Kernel pooled local subspaces for classification.

We investigate the use of subspace analysis methods for learning low-dimensional representations for classification. We propose a kernel-pooled local discriminant subspace method and compare it against competing techniques: kernel principal component analysis (KPCA) and generalized discriminant analysis (GDA) in classification problems. We evaluate the classification performance of the nearest-neighbor rule with each subspace representation. The experimental results using several data sets demonstrate the effectiveness and performance superiority of the kernel-pooled subspace method over competing methods such as KPCA and GDA in some classification problems.     

Weighted-object ensemble clustering: methods and analysis

In recent years, research on provenance has increased exponentially, and such studies in the field of business process monitoring have been especially remarkable. Business process monitoring deals with recording information about the actual execution of processes to then extract valuable knowledge that can be utilized for business process quality improvement. In prior research, we developed an occurrence-centric approach built on our notion of occurrence that provides a holistic perspective of system dynamics. Based on this concept, more complex structures are defined herein, namely Occurrence Base (OcBase) and Occurrence Management System (OcSystem), which serve as scaffolding to develop business process monitoring systems. This paper focuses primarily on the critical provenance task of extracting valuable knowledge from such systems by proposing an Occurrence Query Framework that includes the definition of an Occurrence Base Metamodel and an Occurrence Query Language based on this metamodel. Our framework provides a way of working for the construction of business process monitoring systems that are provenance aware. As a proof of concept, a tool implementing the various components of the framework is presented. This tool has been tested against a real system in the context of biobanks.     

Unusual essential oils with aromatic properties III. Volatile components of gentian roots

Summary The essential oil obtained by steam distillation of gentian roots (Gentiana lutea L.,Gentianaceae family) - a plant well know for its pharmacological properties and employed in the food industry, especially in making liqueurs - was investigated. The oil, after separation into fractions on a silica gel column, was investigated by GLC and GLC/MS. Limonene (34.68%), linalool, carvacrol, cis-linalyl oxide and aterpineol were the main components of the oil, totalling 50.04%. Several other minor components have been identified and are listed.

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Ungewöhnliche etherische Öle mit aromatischen Eigenschaften III. Flüchtige Aromastoffe der Enzianwurzeln

Zusammenfassung Es wurde etherisches Öl analysiert, das durch Wasserdampfdestillation aus Enzianwurzeln (Gentiana lutea L.,Gentianaceae) gewonnen wurde. Die Pflanze ist aufgrund ihrer pharmakologischen Eigenschaften bekannt und wird auch in der Lebensmittelindustrie verwendet, ganz besonders für die Likör-und Magenbitterherstellung. Das Öl wurde durch Säulenchromatographie an Kieselgel mittels GLC und GC/MS analysiert. Limonen (34,68%), Linalool, Carvacrol,cis-Linalyloxid und -Terpineol bilden 50,04% des Öls. Darüber hinaus wurden weitere Bestandteile identifiziert.

     

A comparative study of nonlinear manifold learning methods for cancer microarray data classification

The paper presents an empirical comparison of the most prominent nonlinear manifold learning techniques for dimensionality reduction in the context of high-dimensional microarray data classification. In particular, we assessed the performance of six methods: isometric feature mapping, locally linear embedding, Laplacian eigenmaps, Hessian eigenmaps, local tangent space alignment and maximum variance unfolding. Unlike previous studies on the subject, the experimental framework adopted in this work properly extends to dimensionality reduction the supervised learning paradigm, by regarding the test set as an out-of-sample set of new points which are excluded from the manifold learning process. This in order to avoid a possible overestimate of the classification accuracy which may yield misleading comparative results. The different empirical approach requires the use of a fast and effective out-of-sample embedding method for mapping new high-dimensional data points into an existing reduced space. To this aim we propose to apply multi-output kernel ridge regression, an extension of linear ridge regression based on kernel functions which has been recently presented as a powerful method for out-of-sample projection when combined with a variant of isometric feature mapping. Computational experiments on a wide collection of cancer microarray data sets show that classifiers based on Isomap, LLE and LE were consistently more accurate than those relying on HE, LTSA and MVU. In particular, under different experimental conditions LLE-based classifier emerged as the most effective method whereas Isomap algorithm turned out to be the second best alternative for dimensionality reduction.     

Synthesis of an oxetane‐functionalized hemispiroorthocarbonate used as a low‐shrinkage additive in the cationic ultraviolet curing of oxetane monomers

The synthesis of an hemispiroorthocarbonate functionalized with an oxetane group is reported. The obtained monomer was used as a slow shrinkable additive in the cationic ultraviolet curing of a commercially available dioxetane resin. We evidenced polymer network flexibilization by increasing the oxetane‐functionalized hemispiroorthocarbonate content in the photocurable formulation. It was demonstrated that spiroorthocarbonate acted as a shrinkage reduction additive and reached expansion on volume after polymerization in the presence of 50 wt % of the functionalized spiroorthocarbonate. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

Selenoglutathione-Mediated Rescue of Kinetically Trapped Intermediates in Oxidative Protein Folding

Selenoglutathione has been shown to have considerable potential as a catalyst of oxidative protein folding. Here we examine how this reagent modulates the folding pathway of bovine pancreatic trypsin inhibitor (BPTI) and show that the diselenide increases the efficiency of this process primarily by accelerating the conversion of a kinetically trapped folding intermediate.     

Composition and Physical Stability of the Colloidal Dispersion in Veiled Virgin Olive Oil

Veiled virgin olive oil (VOO) samples of nine different olive cultivars are chosen to have a wide range of physicochemical and biological properties of colloidal dispersions. The contents of proteins and phospholipids range from 40 to 190 mg kg⁻¹ and from 70 to 200 mg kg⁻¹, respectively. The effect of lab-scale centrifugation on cloudy appearance is studied measuring the decrease of turbidity grade values. The time to obtain unveiled oils (20 NTU) is modeled by a logistic equation, and a clear relationship between the initial water content and the above time is observed with a different trend between two groups of the VOO samples. Four VOO samples are selected to study the aggregation phenomena of microdroplets of water, pulp particles, and olive stone fragments via optical microscopy and dynamic light scattering during lab-scale gravity sedimentation. All VOOs are unstable with the cloudiness disappearing within the 230 days of investigation due to an overall diameter increase of cloudy components which is modeled by a power-law equation. The VOO samples, characterized by both small diameter values of dispersed components (150–250 nm) and high values of water content, show the fastest aggregation kinetics, but they have the longest time of cloudiness stability. Practical Applications: Water content and size distribution of VOO cloudy components can be key factors to control the colloidal stability. If removal of cloudy appearance is required, centrifugation can be applied to obtain a fast oil clarification which shows a power law relationship of water content with time. Instead, if physical stability of the colloidal dispersion is required, the aggregation phenomena should be slow down through VOO processing to obtain small diameters of the cloudy components. Tuning both the water content and dispersed phase diameter in the VOO can be the first step towards the control of phenomena related to the colloidal dispersion for every olive oil processing organization, above and beyond the simple removal of cloudy appearance by filtration.     

Synthesis of Fluorescent PMMA‐Based Nanoparticles

The synthesis of PMMA‐based nanoparticles (NPs) covalently labeled with a fluorescent dye is investigated for imaging applications such as cell uptake and biodistribution. Batch emulsion polymerization (BEP) and monomer‐starved semi‐batch emulsion polymerization (MSSEP) are adopted using SDS. Fluorescent properties are added to these NPs using Rhodamine‐B (RhB) as a fluorescent dye covalently bonded to 2‐hyroxyethyl‐acrylate. The resulting HEMA‐RhB monomer is copolymerized with MMA via BEP and MSSEP to synthesize fluorescent NPs. Subsequently, SDS is substituted with a biocompatible surfactant, Tween80, through ionic‐exchange resins. ζ‐Potential measurements confirmed the complete surfactant exchange that leads to biocompatible fluorescent NPs with tunable size and narrow size distribution.

     

Decoration of Nanovesicles with pH (Low) Insertion Peptide (pHLIP) for Targeted Delivery

Acidity at surface of cancer cells is a hallmark of tumor microenvironments, which does not depend on tumor perfusion, thus it may serve as a general biomarker for targeting tumor cells. We used the pH (low) insertion peptide (pHLIP) for decoration of liposomes and niosomes. pHLIP senses pH at the surface of cancer cells and inserts into the membrane of targeted cells, and brings nanomaterial to close proximity of cellular membrane. DMPC liposomes and Tween 20 or Span 20 niosomes with and without pHLIP in their coating were fully characterized in order to obtain fundamental understanding on nanocarrier features and facilitate the rational design of acidity sensitive nanovectors. The samples stability over time and in presence of serum was demonstrated. The size, ζ-potential, and morphology of nanovectors, as well as their ability to entrap a hydrophilic probe and modulate its release were investigated. pHLIP decorated vesicles could be useful to obtain a prolonged (modified) release of biological active substances for targeting tumors and other acidic diseased tissues.