Fuzzy voting in clustering

In this paper we present a fuzzy voting scheme for cluster algorithms. This fuzzy voting method allows us to combine several runs of cluster algorithms resulting in a common fuzzy partition. This helps us to overcome instabilities of the cluster algorithms and results in a better clustering.Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science

Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

Menschenrechte und Demokratie begriffliche Unterschiede und Konvergenzen-

Diese Arbeit möchte der Frage nachgehen, wie eng die Begriffe Demokratie und Menschenrechte eng miteinander verknüpft sind und ob in der ideengeschichtlichen Entwicklung der beiden Phänomene immer schon ein notwendiger Zusammenhang bestand. Es wird beleuchtet wie dieser Zusammenhang von maßgeblichen politischen Denkern des 18. Jahrhunderts, in dem sich mit der französischen Revolution und der amerikanischen Unabhängigkeitserklärung die Gedanken von Demokratie und Menschenrechten stark politisch manifestierten, gesehen wurde. In der zweiten Hälfte der Arbeit 4 wechselt der/die LeserIn ins ausgehende 20. Jahrhundert wo nach wie vor dieser Zusammenhang Gestand zahlreicher Betrachtung darstellt. Abschließend kommt die Arbeit zu dem Schluss, dass es sich bei Demokratie und Menschenrechten um zwei unterschiedliche Phänomene handelt, auch wenn eine Sichtweise, in der alle als freie und gleiche Individuen verstanden werden, häufig zu einem Nebeneinander von beiden führen wird

Comparison of Two Toric IOLs with Different Haptic Design: Optical Quality after 1 Year

Background. The purpose of this prospective, randomised study was to interocularly compare the visual performance after implantation of two different toric IOLs with different haptic design. Methods. 59 subjects with corneal astigmatism greater than 1.25 diopter (D) were implanted with an AT TORBI 709M IOL (Carl Zeiss Meditec AG) in one eye and with a Tecnis toric aspheric IOL (Abbot Medical Optics) in the other eye. Observation procedure was performed 12 months postoperatively. Main outcome measures included uncorrected distance visual acuity (UDVA), manifest refraction, IOL rotation, and IOL position. Results. Mean UCDVA was 0.04 ± 0.14 logMAR for AT TORBI eyes and 0.06 ± 0.15 logMAR for Tecnis eyes (p=0.3). The postoperative spherical equivalent values were significantly lower in the AT TORBI group. Mean toric IOL axis rotation was 3.0 ± 2.26 degrees for AT TORBI eyes and 3.27 ± 2.37 for Tecnis eyes (p=0.5). The mean vertical IOL tilt and vertical decentration values measured with the Visante OCT were significantly larger in the AT TORBI group (p<0.05). Conclusions. Both the Tecnis and the AT TORBI toric IOLs successfully reduced ocular astigmatism. Emmetropia could be better achieved with the AT TORBI IOL, whereas the Tecnis showed better positional stability. This trial is registered with ICMJE NCT03371576

Using Plant Functional Traits to Explain Diversity–Productivity Relationships

Background: The different hypotheses proposed to explain positive species richness–productivity relationships, i.e. selection effect and complementarity effect, imply that plant functional characteristics are at the core of a mechanistic understanding of biodiversity effects. Methodology/Principal Findings: We used two community-wide measures of plant functional composition, (1) community- weighted means of trait values (CWM) and (2) functional trait diversity based on Rao’s quadratic diversity (FDQ) to predict biomass production and measures of biodiversity effects in experimental grasslands (Jena Experiment) with different species richness (2, 4, 8, 16 and 60) and different functional group number and composition (1 to 4; legumes, grasses, small herbs, tall herbs) four years after establishment. Functional trait composition had a larger predictive power for community biomass and measures of biodiversitity effects (40–82% of explained variation) than species richness per se (,1–13% of explained variation). CWM explained a larger amount of variation in community biomass (80%) and net biodiversity effects (70%) than FDQ (36 and 38% of explained variation respectively). FDQ explained similar proportions of variation in complementarity effects (24%, positive relationship) and selection effects (28%, negative relationship) as CWM (27% of explained variation for both complementarity and selection effects), but for all response variables the combination of CWM and FDQ led to significant model improvement compared to a separate consideration of different components of functional trait composition. Effects of FDQ were mainly attributable to diversity in nutrient acquisition and life-history strategies. The large spectrum of traits contributing to positive effects of CWM on biomass production and net biodiversity effects indicated that effects of dominant species were associated with different trait combinations. Conclusions/Significance: Our results suggest that the identification of relevant traits and the relative impacts of functional identity of dominant species and functional diversity are essential for a mechanistic understanding of the role of plant diversity for ecosystem processes such as aboveground biomass production

Fundus-controlled perimetry (microperimetry): Application as outcome measure in clinical trials

YesFundus-controlled perimetry (FCP, also called 'microperimetry') allows for spatially-resolved mapping of visual sensitivity and measurement of fixation stability, both in clinical practice as well as research. The accurate spatial characterization of visual function enabled by FCP can provide insightful information about disease severity and progression not reflected by best-corrected visual acuity in a large range of disorders. This is especially important for monitoring of retinal diseases that initially spare the central retina in earlier disease stages. Improved intra- and inter-session retest-variability through fundus-tracking and precise point-wise follow-up examinations even in patients with unstable fixation represent key advantages of these technique. The design of disease-specific test patterns and protocols reduces the burden of extensive and time-consuming FCP testing, permitting a more meaningful and focused application. Recent developments also allow for photoreceptor-specific testing through implementation of dark-adapted chromatic and photopic testing. A detailed understanding of the variety of available devices and test settings is a key prerequisite for the design and optimization of FCP protocols in future natural history studies and clinical trials. Accordingly, this review describes the theoretical and technical background of FCP, its prior application in clinical and research settings, data that qualify the application of FCP as an outcome measure in clinical trials as well as ongoing and future developments

Voting in clustering and finding the number of clusters

In this paper we present an unsupervised algorithm which performs clustering given a data set and which can also find the number of clusters existing in it. This algorithm consists of two techniques. The first, the voting technique, allows us to combine several runs of clustering algorithms, with the number of clusters predefined, resulting in a common partition. We introduce the idea that there are cases where an input point has a structure with a certain degree of confidence and may belong to more than one cluster with a certain degree of "belongingness". The second part consists of an index measure which receives the results of every voting process for diffrent number of clusters and makes the decision in favor of one. This algorithm is a complete clustering scheme which can be applied to any clustering method and to any type of data set. Moreover, it helps us to overcome instabilities of the clustering algorithms and to improve the ability of a clustering algorithm to find structures in a data set.Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science

On the generation of correlated artificial binary data

The generation of random variates from multivariate binary distributions has not gained as much interest in the literature as, e.g., multivariate normal or Poisson distributions. Binary variables are important in many types of applications. Our main interest is in the segmentation of marketing data, where data come from customer questionnaires with "yes/no" questions. Artificial data provide a valuable tool for the analysis of segmentation tools, because data with known structure can be constructed to mimic situations from the real world (Dolnicar et al. 1998). Questionnaire data can be highly correlated, when several questions covering the same field are likely to be answered similarly by a subject. In this paper we present a computationally fast method to simulate multivariate binary distributions with a given correlation structure. The implementation of the algorithm in R, an implementation of the S statistical language, is described in the appendix.Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science