, and right quadrant). This new analysis technique could potentially combine with diagnostic tools to provide a quantitative platform of collagen fibers in the clinic.

Drawing on Appraisal Framework (Martin & White, 2005), which is developed from Systemic Functional Linguistics Theory (hereafter SFL), this paper investigates how evaluative language is used in writing for middle school students. The data was collected from students' artifacts and questionnaires. Students needed to write two paragraphs on the same topic before and after they were introduced to attitudinal meaning through several lessons in the class. It is an action research project in which students were taught the English writing section with the focus on using evaluative language. The results revealed that students have gained a better understanding of the Appraisal Framework and know how to use evaluative language effectively in their writings after learning about it since they had considerably improved in vocabulary items through main components of Attitude and Graduation. In addition, the questionnaires showed students' reflection on using attitudinal tools in their writing and it reinforced the paper's outcomes mentioned above.