Marginal and simultaneous predictive classification using stratified graphical models

An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution. Several such rules have been recently considered and their asymptotic behavior has been characterized under the assumption that the observed features or variables used for building a classifier are conditionally independent given a simultaneous labeling of both the training samples and those from an unknown origin. Here we extend the theoretical results to predictive classifiers acknowledging feature dependencies either through graphical models or sparser alternatives defined as stratified graphical models. We show through experimentation with both synthetic and real data that the predictive classifiers encoding dependencies have the potential to substantially improve classification accuracy compared with both standard discriminative classifiers and the predictive classifiers based on solely conditionally independent features. In most of our experiments stratified graphical models show an advantage over ordinary graphical models.     

Synthesis of multivalent Streptococcus suis adhesion inhibitors by enzymatic cleavage of polygalacturonic acid and 'click' conjugation

A galabiose disaccharide building block was synthesized by an efficient pectinase cleavage of polygalacturonic acid and subsequent chemical functional group transformations. Besides the disaccharide, the corresponding trisaccharide was also obtained and modified. The compounds were subsequently conjugated to dendrimers with up to eight end groups using 'click' chemistry. The compounds were evaluated as inhibitors of adhesion of the pathogen Streptococcus suis in a hemagglutination assay and strong inhibition was observed for the tetra- and octavalent galabiose compound with MIC values in the low nanomolar range. The corresponding octavalent trisaccharide was a ca. 20-fold weaker inhibitor.     

High-performance liquid chromatography/electrospray ionization tandem mass spectrometry for characterization of enzymatic degradation of 2,2'-bis(2-oxazoline)-linked poly-epsilon-caprolactone

This paper describes a straightforward and rapid on-line characterization using high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC/ESI-MS(n)) of the enzymatic degradation products of 2,2'-bis(2-oxazoline)-linked poly-epsilon-caprolactone (PCL-O). These new PCL-O polymers are expected to be used in a variety of pharmaceutical and biomedical applications since they are degraded enzymatically by surface erosion. PCL-O was polymerized in a three-step reaction and characterized by (1)H-NMR and size-exclusion chromatography (SEC). Solvent cast polymer films were exposed to enzymatic degradation in phosphate buffer (pH 7.5, 1% pancreatin). The enzymatic degradation of the polymer produced a wide variety of water-soluble oligomers which were separated and identified by HPLC/ESI-MS(n). Optimization of the gradient HPLC method resulted in effective separation of the oligomers. Furthermore, specific structures of the oligomers were clearly identified by tandem mass spectrometry. According to these results, ester bonds seem to be most sensitive to enzymatic degradation and, correspondingly, pancreatic lipase seems to be mainly responsible for the enzymatic erosion of the PCL-O films. This novel mass spectrometric method provides important knowledge about the enzymatic degradation process and structure of the polymer which is difficult to ascertain by other conventional methods.     

Boron nitride nanotube-based amphiphilic hybrid nanomaterials for superior encapsulation of hydrophobic cargos

We report an organic-inorganic hybrid core-shell nanomaterial obtained by conjugation of an amphiphilic monomethoxy-poly(ethylene glycol)-b-poly(epsilon-caprolactone) diblock copolymer to hydroxylated boron nitride nanotubes (BNNTs). The extent of copolymer grafting reached 64% w/w, an exceptionally high value. The hybrid materials exhibit excellent physical stability in water and an outstanding loading capacity (31.3% w/w) for curcumin, a hydrophobic drug. Moreover, they present good compatibility with the Caco2 cell line, a model of intestinal epithelium. Our findings demonstrate the potential of multifunctional hybrid BNNTs to serve as a platform for complex amphiphilic nanoparticle architectures with improved features.     

Bayesian spatial modeling of genetic population structure

Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g., due to geographical barriers, stochastic forces reshape certain DNA characteristics of the individuals over generations such that they reflect the restricted migration and mating/reproduction patterns. Such populations are typically termed as genetically structured and they may be statistically represented in terms of several clusters between which DNA variations differ clearly from each other. When detailed knowledge of the ancestry of a natural population is lacking, the DNA characteristics of a sample of current generation individuals often provide a wealth of information in this respect. Several statistical approaches to model-based clustering of such data have been introduced, and in particular, the Bayesian approach to modeling the genetic structure of a population has attained a vivid interest among biologists. However, the possibility of utilizing spatial information from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only very recently. While the standard Bayesian hierarchical modeling techniques through Markov chain Monte Carlo simulation provide flexible means for describing even subtle patterns in data, they may also result in computationally challenging procedures in practical data analysis. Here we develop a method for modeling the spatial genetic structure using a combination of analytical and stochastic methods. We achieve this by extending a novel theory of Bayesian predictive classification with the spatial information available, described here in terms of a colored Voronoi tessellation over the sample domain. Our results for real and simulated data sets illustrate well the benefits of incorporating spatial information to such an analysis.     

The effect of facet reflections in index-coupled distributed feedback lasers with coated facets

The effect of facet reflections and different grating parameters on side-mode suppression ratio in index-coupled distributed feedback lasers without a phase-shift section is analyzed. The effect of uncontrollable facet positions on side-mode suppression ratio is studied when the grating coupling coefficient, the grating filling factor, the grating order, the device length and the facet reflectivities are varied. The single-mode device yield and the facet reflectivities needed for achieving a high yield are evaluated, the reflectivity of the anti-reflection coated facet is optimized as a function of the coupling strength and the effect of facet reflections on the other laser characteristics is shown.     

Optical modeling of laterally-corrugated ridge-waveguide gratings

The paper presents some model improvements for the optical simulation of laterally-corrugated ridge-waveguide distributed feedback lasers. Simulation results are discussed and design principles for achieving single-longitudinal-mode operation are outlined. The effects of the laterally-corrugated ridge geometry both on the coupling coefficient and on the Bragg wavelength of different transverse modes are presented. The improved modeling has been used to design 980 nm distributed feedback lasers with laterally-corrugated ridge-waveguide third-order gratings. The lasers fabricated using nanoimprint lithography exhibited single-mode operation with 50 dB side-mode suppression ratio.     

Optical multi-sensor for simultaneous measurement of absorbance,turbidity, and fluorescence of a liquid

We have built a novel optical multi-sensor apparatus (MSA) for the measurement of light absorption, scattering and fluorescence either separately or simultaneous from a liquid. From the measured data it is possible to calculate different optical properties of liquid samples, such as absorbance and turbidity. The MSA is a portable device which is an advantage considering different field applications. The MSA was tested in laboratory conditions using well-known reference liquids. Data obtained with the MSA were compared with data obtained with a commercial spectrophotometer, fluorometer and a nephelometer, respectively. Relatively good correlations between the data of MSA and the three commercial devices were found. It is proposed that MSA is a potential device for monitoring changes of process liquids and liquid products in industry.     

Building the quality into pellet manufacturing environment--feasibility study and validation of an in-line quantitative near infrared (NIR) method

The present study focuses on the implementation of an in-line quantitative near infrared (NIR) spectroscopic method for determining the active content of pharmaceutical pellets. The first aim was to non-invasively interface a dispersive NIR spectrometer with four realistic particle streams existing in the pellets manufacturing environment. Regardless of the particle stream characteristics investigated, NIR together with Principal Component Analysis (PCA) was able to classify the samples according to their active content. Further, one of these particle stream interfaces was non-invasively investigated with a FT-NIR spectrometer. A predictive model based on Partial Least Squares (PLS) regression was able to determine the active content of pharmaceutical pellets. The NIR method was finally validated with an external validation set for an API concentration range from 80 to 120% of the targeted active content. The prediction error of 0.9% (root mean standard error of prediction, RMSEP) was low, indicating the accuracy of the NIR method. The accuracy profile on the validation results, an innovative approach based on tolerance intervals, demonstrated the actual and future performance of the in-line NIR method. Accordingly, the present approach paves the way for real-time release-based quality system.