iProLINK: an integrated protein resource for literature mining

The exponential growth of large-scale molecular sequence data and of the PubMed scientific literature has prompted active research in biological literature mining and information extraction to facilitate genome/proteome annotation and improve the quality of biological databases. Motivated by the promise of text mining methodologies, but at the same time, the lack of adequate curated data for training and benchmarking, the Protein Information Resource (PIR) has developed a resource for protein literature mining—iProLINK (integrated Protein Literature INformation and Knowledge). As PIR focuses its effort on the curation of the UniProt protein sequence database, the goal of iProLINK is to provide curated data sources that can be utilized for text mining research in the areas of bibliography mapping, annotation extraction, protein named entity recognition, and protein ontology development. The data sources for bibliography mapping and annotation extraction include mapped citations (PubMed ID to protein entry and feature line mapping) and annotation-tagged literature corpora. The latter includes several hundred abstracts and full-text articles tagged with experimentally validated post-translational modifications (PTMs) annotated in the PIR protein sequence database. The data sources for entity recognition and ontology development include a protein name dictionary, word token dictionaries, protein name-tagged literature corpora along with tagging guidelines, as well as a protein ontology based on PIRSF protein family names. iProLINK is freely accessible at http://pir.georgetown.edu/iprolink, with hypertext links for all downloadable files.     

Tandem solid-phase extraction of atrazine ozonation products in water

The preconcentration of aqueous solutions containing atrazine degradation products was investigated using solid-phase extraction on octadecyl and cation-exchanger silica phases. The retention and elution steps were studied and evaluated separately in order to define and optimize the critical experimental parameters involved. A strategy which combines sequentially both phases is proposed to fractionate compounds into two groups of increasing polarities: firstly, the native pesticide, hydroxyatrazine and most chlorotriazines on octadecyl support, and secondly monodealkylated hydroxytriazines, ammeline and ammelide on cation-exchanger. This tandem procedure was successfully applied for analysing and quantifying atrazine ozonation products and its efficiency demonstrated using [U-ring 14C]-labelled atrazine experiments.     

Discussion of “Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue”

This paper, “Multivariate Dynamic Modeling for Bayesian Forecasting of Business Revenue,” proposes a novel Bayesian approach based on dynamic linear models to share information from different sectors, LSG (Local Store Group), and item category, through the use of auxiliary information (the discount information). The authors demonstrate the feasibility of parallel computing with multiple item categories, making the Bayesian method highly scalable. The proposed method in the paper should have wide applicability in inventory and revenue management. We suggest in this discussion potential areas for further development.     

Synthesis and binding properties of divalent and trivalent clusters of the Lewis a disaccharide moiety to Pseudomonas aeruginosa lectin PA-IIL

The synthesis of oligomeric glycocomimetics has been performed for targeting the Pseudomonas aeruginosa PA-IIL lectin, which is of therapeutical interest for anti-adhesive treatment. The disaccharide alpha-L-Fucp-(1-->4)-beta-D-GlcNAc, which is a high-affinity ligand of the lectin, has been coupled to dimeric and trimeric linkers with various lengths and geometries. A series of linear dimers displayed an efficient clustering effect and a very strong affinity, with a lower dissociation constant of 90 nM. The trimeric compound was less efficient in inhibition assays but displayed high affinity in solution. Titration microcalorimetry and molecular modeling allowed in-depth analysis and rationalization of the binding data. These glycoclusters could act by crosslinking the lectins present on the surface of bacteria and therefore interfere with host recognition or biofilm formation.     

A rapid method for quantitatively estimating metabolites in human plasma in the absence of synthetic standards using a combination of liquid chromatography/mass spectrometry and radiometric detection

An approach to estimating the levels of drug-related metabolites in human plasma in the absence of synthesized chemical standards has been developed. High-performance liquid chromatography/mass spectrometry (LC/MS) in combination with radiometric detection was used in this method. Biologically derived [(14)C] metabolites from preclinical in vitro and in vivo matrices are used as [(14)C] metabolite standards and their concentrations in matrices are calculated based on the corresponding radioactivity. The amount of drug-related metabolites in human plasma samples can be estimated by determining relative MS responses of metabolites between plasma and [(14)C] metabolite standards, and using the calculated concentrations of metabolite standards as calibrants. An example for the estimation of metabolites in human plasma was used to demonstrate the utility of this methodology.     

Time-lag focusing and cation attachment in the analysis of synthetic polymers by matrix-assisted laser desorption/ionization-time-of-flight-mass spectrometry

Ultraviolet matrix-assisted laser desorption/ionization-mass spectrometry has been employed with time-lag focusing to explore its utility for the characterization of synthetic polymers with broad distributions. Mixtures of five polymer standards with narrow molecular weight distributions were analyzed. The spectra were found to be broadly those expected for three different types of polymer systems—poly(styrene), poly(methyl methacrylate), and poly(ethylene glycol)—when equimass mixtures were used. Large changes in the apparent molecular weight distribution of poly(ethylene terephthalate) were observed when the cation was varied. The shift in the envelope was found to be related to the size and the ability of the oligomers to solvate the cation.     

Immunosuppressive Effects of Silicon Phthalocyanine Photodynamic Therapy

The purpose of this study was to determine if silicon phthalocyanine 4 (Pc 4), a second-generation photosensitizer being evaluated for the photodynamic therapy (PDT) of solid tumors, was immunosuppressive. Mice treated with Pc 4 PDT 3 days before dinitrofluorobenzene sensitization showed significant suppression of their cell-mediated immune response when compared to mice that were not exposed to PDT. The response was dose dependent, required both Pc 4 and light and occurred at a skin site remote from that exposed to the laser. The immunosuppression could not be reversed by in vivo pre-treatment of mice with antibodies to tumor necrosis factor-alpha or interleukin-10. These results provide evidence that induction of cell-mediated immunity is suppressed after Pc 4 PDT. Strategies that prevent PDT-mediated immunosuppression may therefore enhance the efficacy of this therapeutic modality.     

Ordinal Time Series Forecasting of the Air Quality Index

This research models and forecasts daily AQI (air quality index) levels in 16 cities/counties of Taiwan, examines their AQI level forecast performance via a rolling window approach over a one-year validation period, including multi-level forecast classification, and measures the forecast accuracy rates. We employ statistical modeling and machine learning with three weather covariates of daily accumulated precipitation, temperature, and wind direction and also include seasonal dummy variables. The study utilizes four models to forecast air quality levels: (1) an autoregressive model with exogenous variables and GARCH (generalized autoregressive conditional heteroskedasticity) errors; (2) an autoregressive multinomial logistic regression; (3) multi-class classification by support vector machine (SVM); (4) neural network autoregression with exogenous variable (NNARX). These models relate to lag-1 AQI values and the previous day’s weather covariates (precipitation and temperature), while wind direction serves as an hour-lag effect based on the idea of nowcasting. The results demonstrate that autoregressive multinomial logistic regression and the SVM method are the best choices for AQI-level predictions regarding the high average and low variation accuracy rates.