Decision support based on genomics:integration of data-and knowledge-driven reasoning

The breadth and depth of available clinico-genomic information, present an enormous opportunity for improving our ability to study disease mechanisms and meet the individualised medicine needs. A difficulty occurs when the results are to be transferred 'from bench to bedside'. Diversity of methods is one of the causes, but the most critical one relates to our inability to share and jointly exploit data and tools. This paper presents a perspective on current state-of-the-art in the analysis of clinico-genomic data and its relevance to medical decision support. It is an attempt to investigate the issues related to data and knowledge integration. Copyright © 2010 Inderscience Enterprises Ltd.

Discovering biomarkers for antidepressant response:protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort

Background: Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. Methods: CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. Discussion: From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants) has been evaluated at baseline. The clinical characteristics of this cohort are similar to other studies of MDD. Recruitment at all sites is ongoing to a target sample of 290 participants. CAN-BIND will identify biomarkers of treatment response in MDD through extensive clinical, molecular, and imaging assessments, in order to improve treatment practice and clinical outcomes. It will also create an innovative, robust platform and database for future research. Trial registration: ClinicalTrials.gov identifier NCT01655706. Registered July 27, 2012.

The use of future Internet technologies in the agriculture and food sectors:integrating the supply chain

The Future Internet is expected to greatly influence how the food and agriculture sector is currently operating. In this paper, we present the specific characteristics of the agri-food sector focusing on how information management in this area will take place under a highly heterogeneous group of actors and services, based on the EU SmartAgriFood project. We also discuss how a new dynamic marketplace will be realized based on the adoption of a number of specialized software modules, called “Generic Enablers” that are currently developed in the context of the EU FI-WARE project. Thus, the paper presents the overall vision for data integration along the supply chain as well as the development and federation of Future Internet services that are expected to revolutionize the agriculture sector.

Enhancing visibility in EPCIS governing agri-food supply chains via linked pedigrees

Data integration for the purposes of tracking, tracing and transparency are important challenges in the agri-food supply chain. The Electronic Product Code Information Services (EPCIS) is an event-oriented GS1 standard that aims to enable tracking and tracing of products through the sharing of event-based datasets that encapsulate the Electronic Product Code (EPC). In this paper, the authors propose a framework that utilises events and EPCs in the generation of "linked pedigrees" - linked datasets that enable the sharing of traceability information about products as they move along the supply chain. The authors exploit two ontology based information models, EEM and CBVVocab within a distributed and decentralised framework that consumes real time EPCIS events as linked data to generate the linked pedigrees. The authors exemplify the usage of linked pedigrees within the fresh fruit and vegetables supply chain in the agri-food sector.

The integration of multi-source data to improve the classification of remotely sensed images

The number of remote sensing platforms and sensors rises almost every year, yet much work on the interpretation of land cover is still carried out using either single images or images from the same source taken at different dates. Two questions could be asked of this proliferation of images: can the information contained in different scenes be used to improve the classification accuracy and, what is the best way to combine the different imagery? Two of these multiple image sources are MODIS on the Terra platform and ETM+ on board Landsat7, which are suitably complementary. Daily MODIS images with 36 spectral bands in 250-1000 m spatial resolution and seven spectral bands of ETM+ with 30m and 16 days spatial and temporal resolution respectively are available. In the UK, cloud cover may mean that only a few ETM+ scenes may be available for any particular year and these may not be at the time of year of most interest. The MODIS data may provide information on land cover over the growing season, such as harvest dates, that is not present in the ETM+ data. Therefore, the primary objective of this work is to develop a methodology for the integration of medium spatial resolution Landsat ETM+ image, with multi-temporal, multi-spectral, low-resolution MODIS \Terra images, with the aim of improving the classification of agricultural land. Additionally other data may also be incorporated such as field boundaries from existing maps. When classifying agricultural land cover of the type seen in the UK, where crops are largely sown in homogenous fields with clear and often mapped boundaries, the classification is greatly improved using the mapped polygons and utilising the classification of the polygon as a whole as an apriori probability in classifying each individual pixel using a Bayesian approach. When dealing with multiple images from different platforms and dates it is highly unlikely that the pixels will be exactly co-registered and these pixels will contain a mixture of different real world land covers. Similarly the different atmospheric conditions prevailing during the different days will mean that the same emission from the ground will give rise to different sensor reception. Therefore, a method is presented with a model of the instantaneous field of view and atmospheric effects to enable different remote sensed data sources to be integrated.

Knowledge integration for analyzing ChIP-seq

To capture the genomic profiles for histone modification, chromatin immunoprecipitation (ChIP) is combined with next generation sequencing, which is called ChIP-seq. However, enriched regions generated from the ChIP-seq data are only evaluated on the limited knowledge acquired from manually examining the relevant biological literature. This paper proposes a novel framework, which integrates multiple knowledge sources such as biological literature, Gene Ontology, and microarray data. In order to precisely analyze ChIP-seq data for histone modification, knowledge integration is based on a unified probabilistic model. The model is employed to re-rank the enriched regions generated from peak finding algorithms. Through filtering the reranked enriched regions using some predefined threshold, more reliable and precise results could be generated. The combination of the multiple knowledge sources with the peaking finding algorithm produces a new paradigm for ChIP-seq data analysis. © (2012) Trans Tech Publications, Switzerland.

Integration of semantically annotated data by the KnoFuss architecture

Most of the existing work on information integration in the Semantic Web concentrates on resolving schema-level problems. Specific issues of data-level integration (instance coreferencing, conflict resolution, handling uncertainty) are usually tackled by applying the same techniques as for ontology schema matching or by reusing the solutions produced in the database domain. However, data structured according to OWL ontologies has its specific features: e.g., the classes are organized into a hierarchy, the properties are inherited, data constraints differ from those defined by database schema. This paper describes how these features are exploited in our architecture KnoFuss, designed to support data-level integration of semantic annotations.

Data visualization during the early stages of drug discovery

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software. © 2006 American Chemical Society.

Integration and devolvement of human resource practices in Nepal

Purpose: The purpose of this study is to explore the nature of human resource management in publicly listed finance sector companies in Nepal. In particular, it explores the extent to which HR practice is integrated into organisational strategy and devolved to line management. Design/methodology/ approach: A structured interview was conducted with the senior executive responsible for human resource management in 26 commercial banks and insurance companies in Nepal. Findings: The degree of integration of HR practice appears to be increasing within this sector, but this is dependent on the maturity of the organisations. The devolvement of responsibility to line managers is at best partial, and in the case of the insurance companies, it is more out of necessity due to the absence of a strong central HR function. Research limitations/implications: The survey is inevitably based on a small sample; however this represents 90 per cent of the relevant population. The data suggest that Western HR is making inroads into more developed aspects of Nepalese business. Compared with Nepalese business as a whole, the financial sector appears relatively Westernised, although Nepal still lags India in its uptake of HR practices. Practical implications: It appears unlikely from a cultural perspective that the devolvement of responsibility will be achieved as a result of HR strategy. National cultural, political and social factors continue to be highly influential in shaping the Nepalese business environment. Originality/value: Few papers have explored HR practice in Nepal. This paper contributes to the overall assessment of HR uptake globally and highlights emic features impacting on that uptake. © Emerald Group Publishing Limited.

Three-factor models versus time series models:quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data

Signal integration determines cell fate on the cellular level, affects cognitive processes and affective responses on the behavioural level, and is likely to be involved in psychoneurobiological processes underlying mood disorders. Interactions between stimuli may subjected to time effects. Time-dependencies of interactions between stimuli typically lead to complex cell responses and complex responses on the behavioural level. We show that both three-factor models and time series models can be used to uncover such time-dependencies. However, we argue that for short longitudinal data the three factor modelling approach is more suitable. In order to illustrate both approaches, we re-analysed previously published short longitudinal data sets. We found that in human embryonic kidney 293 cells cells the interaction effect in the regulation of extracellular signal-regulated kinase (ERK) 1 signalling activation by insulin and epidermal growth factor is subjected to a time effect and dramatically decays at peak values of ERK activation. In contrast, we found that the interaction effect induced by hypoxia and tumour necrosis factor-alpha for the transcriptional activity of the human cyclo-oxygenase-2 promoter in HEK293 cells is time invariant at least in the first 12-h time window after stimulation. Furthermore, we applied the three-factor model to previously reported animal studies. In these studies, memory storage was found to be subjected to an interaction effect of the beta-adrenoceptor agonist clenbuterol and certain antagonists acting on the alpha-1-adrenoceptor / glucocorticoid-receptor system. Our model-based analysis suggests that only if the antagonist drug is administer in a critical time window, then the interaction effect is relevant.

Data driven graphical applications:a fluid approach

The inclusion of high-level scripting functionality in state-of-the-art rendering APIs indicates a movement toward data-driven methodologies for structuring next generation rendering pipelines. A similar theme can be seen in the use of composition languages to deploy component software using selection and configuration of collaborating component implementations. In this paper we introduce the Fluid framework, which places particular emphasis on the use of high-level data manipulations in order to develop component based software that is flexible, extensible, and expressive. We introduce a data-driven, object oriented programming methodology to component based software development, and demonstrate how a rendering system with a similar focus on abstract manipulations can be incorporated, in order to develop a visualization application for geospatial data. In particular we describe a novel SAS script integration layer that provides access to vertex and fragment programs, producing a very controllable, responsive rendering system. The proposed system is very similar to developments speculatively planned for DirectX 10, but uses open standards and has cross platform applicability. © The Eurographics Association 2007.

Purification of plasmid DNA for use in human gene therapy

Two key issues defined the focus of this research in manufacturing plasmid DNA for use In human gene therapy. First, the processing of E.coli bacterial cells to effect the separation of therapeutic plasmid DNA from cellular debris and adventitious material. Second, the affinity purification of the plasmid DNA in a Simple one-stage process. The need arises when considering the concerns that have been recently voiced by the FDA concerning the scalability and reproducibility of the current manufacturing processes in meeting the quality criteria of purity, potency, efficacy, and safety for a recombinant drug substance for use in humans. To develop a preliminary purification procedure, an EFD cross-flow micro-filtration module was assessed for its ability to effect the 20-fold concentration, 6-time diafiltration, and final clarification of the plasmid DNA from the subsequent cell lysate that is derived from a 1 liter E.coli bacterial cell culture. Historically, the employment of cross-flow filtration modules within procedures for harvesting cells from bacterial cultures have failed to reach the required standards dictated by existing continuous centrifuge technologies, frequently resulting in the rapid blinding of the membrane with bacterial cells that substantially reduces the permeate flux. By challenging the EFD module, containing six helical wound tubular membranes promoting centrifugal instabilities known as Dean vortices, with distilled water between the Dean number's of 187Dn and 818Dn,and the transmembrane pressures (TMP) of 0 to 5 psi. The data demonstrated that the fluid dynamics significantly influenced the permeation rate, displaying a maximum at 227Dn (312 Imh) and minimum at 818Dn (130 Imh) for a transmembrane pressure of 1 psi. Numerical studies indicated that the initial increase and subsequent decrease resulted from a competition between the centrifugal and viscous forces that create the Dean vortices. At Dean numbers between 187Dn and 227Dn , the forces combine constructively to increase the apparent strength and influence of the Dean vortices. However, as the Dean number in increases above 227 On the centrifugal force dominates the viscous forces, compressing the Dean vortices into the membrane walls and reducing their influence on the radial transmembrane pressure i.e. the permeate flux reduced. When investigating the action of the Dean vortices in controlling tile fouling rate of E.coli bacterial cells, it was demonstrated that the optimum cross-flow rate at which to effect the concentration of a bacterial cell culture was 579Dn and 3 psi TMP, processing in excess of 400 Imh for 20 minutes (i.e., concentrating a 1L culture to 50 ml in 10 minutes at an average of 450 Imh). The data demonstrated that there was a conflict between the Dean number at which the shear rate could control the cell fouling, and the Dean number at which tile optimum flux enhancement was found. Hence, the internal geometry of the EFD module was shown to sub-optimal for this application. At 579Dn and 3 psi TMP, the 6-fold diafiltration was shown to occupy 3.6 minutes of process time, processing at an average flux of 400 Imh. Again, at 579Dn and 3 psi TMP the clarification of the plasmid from tile resulting freeze-thaw cell lysate was achieved at 120 Iml1, passing 83% (2,5 mg) of the plasmid DNA (6,3 ng μ-1 10.8 mg of genomic DNA (∼23,00 Obp, 36 ng μ-1 ), and 7.2 mg of cellular proteins (5-100 kDa, 21.4 ngμ-1 ) into the post-EFD process stream. Hence the EFD module was shown to be effective, achieving the desired objectives in approximately 25 minutes. On the basis of its ability to intercalate into low molecular weight dsDNA present in dilute cell lysates, and be electrophoresed through agarose, the fluorophore PicoGreen was selected for the development of a suitable dsDNA assay. It was assesseel for its accuracy, and reliability, In determining the concentration and identity of DNA present in samples that were eleclrophoresed through agarose gels. The signal emitted by intercalated PicoGreen was shown to be constant and linear, and that the mobility of the PicaGreen-DNA complex was not affected by the intercalation. Concerning the secondary purification procedure, various anion-exchange membranes were assessed for their ability to capture plasmid DNA from the post-EFD process stream. For a commercially available Sartorius Sartobind Q15 membrane, the reduction in the equilibriumbinding capacity for  ctDNA in buffer of increasing ionic demonstrated that DNA was being.adsorbed by electrostatic  interactions only. However, the problems associated with fluid distribution across the membrane demonstrated that the membrane housing was the predominant cause of the .erratic breakthrough curves. Consequently, this would need to be rectified before such a membrane could be integrated into the current system, or indeed be scaled beyond laboratory scale. However, when challenged with the process material, the data showed that considerable quantities of protein (1150 μg) were adsorbed preferentially to the plasmid DNA (44 μg). This was also shown for derived Pall Gelman UltraBind US450 membranes that had been functionalised by varying molecular weight poly-L~lysine and polyethyleneimine ligands. Hence the anion-exchange membranes were shown to be ineffective in capturing plasmid DNA from the process stream. Finally, work was performed to integrate a sequence-specific DNA·binding protein into a single-stage DNA chromatography, isolating plasmid DNA from E.coli cells whilst minimising the contamination from genomic DNA and cellular protein. Preliminary work demonstrated that the fusion protein was capable of isolating pUC19 DNA into which the recognition sequence for the fusion-protein had been inserted (pTS DNA) when in the presence of the conditioned process material. Althougth the pTS recognition sequence differs from native pUC19 sequences by only 2 bp, the fusion protein was shown to act as a highly selective affinity ligand for pTS DNA alone. Subsequently, the scale of the process was scaled 25-fold and positioned directly following the EFD system. In conclusion, the integration of the EFD micro-filtration system and zinc-finger affinity purification technique resulted in the capture of approximately 1 mg of plasmid DNA was purified from 1L of E.coli  culture in a simple two stage process, resulting in the complete removal of genomic DNA and 96.7% of cellular protein in less than 1 hour of process time.

Relational diversity, social integration and individual effectiveness: a social self-regulation perspective

Relational demographers and dissimilarity researchers contend that group members who are dissimilar (vs. similar) to their peers in terms of a given diversity attribute (e.g. demographics, attitudes, values or traits) feel less attached to their work group, experience less satisfying and more conflicted relationships with their colleagues, and consequently are less effective. However, qualitative reviews suggest empirical findings tend to be weak and inconsistent (Chattopadhyay, Tluchowska and George, 2004; Riordan, 2000; Tsui and Gutek, 1999), and that it remains unclear when, how and to what extent such differences (i.e. relational diversity) affect group members social integration (i.e. attachment with their work group, satisfaction and conflicted relationships with their peers) and effectiveness (Riordan, 2000). This absence of meta-analytically derived effect size estimates and the lack of an integrative theoretical framework leave practitioners with inconclusive advice regarding whether the effects elicited by relational diversity are practically relevant, and if so how these should be managed. The current research develops an integrative theoretical framework, which it tests by using meta-analysis techniques and adding two further empirical studies to the literature. The first study reports a meta-analytic integration of the results of 129 tests of the relationship between relational diversity with social integration and individual effectiveness. Using meta-analytic and structural equation modelling techniques, it shows different effects of surface- and deep-level relational diversity on social integration Specifically, low levels of interdependence accentuated the negative effects of surface-level relational diversity on social integration, while high levels of interdependence accentuated the negative effects of deep-level relational diversity on social integration. The second study builds on a social self-regulation framework (Abrams, 1994) and suggests that under high levels of interdependence relational diversity is not one but two things: visibility and separation. Using ethnicity as a prominent example it was proposed that separation has a negative effect on group members effectiveness leading for those high in visibility and low in separation to overall positive additive effects, while to overall negative additive effects for those low in visibility and high in separation. These propositions were sustained in a sample of 621 business students working in 135 ethnically diverse work groups in a business simulation course over a period of 24 weeks. The third study suggests visibility has a positive effect on group members self-monitoring, while separation has a negative effect. The study proposed that high levels of visibility and low levels of separation lead to overall positive additive effects on self-monitoring but overall negative additive effects for those low in visibility and high in separation. Results from four waves of data on 261 business students working in 69 ethnically diverse work groups in a business simulation course held over a period of 24 weeks support these propositions.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Integration issues in the healthcare supply chain

The efficacy, quality, responsiveness, and value of healthcare services provided is increasingly attracting the attention and the questioning of governments, payers, patients, and healthcare providers. Investments on integration technologies and integration of supply chain processes, has been considered as a way towards removing inefficiencies in the sector. This chapter aims to initially provide an in depth analysis of the healthcare supply chain and to present core entities, processes, and flows. Moreover, the chapter explores the concept of integration in the context of the healthcare sector, and indentifies the integration drivers, as well as challenges.