Information inequalities and Generalized Graph Entropies

In this article, we discuss the problem of establishing relations between information measures assessed for network structures. Two types of entropy based measures namely, the Shannon entropy and its generalization, the R\'{e}nyi entropy have been considered for this study. Our main results involve establishing formal relationship, in the form of implicit inequalities, between these two kinds of measures when defined for graphs. Further, we also state and prove inequalities connecting the classical partition-based graph entropies and the functional-based entropy measures. In addition, several explicit inequalities are derived for special classes of graphs.Comment: A preliminary version. To be submitted to a journa

Experimental Resonance Enhanced Multiphoton Ionization (REMPI) studies of small molecules

Resonance enhanced multiphoton ionization (REMPI) utilizes tunable dye lasers to ionize an atom or molecule by first preparing an excited state by multiphoton absorption and then ionizing that state before it can decay. This process is highly selective with respect to both the initial and resonant intermediate states of the target, and it can be extremely sensitive. In addition, the products of the REMPI process can be detected as needed by analyzing the resulting electrons, ions, fluorescence, or by additional REMPI. This points to a number of exciting opportunities for both basic and applied science. On the applied side, REMPI has great potential as an ultrasensitive, highly selective detector for trace, reactive, or transient species. On the basic side, REMPI affords an unprecedented means of exploring excited state physics and chemistry at the quantum-state-specific level. An overview of current studies of excited molecular states is given to illustrate the principles and prospects of REMPI

Long Gone Lake Wobegon? The State of Investments in University of Minnesota Research

Research and Development/Tech Change/Emerging Technologies,

Agricultural research: a growing global divide?

"Sustained, well-targeted, and effectively used investments in R&D have reaped handsome rewards from improved agricultural productivity and cheaper, higher quality foods and fibers. As we begin a new millennium, the global patterns of investments in agricultural R&D are changing in ways that may have profound consequences for the structure of agriculture worldwide and the ability of poor people in poor counties to feed themselves. This report documents and discusses these changing investment patterns, highlighting developments in the public and private sectors. It revises and carries forward to 2000 data that were previously reported in the 2001 IFPRI Food Policy Report Slow Magic: Agricultural R&D a Century After Mendel. Some past trends are continuing or have come into sharper focus, while others are moving in new directions not apparent in the previous series. In addition, this report illustrates the use of spatial data to analyze spillover prospects among countries or agroecologies and the targeting of R&D to address specific production problems like drought-induced production risks." Authors' PrefaceResearch and development, Agricultural productivity, Investments, Agricultural research, Poverty, Public investment, Private sector, Spatial analysis (Statistics),

Novel topological descriptors for analyzing biological networks

<p>Abstract</p> <p>Background</p> <p>Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information.</p> <p>Results</p> <p>In this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem.</p> <p>Conclusions</p> <p>Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.</p

Information Indices with High Discriminative Power for Graphs

In this paper, we evaluate the uniqueness of several information-theoretic measures for graphs based on so-called information functionals and compare the results with other information indices and non-information-theoretic measures such as the well-known Balaban index. We show that, by employing an information functional based on degree-degree associations, the resulting information index outperforms the Balaban index tremendously. These results have been obtained by using nearly 12 million exhaustively generated, non-isomorphic and unweighted graphs. Also, we obtain deeper insights on these and other topological descriptors when exploring their uniqueness by using exhaustively generated sets of alkane trees representing connected and acyclic graphs in which the degree of a vertex is at most four

Rotationally resolved energy-dispersive photoelectron spectroscopy of H_2O: Photoionization of the C̃(0,0,0) state at 355 nm

Measured and calculated rotationally resolved photoelectron spectra for photoionization of low rotational levels of the C̃^1B_1 Rydberg state of water are reported. This is the first example of rotationally resolved photoionization spectra beyond the special cases of H_2, high-J levels, and threshold spectra. These spectra reveal very nonatomiclike behavior and, surprisingly, the influence of multiple Cooper minima in the photoelectron matrix elements

Spectral analysis of Gene co-expression network of Zebrafish

We analyze the gene expression data of Zebrafish under the combined framework of complex networks and random matrix theory. The nearest neighbor spacing distribution of the corresponding matrix spectra follows random matrix predictions of Gaussian orthogonal statistics. Based on the eigenvector analysis we can divide the spectra into two parts, first part for which the eigenvector localization properties match with the random matrix theory predictions, and the second part for which they show deviation from the theory and hence are useful to understand the system dependent properties. Spectra with the localized eigenvectors can be characterized into three groups based on the eigenvalues. We explore the position of localized nodes from these different categories. Using an overlap measure, we find that the top contributing nodes in the different groups carry distinguished structural features. Furthermore, the top contributing nodes of the different localized eigenvectors corresponding to the lower eigenvalue regime form different densely connected structure well separated from each other. Preliminary biological interpretation of the genes, associated with the top contributing nodes in the localized eigenvectors, suggests that the genes corresponding to same vector share common features.Comment: 6 pages, four figures (accepted in EPL

Entwicklung von Phytophthora-resistentem Zuchtmaterial für den ökologischen Landbau

Die Ansprüche des ökologischen Landbaus an Kartoffelsorten unterscheiden sich zum Teil erheblich von denen des konventionellen Anbaus. Dies ist bedingt durch den alternativen Pflanzenschutz mit mechanischer Unkrautbekämpfung sowie durch eine in der Regel geringere Stickstoffversorgung der Pflanzen. Darum benötigt der ökologische Landbau spezielle Sorten, die neben den vom Verbraucher gewünschten Qualitätsmerkmalen auch eine hohe Widerstandsfähigkeit gegenüber Krankheiten und Schädlingen besitzen, durch schnelle Jugendentwicklung das Unkrautwachstum unterdrücken und eine hohe Nährstoffeffizienz aufweisen. Um neue Zuchtstämme zu schaffen, in denen die im Biolandbau gewünschten Eigenschaften kombiniert vorliegen, etablierte das seit 2012 im „Bundesprogramm Ökologischer Landbau und andere Formen nachhaltiger Landwirtschaft“ geförderte Projekt „Entwicklung von Phytophthora-resistentem Zuchtmaterial für den ökologischen Landbau“ ein Zuchtprogramm speziell für den ökologischen Kartoffelanbau. Für die Züchtung auf geringe Anfälligkeit gegenüber der Kraut- und Knollenfäule, die durch den Oomyceten Phytophthora infestans hervorgerufen wird, wurde auf resistente Kartoffelklone aus der Vorzüchtung des Julius-Kühn Institutes zurückgegriffen. Die untersuchten Zuchtstämme und Vergleichssorten wurden parallel in einem Beobachtungsanbau hinsichtlich weiterer agronomischer und qualitativer Eigenschaften wie Ertragsleistung, Wuchsform, Stärkegehalt, Speise- und Veredelungseignung und Abreifeverhalten untersucht. Weitere Resistenzen sowie gewünschte Qualitätsmerkmale wurden aus modernen Hochleistungssorten deutscher Züchtungsunternehmen und historischen Sorten der IPK Genbank während des Projektverlaufs ins Zuchtmaterial eingebracht. Neben der Evaluierung der Prüfglieder hinsichtlich der Resistenzeigenschaften wurden zur Erweiterung des Basiszuchtmaterials zahlreiche Kreuzungen durchgeführt. Hierbei wurden die Kreuzungseltern gezielt aus den im Projekt geprüften Sorten und Zuchtstämmen ausgewählt. Für die Bewertung und die Selektion der Klone wurde ein partizipativer Züchtungsansatz gewählt und dafür drei ökologisch wirtschaftende, landwirtschaftliche Betriebe ins Projekt eingebunden. Die Betriebsleiter wurden intensiv geschult und führten die Bewertung der Klone und die Selektion der Knollen in enger Zusammenarbeit mit den wissenschaftlichen Mitarbeitern der Institute durch. Ein solches Modell ist bislang in der der deutschen Kartoffelzüchtung einzigartig. Wissenschaftlich begleitet wurden die Züchtungsarbeiten durch eine phänotypische und genotypische Charakterisierung des Ausgangsmaterials und der Zuchtklone. Ziel hierbei war es, Grundlagen für eine molekulare Selektion KF-resistenter Nachkommen zu schaffen. Hierzu wurden an einem Prüfgliedsortiment Assoziationsstudien mit DArT- und SNP-Markern durchgeführt und ausgewertet. Im Projekt wurden am IPK erstmalig zwei Sortimente umfassend genetisch mittels zwei verschiedener Markersysteme zu charakterisiert. Mit den erzielten Ergebnisse konnten Duplikatgruppen innerhalb der Genbankakzessionen aufgedeckt sowie Unstimmigkeiten erkannt und bereinigt werden. Damit wurde das Genbankmanagement verbessert und nutzerorientiert gestaltet. Somit hat das Projekt eine nachhaltigere und intensivere Nutzung pflanzengenetischer Ressourcen ermöglicht. Dadurch ist es Züchtern wie auch Forschungsinstitutionen zukünftig möglich, zielgerichteter auf genetische Ressourcen der Kartoffel zuzugreifen und diese in die eigenen Zuchtprogramme und Forschungsprojekte einzubringen

Connections between Classical and Parametric Network Entropies

This paper explores relationships between classical and parametric measures of graph (or network) complexity. Classical measures are based on vertex decompositions induced by equivalence relations. Parametric measures, on the other hand, are constructed by using information functions to assign probabilities to the vertices. The inequalities established in this paper relating classical and parametric measures lay a foundation for systematic classification of entropy-based measures of graph complexity