University of Lincoln

About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Higher education. The organization has 2341 authors who have published 7025 publications receiving 124797 citations.

Outcome Orientated Child and Adolescent Mental Health Services (OO-CAMHS): a whole service model.

Abstract: The international evidence base on factors that most influence outcomes in mental health care finds that matching therapeutic intervention to diagnosis has a clinically insignificant impact on outcomes. Decades of outcome research into treatment of psychiatric disorders shows that, despite the development of many new techniques, the outcomes being achieved in studies 30 years ago are similar to those being achieved now. In the last few years, new service models that incorporate systems of feedback on progress and alliance have emerged and show promise with regards improving overall outcomes for mental health service users. Growing familiarity with this outcome literature, together with a desire to be part of a service that can continue to improve patient outcomes, led a small community Child and Adolescent Mental Health Services team to develop a new whole service model - Outcome Orientated Child and Adolescent Mental Health Services (OO-CAMHS). OO-CAMHS incorporates key aspects of the evidence base on what could make a differential positive impact on outcomes and relinquishes those aspects that do not. In this paper, we outline the evidence base on which OO-CAMHS is built, describe the key features of the approach and present some of the early findings on its impact.

Drivers of Population Structure of the Bottlenose Dolphin (Tursiops truncatus) in the Eastern Mediterranean Sea

Abstract: The drivers of population differentiation in oceanic high dispersal organisms, have been crucial for research in evolutionary biology Adaptation to different environments is commonly invoked as a driver of differ- entiation in the oceans, in alternative to geographic isola- tion In this study, we investigate the population structure and phylogeography of the bottlenose dolphin (Tursiops truncatus) in the Mediterranean Sea, using microsatellite loci and the entire mtDNA control region By further comparing the Mediterranean populations with the well described Atlantic populations, we addressed the following hypotheses: (1) bottlenose dolphins show population structure within the environmentally complex Eastern Mediterranean Sea; (2) population structure was gained locally or otherwise results from chance distribution of pre- existing genetic structure; (3) strong demographic varia- tions within the Mediterranean basin have affected genetic variation sufficiently to bias detected patterns of population structure Our results suggest that bottlenose dolphin ex- hibits population structures that correspond well to the main Mediterranean oceanographic basins Furthermore, we found evidence for fine scale population division within the Adriatic and the Levantine seas We further describe for the first time, a distinction between populations in- habiting pelagic and coastal regions within the Mediter- ranean Phylogeographic analysis suggests that current genetic structure, results mostly from stochastic distribu- tion of Atlantic genetic variation, during a recent post- glacial expansion Comparison with Atlantic mtDNA haplotypes, further suggest the existence of a metapopulation across North Atlantic/Mediterranean, with pelagic regions acting as source for coastal environments

Semi-Automated Segmentation of Single and Multiple Tumors in Liver CT Images Using Entropy-Based Fuzzy Region Growing

Abstract: Aims The liver CT image segmentation is still until now a challenging problem due to the fuzzy nature of the tumor transition to the surrounding tissues. The objective of this article is the consideration of the uncertainty present around the boundaries of a tumor region in the segmentation of the liver CT images as well as the segmentation of multiple tumors in the same CT image. Materials and methods A semi-automated segmentation method, including entropy-based processing, is proposed to segment single and multiple liver lesions from CT images. The proposed method introduces an entropy-based fuzzy region growing (EFRG) technique to extract the liver tumors whilst reducing the leakage, notably in CT images including several lesions. In fact, after the image rehaussement, an entropy-based fuzzy region growing was introduced in order to take into account the fuzzy nature of the tumor transition to the surrounding tissues. In fact, the local entropy is computed for each pixel in order to consider the spatial distribution of gray levels and to represent the variance of the local region. Then, after selecting manually the seed pixel, fuzzy membership function is used to preserve the fuzzy nature of tumor boundaries and to postpone the crisp decision until further information can be available to make the final decision. Starting with the seed pixel, the proposed method iteratively computes the region mean entropy and the resulted tumor region is obtained using a fixed threshold-based membership degree. In the case of multiple tumors in the same liver CT image, the overlapping between adjacent tumors is treated through a distance-based processing in order to assign each pixel exclusively to one tumor. Results Experimental results prove that the method accurately segments single and multiple tumors in liver CT images over three different datasets, despite their small size, heterogeneity and fuzzy boundaries. Results were evaluated using standard quantitative measures, including the area overlapping error (AOE) and the relative area difference (RAD), for 2D segmentation, the volume overlapping error (VOE) and the relative volume difference (RVD), for 3D segmentation, and Dice similarity measure (DSM) for both cases. The mean AOE, RAD and DSM values reached by the entropy-based fuzzy region growing method over the ImageCLEF dataset were 19.9, 15.45 and 0.88, respectively. The results show that the proposed method is equivalent or even better to state-of-the-art methods over the 2D NUH dataset as well as over the 3D MIDAS dataset. Conclusion An entropy-based fuzzy region growing method was proposed to treat the overlap between overlapped tumors in liver CT images. This allows to improve results compared to the liver CT image segmentation methods of the state-of-the-art.