University of Erlangen-Nuremberg

About: University of Erlangen-Nuremberg is a education organization based out in Erlangen, Bayern, Germany. It is known for research contribution in the topics: Population & Immune system. The organization has 42405 authors who have published 85600 publications receiving 2663922 citations.

Genetic evidence implicates the immune system and cholesterol metabolism in the aetiology of Alzheimer's disease.

Abstract: Background 1Late Onset Alzheimer's disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes. Methodology We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset. Principal Findings We found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD. Significance Processes related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer's disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches.

The open innovation research landscape: established perspectives and emerging themes across different levels of analysis

Abstract: This paper provides an overview of the main perspectives and themes emerging in research on open innovation (OI). The paper is the result of a collaborative process among several OI scholars – having a common basis in the recurrent Professional Development Workshop on ‘Researching Open Innovation’ at the Annual Meeting of the Academy of Management. In this paper, we present opportunities for future research on OI, organised at different levels of analysis. We discuss some of the contingencies at these different levels, and argue that future research needs to study OI – originally an organisational-level phenomenon – across multiple levels of analysis. While our integrative framework allows comparing, contrasting and integrating various perspectives at different levels of analysis, further theorising will be needed to advance OI research. On this basis, we propose some new research categories as well as questions for future research – particularly those that span across research domains that have so far developed in isolation.

An Evaluation of Popular Copy-Move Forgery Detection Approaches

Abstract: A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features Sift and Surf, as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.

Phase III Randomized Study of Ribociclib and Fulvestrant in Hormone Receptor–Positive, Human Epidermal Growth Factor Receptor 2–Negative Advanced Breast Cancer: MONALEESA-3

Abstract: PurposeThis phase III study evaluated ribociclib plus fulvestrant in patients with hormone receptor–positive/human epidermal growth factor receptor 2–negative advanced breast cancer who were treatment naive or had received up to one line of prior endocrine therapy in the advanced setting.Patients and MethodsPatients were randomly assigned at a two-to-one ratio to ribociclib plus fulvestrant or placebo plus fulvestrant. The primary end point was locally assessed progression-free survival. Secondary end points included overall survival, overall response rate, and safety.ResultsA total of 484 postmenopausal women were randomly assigned to ribociclib plus fulvestrant, and 242 were assigned to placebo plus fulvestrant. Median progression-free survival was significantly improved with ribociclib plus fulvestrant versus placebo plus fulvestrant: 20.5 months (95% CI, 18.5 to 23.5 months) versus 12.8 months (95% CI, 10.9 to 16.3 months), respectively (hazard ratio, 0.593; 95% CI, 0.480 to 0.732; P < .001). Consiste...