Multiple clusterings of heterogeneous information networks

Machine Learning - Traditional clustering algorithms focus on a single clustering result; as such, they cannot explore potential diverse patterns of complex real world data. To deal with this...     

Unusual essential oils with aromatic properties

Summary The essential oil obtained by steam distillation of cinchona bark (Cinchona spp.,Rubiaceae family) — a plant well known for its pharmacological properties, also employed in the food industry, especially in making liqueurs - was investigated. The oil, of completely unknown chemical composition, was analyzed by GLC and GLC/MS after separation on a silica gel column, and one hundred components were identified. The presence of 2-hexyl-3-methylmaleic anhydride is rather unusual.

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Ungewöhnliche ätherische Öle mit aromatischen Eigenschaften. Die flüchtigen Aromastoffe der China-Rinde

Zusammenfassung Es wurde ätherisches Öl analysiert, das durch Wasserdampfdestillation von Chinarinde (Cinchona spp. derRubiaceae-Familie) gewonnen wurde. Diese Pflanze ist aufgrund ihrer pharmakologischen Eigenschaften bekannt und wird auch in der Lebensmittelindustrie verwendet, ganz besonders für die Likör- und Magenbitterherstellung. Die chemische Zusammensetzung dieses Öls war unbekannt. Nach Abtrennung auf einer Kieselgelsäule wurden im Öl mit GLC und GLC/MS etwa 100 Verbindungen identifiziert. Das Vorhandensein von 2-Hexyl-3-methyl-maleinsäureanhydrid ist ziemlich ungewöhnlich.

     

Kernel ridge regression for out-of-sample mapping in supervised manifold learning

Manifold learning methods for unsupervised nonlinear dimensionality reduction have proven effective in the visualization of high dimensional data sets. When dealing with classification tasks, supervised extensions of manifold learning techniques, in which class labels are used to improve the embedding of the training points, require an appropriate method for out-of-sample mapping.In this paper we propose multi-output kernel ridge regression (KRR) for out-of-sample mapping in supervised manifold learning, in place of general regression neural networks (GRNN) that have been adopted by previous studies on the subject. Specifically, we consider a supervised agglomerative variant of Isomap and compare the performance of classification methods when the out-of-sample embedding is based on KRR and GRNN, respectively. Extensive computational experiments, using support vector machines and k-nearest neighbors as base classifiers, provide statistical evidence that out-of-sample mapping based on KRR consistently dominates its GRNN counterpart, and that supervised agglomerative Isomap with KRR achieves a higher accuracy than direct classification methods on most data sets.     

15‐Methylene‐Eburnamonine Kills Leukemic Stem Cells and Reduces Engraftment in a Humanized Bone Marrow Xenograft Mouse Model of Leukemia

Recent studies suggest that leukemia stem cells (LSCs) play a critical role in the initiation, propagation, and relapse of leukemia. Herein we show that (?)‐15‐methylene‐eburnamonine, a derivative of the alkaloid (?)‐eburnamonine, is cytotoxic against acute and chronic lymphocytic leukemias (ALL and CLL) and acute myelogenous leukemia (AML). The agent also decreases primary LSC frequency in vitro. The cytotoxic effects appear to be mediated via the oxidative stress pathways. Furthermore, we show that the compound kills AML, ALL, and CLL stem cells. By the use of a novel humanized bone marrow murine model of leukemia (huBM/NSG), it was found to decrease progenitor cell engraftment.     

Special Issue: Water Engineering and Management in a Changing Environment

The paper is the introduction to the special issue Water Engineering and Management in a Changing Environment which presents a set of the most innovative contributions at the EWRA Symposium, held in Catania, Italy on 2011.     

Risks and benefits related to alimentary exposure to xenoestrogens

Xenoestrogens are widely diffused in the environment and in food, thus a large portion of human population worldwide is exposed to them. Among alimentary xenoestrogens, phytoestrogens (PhyEs) are increasingly being consumed because of their potential health benefits, although there are also important risks associated to their ingestion. Furthermore, other xenoestrogens that may be present in food are represented by other chemicals possessing estrogenic activities, that are commonly defined as endocrine disrupting chemicals (EDCs). EDCs pose a serious health concern since they may cause a wide range of health problems, starting from pre-birth till adult lifelong exposure. We herein provide an overview of the main classes of xenoestrogens, which are classified on the basis of their origin, their structures and their occurrence in the food chain. Furthermore, their either beneficial or toxic effects on human health are discussed in this review.     

Docosahexaenoic Acid Levels in Blood and Metabolic Syndrome in Obese Children: Is There a Link?

Prevalence of metabolic syndrome is increasing in the pediatric population. Considering the different existing criteria to define metabolic syndrome, the use of the International Diabetes Federation (IDF) criteria has been suggested in children. Docosahexaenoic acid (DHA) has been associated with beneficial effects on health. The evidence about the relationship of DHA status in blood and components of the metabolic syndrome is unclear. This review discusses the possible association between DHA content in plasma and erythrocytes and components of the metabolic syndrome included in the IDF criteria (obesity, alteration of glucose metabolism, blood lipid profile, and blood pressure) and non-alcoholic fatty liver disease in obese children. The current evidence is inconsistent and no definitive conclusion can be drawn in the pediatric population. Well-designed longitudinal and powered trials need to clarify the possible association between blood DHA status and metabolic syndrome.     

A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules

A neural networks approach is applied to the derivation of the operating rules of an irrigation supply reservoir. Operating rules are determined as a two step process: first, a dynamic programming technique, which determines the optimal releases byminimizing the sum of squared deficits, assumed as objective function, subject to various constraints is applied. Then, theresulting releases from the reservoir are expressed as a functionof significant variables by neural networks. Neural networks aretrained on a long period, including severe drought events, andthe operation rules so determined are validated on a differentshorter period. The behaviour of different operating rules is assessed by simulating reservoir operation and by computing several performance indices of the reservoir and crop yield through a soil water balance model. Results show that operating rules based on an optimization with constraints resembling real system operation criteria lead to a good performance both in normal and in drought periods, reducing maximum deficits and water spills.