Mass spectrometry-based methods for identifying oxidized proteins in disease:advances and challenges

Many inflammatory diseases have an oxidative aetiology, which leads to oxidative damage to biomolecules, including proteins. It is now increasingly recognized that oxidative post-translational modifications (oxPTMs) of proteins affect cell signalling and behaviour, and can contribute to pathology. Moreover, oxidized proteins have potential as biomarkers for inflammatory diseases. Although many assays for generic protein oxidation and breakdown products of protein oxidation are available, only advanced tandem mass spectrometry approaches have the power to localize specific oxPTMs in identified proteins. While much work has been carried out using untargeted or discovery mass spectrometry approaches, identification of oxPTMs in disease has benefitted from the development of sophisticated targeted or semi-targeted scanning routines, combined with chemical labeling and enrichment approaches. Nevertheless, many potential pitfalls exist which can result in incorrect identifications. This review explains the limitations, advantages and challenges of all of these approaches to detecting oxidatively modified proteins, and provides an update on recent literature in which they have been used to detect and quantify protein oxidation in disease.

EPCIS Event-Based traceability in pharmaceutical supply chains via automated generation of linked pedigrees

In this paper we show how event processing over semantically annotated streams of events can be exploited, for implementing tracing and tracking of products in supply chains through the automated generation of linked pedigrees. In our abstraction, events are encoded as spatially and temporally oriented named graphs, while linked pedigrees as RDF datasets are their specific compositions. We propose an algorithm that operates over streams of RDF annotated EPCIS events to generate linked pedigrees. We exemplify our approach using the pharmaceuticals supply chain and show how counterfeit detection is an implicit part of our pedigree generation. Our evaluation results show that for fast moving supply chains, smaller window sizes on event streams provide significantly higher efficiency in the generation of pedigrees as well as enable early counterfeit detection.

Bio-based chemicals from biorefining:carbohydrate conversion and utilisation

The quest for sustainable sources of fuels and chemicals to meet the demands of a rapidly rising global population represents one of this century's grand challenges. Biomass offers the most readily implemented, and low cost, solution for transportation fuels, and the only non-petroleum route to organic molecules for the manufacture of bulk, fine and speciality chemicals and polymers. Chemical processing of such biomass-derived building blocks requires catalysts compatible with hydrophilic, bulky substrates to facilitate the selective deoxygenation of highly functional bio-molecules to their target products. This chapter addresses the challenges associated with carbohydrate utilisation as a sustainable feedstock, highlighting innovations in catalyst and process design that are needed to deliver high-value chemicals from biomass-derived building blocks. © 2014 Woodhead Publishing Limited. All rights reserved.

Ontology-based pathways generation for biomass to bioenergy conversion

Bio energy is a renewable energy and a solution to the depleting fossil fuels. Bio energy such as heat, power and bio fuel is generated by conversion technologies using biomass for example domestic waste, root crops, forest residue and animal slurry. Pyrolysis, anaerobic digestion and combined heat and power engine are some examples of the technologies. Depending on the nature of a biomass, it can be treated with various technologies giving out some products, which can be further treated with other technologies and eventually converted into the final products as bio energy. The pathway followed by the biomass, technologies, intermediate products and bio energy in the conversion process is referred to as bio energy pathway. Identification of appropriate pathways optimizes the conversion process. Although there are various approaches to create or generate the pathways, there is still a need for a semantic approach to generate the pathways, which allow checking the consistency of the knowledge, and to share and extend the knowledge efficiently. This paper presents an ontology-based approach to automatic generation of the pathways for biomass to bio energy conversion, which exploits the definition and hierarchical structure of the biomass and technologies, their relationship and associated properties, and infers appropriate pathways. A case study has been carried out in a real-life scenario, the bio energy project for the North West of Europe (Bioen NW), which showed promising results.

We know what you want to buy:a demographic-based system for product recommendation on microblogs

Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time. © 2014 ACM.

The early career researcher's toolkit:translating tissue engineering, regenerative medicine and cell therapy products

Although the importance of translation for the development of tissue engineering, regenerative medicine and cell-based therapies is widely recognized, the process of translation is less well understood. This is particularly the case among some early career researchers who may not appreciate the intricacies of translational research or make decisions early in development which later hinders effective translation. Based on our own research and experiences as early career researchers involved in tissue engineering and regenerative medicine translation, we discuss common pitfalls associated with translational research, providing practical solutions and important considerations which will aid process and product development. Suggestions range from effective project management, consideration of key manufacturing, clinical and regulatory matters and means of exploiting research for successful commercialization.

When is an image a health claim? A false-recollection method to detect implicit inferences about products' health benefits

Objective: Images on food and dietary supplement packaging might lead people to infer (appropriately or inappropriately) certain health benefits of those products. Research on this issue largely involves direct questions, which could (a) elicit inferences that would not be made unprompted, and (b) fail to capture inferences made implicitly. Using a novel memory-based method, in the present research, we explored whether packaging imagery elicits health inferences without prompting, and the extent to which these inferences are made implicitly. Method: In 3 experiments, participants saw fictional product packages accompanied by written claims. Some packages contained an image that implied a health-related function (e.g., a brain), and some contained no image. Participants studied these packages and claims, and subsequently their memory for seen and unseen claims were tested. Results: When a health image was featured on a package, participants often subsequently recognized health claims that—despite being implied by the image—were not truly presented. In Experiment 2, these recognition errors persisted despite an explicit warning against treating the images as informative. In Experiment 3, these findings were replicated in a large consumer sample from 5 European countries, and with a cued-recall test. Conclusion: These findings confirm that images can act as health claims, by leading people to infer health benefits without prompting. These inferences appear often to be implicit, and could therefore be highly pervasive. The data underscore the importance of regulating imagery on product packaging; memory-based methods represent innovative ways to measure how leading (or misleading) specific images can be. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Supercritical carbonation of lime based sustainable structural ceramics

Abstract Structural ceramics were manufactured from industrial byproducts and lime by a compression moulding/vacuum dewatering technique. Treatment of these ceramics with supercritical carbon dioxide was found to both significantly increase their flexural strength and activate cementation in the industrial byproducts at least as efficiently as heat curing. Flexural strengths of up to 10 MPa were achieved. Strength improvements were associated with decreased porosity and conversion of calcium hydroxide to calcium carbonate. Life cycle assessment of proposed products made from such materials indicated that the total reduction in embodied carbon dioxide achieved, as a result of combining use of byproducts with recombination of carbon dioxide, was up to 70%. © 2010 Institute of Materials, Minerals and Mining.

Kereslet-előrejelzés sporadikus keresletű termékekre: Egy gyógyszer-nagykereskedelmi vállalat esettanulmánya (Forecasting of Sporadic Products - A Case Study of a Pharmaceutical Wholesaler Company)

A vállalatok jelentős része szembesül azzal, hogy termékei jelentős része iránt viszonylag kevés alkalommal jelentkezik kereslet. Ebből következik, hogy az ilyen termékekre a klasszikus előrejelzési módszerek, mint pl. a mozgó átlag számítása, vagy az exponenciális simítás nem alkalmazható. Azon termékeket, amelyek iránt viszonylag ritkán jelenik meg kereslet, sporadikus keresletű termékeknek nevezzük. A megkülönböztetés a sporadikus és nem sporadikus termékek között sokszor csak hüvelykujj szabály alapján állapítható meg, de erre vonatkozóan a szakirodalomban találunk iránymutatást. A nemzetközi szakirodalomban már megjelentek olyan új kereslet-előrejelzési módszerek, melyeket kimondottan az ilyen, sporadikus kereslettel rendelkező termékek estében javasoltak. Cikkünk célja, hogy ezeket a szakirodalmi ajánlásokat egy konkrét hazai vállalat valós adatain esettanulmány jelleggel tesztelje. A nemzetközi szakirodalomban is ritkán publikálnak tudományos dolgozatokat, amelyek ezt a témakört valós alkalmazási környezetben tárgyalják; ismereteink szerint magyar nyelven erről tudományos dolgozat pedig még nem született. Elméleti bevezetőnk után egy gyógyszer-nagykereskedelmi vállalatnál valós adatait használva vizsgáljuk a kérdéskört. Sor kerül a vállalat termékportfóliójának a kereslet-előrejelzés szempontjából történő tipizálására, majd sporadikus keresletű termékek keresletének előrejelzésére és ennek során a szakirodalomban az alkalmazandó módszerekre vonatkozó ajánlások vizsgálatára. _____ Significant numbers of companies have the problem that demand for their products are sporadic in nature. Demand of such products is not continual in time; its demand is diffused, is random with large proportion of zero values in the analyzed time series. The sporadic character of a demand pattern actually means that available information on the demand of previous selling periods is leaky resulting in lower quality of data available. In these cases traditional forecasting techniques do not result in reliable forecast. Special forecasting algorithms have been developed during the last decade dealing with this problem. The paper introduces these techniques and offers suggestions for application. It also presents the case study of a Hungarian pharmaceutical wholesaler company. Based on real data we develop a topology of the company's product portfolio, carry out forecasts using different techniques including those developed for products with sporadic demand and also analyze the quality of these forecasts.

A projektalapú szervezetek projektmarketing-tevékenységének sajátos kontextusa (Contextual features of project marketing activity of the project-based organisations)

Ez a tanulmány a projektvezetési szakirodalomban kialakult ismeretanyagot szem előtt tartva (noha tételesen nem hivatkozva arra) tárja fel azt a sajátos és tipikusnak nevezhető kontextust, amelyben a projektalapú szervezetek projektmarketing tevékenysége megnyilvánul. A tanulmány célja tehát nem magának a projektmarketingnek a kérdéskörére irányul, hanem elsősorban annak projektspecifikus kontextusára. Jellegét illetően a tanulmány spekulatív jellegű, vagyis lényegét tekintve nem empirikus kutatási eredményekből levont következtetésekre épül. _____ Traditional approach to project marketing focuses on process-related aspects of the marketing efforts of project- based organisations. This paper is different. Unlike to the traditional approach it highlights the decisive contextual features of project marketing, bearing in mind the typical project business from the point of view of project-based organisations. These features include: a) instead of physically existing products project-based organisations need to sell their ability to create the project outcome physically; b) the project outcome and the conditions of implementation are defined by the project client; c) project clients are involved in creating the project outcome; d) project implementation strategy applied in a client organisation may vary project by project. These determining contextual features shape to a great extent the actual competitive position of the project-based organisations which may vary project by project even in relation to the very same project client.

Rethinking Kyoto Compliance from a Consumption-Based Perspective.

Environmental consequences of international trade are quite relevant for climate change policy. Apparent decoupling of GHG emission and GDP growth, observed in several European countries, is partly due to the increasing dislocation of manufacturing industries from the developed world to emerging economies. Consequently, decoupling is coupled with increasing GHG emission embodied in imported products from these nations. The article scrutinises the GHG emission embedded in Hungarian import of Chinese products. It argues that a stagnating GHG emission observed in Hungary is intertwined with hidden GHG export to China that takes place through trading of goods. Objective evaluation of compliance status with Kyoto targets would require a consumption-based accounting of GHG emissions rather than a production-based one, unless we accept facing a BIG problem at global level.

Bioinformatic analysis for exploring relationships between genes and gene products

To carry out their specific roles in the cell, genes and gene products often work together in groups, forming many relationships among themselves and with other molecules. Such relationships include physical protein-protein interaction relationships, regulatory relationships, metabolic relationships, genetic relationships, and much more. With advances in science and technology, some high throughput technologies have been developed to simultaneously detect tens of thousands of pairwise protein-protein interactions and protein-DNA interactions. However, the data generated by high throughput methods are prone to noise. Furthermore, the technology itself has its limitations, and cannot detect all kinds of relationships between genes and their products. Thus there is a pressing need to investigate all kinds of relationships and their roles in a living system using bioinformatic approaches, and is a central challenge in Computational Biology and Systems Biology. This dissertation focuses on exploring relationships between genes and gene products using bioinformatic approaches. Specifically, we consider problems related to regulatory relationships, protein-protein interactions, and semantic relationships between genes. A regulatory element is an important pattern or "signal", often located in the promoter of a gene, which is used in the process of turning a gene "on" or "off". Predicting regulatory elements is a key step in exploring the regulatory relationships between genes and gene products. In this dissertation, we consider the problem of improving the prediction of regulatory elements by using comparative genomics data. With regard to protein-protein interactions, we have developed bioinformatics techniques to estimate support for the data on these interactions. While protein-protein interactions and regulatory relationships can be detected by high throughput biological techniques, there is another type of relationship called semantic relationship that cannot be detected by a single technique, but can be inferred using multiple sources of biological data. The contributions of this thesis involved the development and application of a set of bioinformatic approaches that address the challenges mentioned above. These included (i) an EM-based algorithm that improves the prediction of regulatory elements using comparative genomics data, (ii) an approach for estimating the support of protein-protein interaction data, with application to functional annotation of genes, (iii) a novel method for inferring functional network of genes, and (iv) techniques for clustering genes using multi-source data.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Physics-based modeling of power system components for the evaluation of low-frequency radiated electromagnetic fields

The low-frequency electromagnetic compatibility (EMC) is an increasingly important aspect in the design of practical systems to ensure the functional safety and reliability of complex products. The opportunities for using numerical techniques to predict and analyze system's EMC are therefore of considerable interest in many industries. As the first phase of study, a proper model, including all the details of the component, was required. Therefore, the advances in EMC modeling were studied with classifying analytical and numerical models. The selected model was finite element (FE) modeling, coupled with the distributed network method, to generate the model of the converter's components and obtain the frequency behavioral model of the converter. The method has the ability to reveal the behavior of parasitic elements and higher resonances, which have critical impacts in studying EMI problems. For the EMC and signature studies of the machine drives, the equivalent source modeling was studied. Considering the details of the multi-machine environment, including actual models, some innovation in equivalent source modeling was performed to decrease the simulation time dramatically. Several models were designed in this study and the voltage current cube model and wire model have the best result. The GA-based PSO method is used as the optimization process. Superposition and suppression of the fields in coupling the components were also studied and verified. The simulation time of the equivalent model is 80-100 times lower than the detailed model. All tests were verified experimentally. As the application of EMC and signature study, the fault diagnosis and condition monitoring of an induction motor drive was developed using radiated fields. In addition to experimental tests, the 3DFE analysis was coupled with circuit-based software to implement the incipient fault cases. The identification was implemented using ANN for seventy various faulty cases. The simulation results were verified experimentally. Finally, the identification of the types of power components were implemented. The results show that it is possible to identify the type of components, as well as the faulty components, by comparing the amplitudes of their stray field harmonics. The identification using the stray fields is nondestructive and can be used for the setups that cannot go offline and be dismantled