New-age elderly in Germany - how to live better with healthy experiences

This study aims to analyze which potential attributes of anti-ageing experiences (goods, services, and experiences) will tend to be associated with subjective happiness; how cognitive age is associated with mindfulness and the influence of mindfulness on subjective happiness. Senior universities were contacted and approached by the research team to conduct the study. The goals of this study were explained to the managers of the senior universities and the survey collected among the people who participate in the activities and are enrolled in the senior universities. To analyze this theme over two hundred (250) questionnaires were distributed during January 2016 in Hamburg. The findings reveal that (i) mindfulness tend to have a positive effect on subjective happiness among elderly consumers, (ii) cognitive age and chronological age are not overlapped and (iii) the way elderly consumers perceive the anti-aging products and experiences may be correlated with subjective happiness.info:eu-repo/semantics/publishedVersio

Multilevel modelling of mechanical properties of textile composites: ITOOL Project

The paper presents an overview of the multi-level modelling of textile composites in the ITOOL project, focusing on the models of textile reinforcements, which serve as a basis for micromechanical models of textile composites on the unit cell level. The modelling is performed using finite element analysis (FEA) or approximate methods (method of inclusions), which provide local stiffness and damage information to FEA of composite part on the macro-level

A classification-based framework for predicting and analyzing gene regulatory response

BACKGROUND: We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences ("motifs") in the gene's regulatory region and the expression levels of regulators such as transcription factors in the experiment ("parents"). GeneClass formulates the learning task as a classification problem — predicting +1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternating decision tree, a margin-based generalization of a decision tree. METHODS: In the current work, we introduce a new, robust version of the GeneClass algorithm that increases stability and computational efficiency, yielding a more scalable and reliable predictive model. The improved stability of the prediction tree enables us to introduce a detailed post-processing framework for biological interpretation, including individual and group target gene analysis to reveal condition-specific regulation programs and to suggest signaling pathways. Robust GeneClass uses a novel stabilized variant of boosting that allows a set of correlated features, rather than single features, to be included at nodes of the tree; in this way, biologically important features that are correlated with the single best feature are retained rather than decorrelated and lost in the next round of boosting. Other computational developments include fast matrix computation of the loss function for all features, allowing scalability to large datasets, and the use of abstaining weak rules, which results in a more shallow and interpretable tree. We also show how to incorporate genome-wide protein-DNA binding data from ChIP chip experiments into the GeneClass algorithm, and we use an improved noise model for gene expression data. RESULTS: Using the improved scalability of Robust GeneClass, we present larger scale experiments on a yeast environmental stress dataset, training and testing on all genes and using a comprehensive set of potential regulators. We demonstrate the improved stability of the features in the learned prediction tree, and we show the utility of the post-processing framework by analyzing two groups of genes in yeast — the protein chaperones and a set of putative targets of the Nrg1 and Nrg2 transcription factors — and suggesting novel hypotheses about their transcriptional and post-transcriptional regulation. Detailed results and Robust GeneClass source code is available for download from

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Modeling and verifying a broad array of network properties

Motivated by widely observed examples in nature, society and software, where groups of already related nodes arrive together and attach to an existing network, we consider network growth via sequential attachment of linked node groups, or graphlets. We analyze the simplest case, attachment of the three node V-graphlet, where, with probability alpha, we attach a peripheral node of the graphlet, and with probability (1-alpha), we attach the central node. Our analytical results and simulations show that tuning alpha produces a wide range in degree distribution and degree assortativity, achieving assortativity values that capture a diverse set of many real-world systems. We introduce a fifteen-dimensional attribute vector derived from seven well-known network properties, which enables comprehensive comparison between any two networks. Principal Component Analysis (PCA) of this attribute vector space shows a significantly larger coverage potential of real-world network properties by a simple extension of the above model when compared against a classic model of network growth.Comment: To appear in Europhysics Letter

Integrating tropical research into biology education is urgently needed

Understanding tropical biology is important for solving complex problems such as climate change, biodiversity loss, and zoonotic pandemics, but biology curricula view research mostly via a temperatezone lens. Integrating tropical research into biology education is urgently needed to tackle these issues. The tropics are engines of Earth systems that regulate global cycles of carbon and water, and are thus critical for management of greenhouse gases. Compared with higher-latitude areas, tropical regions contain a greater diversity of biomes, organisms, and complexity of biological interactions. The tropics house the majority of the world’s human population and provide important global commodities from species that originated there: coffee, chocolate, palm oil, and species that yield the cancer drugs vincristine and vinblastine. Tropical regions, especially biodiversity hotspots, harbor zoonoses, thereby having an important role in emerging infectious diseases amidst the complex interactions of global environmental change and wildlife migration [1]. These well-known roles are oversimplified, but serve to highlight the global biological importance of tropical systems. Despite the importance of tropical regions, biology curricula worldwide generally lack coverage of tropical research. Given logistical, economic, or other barriers, it is difficult for undergraduate biology instructors to provide their students with field-based experience in tropical biology research in a diverse range of settings, an issue exacerbated by the Coronavirus Disease 2019 (COVID-19) pandemic. Even in the tropics, field-based experience may be limited to home regions. When tropical biology is introduced in curricula, it is often through a temperate- zone lens that does not do justice to the distinct ecosystems, sociopolitical histories, and conservation issues that exist across tropical countries and regions [2]. The tropics are often caricatured as distant locations known for their remarkable biodiversity, complicated species interactions, and unchecked deforestation. This presentation, often originating from a colonial and culturally biased perspective, may fail to highlight the role of tropical ecosystems in global environmental and social challenges that accompany rising temperatures, worldwide biodiversity loss, zoonotic pandemics, and the environmental costs of ensuring food, water, and other ecosystem services for humans [3]

Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure

Highlights from the Pierre Auger Observatory

The Pierre Auger Observatory is the world's largest cosmic ray observatory. Our current exposure reaches nearly 40,000 km$^2$ str and provides us with an unprecedented quality data set. The performance and stability of the detectors and their enhancements are described. Data analyses have led to a number of major breakthroughs. Among these we discuss the energy spectrum and the searches for large-scale anisotropies. We present analyses of our X$_{max}$ data and show how it can be interpreted in terms of mass composition. We also describe some new analyses that extract mass sensitive parameters from the 100% duty cycle SD data. A coherent interpretation of all these recent results opens new directions. The consequences regarding the cosmic ray composition and the properties of UHECR sources are briefly discussed.Comment: 9 pages, 12 figures, talk given at the 33rd International Cosmic Ray Conference, Rio de Janeiro 201

Dissecting complex transcriptional responses using pathway-level scores based on prior information

<p>Abstract</p> <p>Background</p> <p>The genomewide pattern of changes in mRNA expression measured using DNA microarrays is typically a complex superposition of the response of multiple regulatory pathways to changes in the environment of the cells. The use of prior information, either about the function of the protein encoded by each gene, or about the physical interactions between regulatory factors and the sequences controlling its expression, has emerged as a powerful approach for dissecting complex transcriptional responses.</p> <p>Results</p> <p>We review two different approaches for combining the noisy expression levels of multiple individual genes into robust pathway-level differential expression scores. The first is based on a comparison between the distribution of expression levels of genes within a predefined gene set and those of all other genes in the genome. The second starts from an estimate of the strength of genomewide regulatory network connectivities based on sequence information or direct measurements of protein-DNA interactions, and uses regression analysis to estimate the activity of gene regulatory pathways. The statistical methods used are explained in detail.</p> <p>Conclusion</p> <p>By avoiding the thresholding of individual genes, pathway-level analysis of differential expression based on prior information can be considerably more sensitive to subtle changes in gene expression than gene-level analysis. The methods are technically straightforward and yield results that are easily interpretable, both biologically and statistically.</p