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.

Cytokines in inflammatory bowel disease

Abstract: Erroneous communication between the innate and adaptive immune systems through cytokines results in exaggerated or attenuated immune response. It is not known whether the pathologic immune response in inflammatory bowel disease has its origin in a dysbalance of pro- and anti-inflammatory cytokine release or whether it is secondary in subsequence of a defective intestinal barrier or the destructive power of aggressive microbiota in the gut lumen.

Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects.

Abstract: If the clinical diagnosis of probable AD is imprecise with accuracy rates of approximately 90% or lower using established consensus criteria for probable AD, but definite AD requires autopsy confirmation, it is not surprising that diagnostic accuracy is lower at early and presymptomatic stages of AD.1–4 It is believed that the development of full-blown AD takes place over an approximately 20-year prodromal period, but this is difficult to determine in the absence of biomarkers that reliably signal the onset of nascent disease before the emergence of measurable cognitive impairments. Because intervention with disease-modifying therapies for AD is likely to be most efficacious before significant neurodegeneration has occurred, there is an urgent need for biomarker-based tests that enable a more accurate and early diagnosis of AD.5–7 Moreover, such tests could also improve monitoring AD progression, evaluation of new AD therapies, and enrichment of AD cohorts with specific subsets of AD subjects in clinical trials. The defining lesions of AD are neurofibrillary tangles and senile plaques formed, respectively, by neuronal accumulations of abnormal hyperphosphorylated tau filaments and extracellular deposits of amyloid β (Aβ) fibrils, mostly the 1 to 42 peptide (Aβ1-42), the least soluble of the known Aβ peptides produced from Aβ precursor protein by the action of various peptidases.1–3 Hence, for these and other reasons summarized in consensus reports on AD biomarkers, cerebrospinal fluid (CSF), total tau (t-tau), and Aβ were identified as being among the most promising and informative AD biomarkers.5,6 Increased levels of tau in CSF are thought to occur after its release from damaged and dying neurons that harbor dystrophic tau neurites and tangles, whereas reduced CSF levels of Aβ1-42 are believed to result from large-scale accumulation of this least soluble of Aβ peptides into insoluble plaques in the AD brain. The combination of increased CSF concentrations of t-tau and phosphotau (p-tau) species and decreased concentrations of Aβ1-42 are considered to be a pathological CSF biomarker signature that is diagnostic for AD.5,6,8,9 Notably, recent studies have provided compelling preliminary data to suggest that this combination of CSF tau and Aβ biomarker changes may predict the conversion to AD in mild cognitive impairment (MCI) subjects.10 Thus, an increase in levels of CSF tau associated with a decline in levels of CSF Aβ1-42 may herald the onset of AD before it becomes clinically manifest. However, before the utility of CSF Aβ1-42 and tau concentrations for diagnosis of AD can be established, it is critical to standardize the methodology for their measurement.5–8,10 For example, among the published studies of CSF tau and Aβ, there is considerable variability in the observed levels of these analytes, as well as their diagnostic sensitivity and specificity. This is attributable to variability in analytical methodology standardization and other factors that differ between studies of the same CSF analytes in similar but not identical cohorts.5–7 The Alzheimer’s Disease Neuroimaging Initiative (ADNI) was launched in 2004 to address these and other limitations in AD biomarkers (see reviews in Shaw and colleagues7 and Mueller and coauthors,11 and the ADNI Web site [http://www.adni-info.org/index] where the ADNI grant and all ADNI data are posted for public access). To this end, the Biomarker Core of ADNI conducts studies on ADNI-derived CSF samples to measure CSF Aβ1-42, t-tau, and p-tau (tau phosphorylated at threonine181 [p-tau181p]) in standardized assays. Evaluation of CSF obtained at baseline evaluation of 416 of the 819 ADNI subjects is now complete, and we report here our findings on the performance of these tests using a standardized multiplex immunoassay system that measures the biomarkers simultaneously in the same sample aliquot in ADNI subjects and in an independent cohort of autopsy-confirmed AD cases.

Sharper Focus for a Radially Polarized Light Beam

Abstract: We experimentally demonstrate for the first time that a radially polarized field can be focused to a spot size significantly smaller [$0.16(1){\ensuremath{\lambda}}^{2}$] than for linear polarization ($0.26{\ensuremath{\lambda}}^{2}$). The effect of the vector properties of light is shown by a comparison of the focal intensity distribution for radially and azimuthally polarized input fields. For strong focusing, a radially polarized field leads to a longitudinal electric field component at the focus which is sharp and centered at the optical axis. The relative contribution of this component is enhanced by using an annular aperture.

DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.

Abstract: Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain unprotected from novel malware. In this paper, we propose DREBIN, a lightweight method for detection of Android malware that enables identifying malicious applications directly on the smartphone. As the limited resources impede monitoring applications at run-time, DREBIN performs a broad static analysis, gathering as many features of an application as possible. These features are embedded in a joint vector space, such that typical patterns indicative for malware can be automatically identified and used for explaining the decisions of our method. In an evaluation with 123,453 applications and 5,560 malware samples DREBIN outperforms several related approaches and detects 94% of the malware with few false alarms, where the explanations provided for each detection reveal relevant properties of the detected malware. On five popular smartphones, the method requires 10 seconds for an analysis on average, rendering it suitable for checking downloaded applications directly on the device.