Business Intelligence and Multi-market Competition

We consider a multi-market framework where a set of firms compete on two oligopolistic markets. The cost of production of each firm allows for spillovers across markets, ensuring that output decisions for both markets have to be made jointly. Prior to competing in these markets, firms can establish links gathering business intelligence about other firms. A link formed by a firm generates two types of externalities for competitors and consumers. We characterize the business intelligence equilibrium networks and networks that maximize social welfare. By contrast with single market competition, we show that in multi-market competition there exist situations where intelligence gathering activities are underdeveloped with regard to social welfare and should be tolerated, if not encouraged, by public authorities.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.

Guidelines for the measurement of BCR-ABL1 transcripts in chronic myeloid leukaemia

Molecular testing for the BCR-ABL1 fusion gene by real time quantitative polymerase chain reaction (RT-qPCR) is the most sensitive routine approach for monitoring the response to therapy of patients with chronic myeloid leukaemia. In the context of tyrosine kinase inhibitor (TKI) therapy, the technique is most appropriate for patients who have achieved complete cytogenetic remission and can be used to define specific therapeutic milestones. To achieve this effectively, standardization of the laboratory procedures and the interpretation of results are essential. We present here consensus best practice guidelines for RT-qPCR testing, data interpretation and reporting that have been drawn up and agreed by a consortium of 21 testing laboratories in the United Kingdom and Ireland in accordance with the procedures of the UK Clinical Molecular Genetics Society.

Hypermobility and musculoskeletal pain in children:a systematic review

Objective: To examine the evidence of an association between hypermobility and musculoskeletal pain in children. Methods: A systematic review of the literature was performed using the databases PubMed, EMBASE, NHS Evidence, and Medline. Inclusion criteria were observational studies investigating hypermobility and musculoskeletal pain in children. Exclusion criteria were studies conducted on specialist groups (i.e. dancers) or hospital referrals. Pooled odds ratios (ORs) were calculated using random effects models and heterogeneity was tested using ?(2)-tests. Study quality was assessed using the Newcastle-Ottawa Scale for case-control studies. Results: Of the 80 studies identified, 15 met the inclusion criteria and were included in the review. Of these, 13 were included in the statistical analyses. Analysing the data showed that the heterogeneity was too high to allow for interpretation of the meta-analysis (I(2) = 72%). Heterogeneity was much lower when the studies were divided into European (I(2) = 8%) and Afro-Asian subgroups (I(2) = 65%). Sensitivity analysis based on data from studies reporting from European and Afro-Asian regions showed no association in the European studies [OR 1.00, 95% confidence interval (CI) 0.79-1.26] but a marked relationship between hypermobility and joint pain in the Afro-Asian group (OR 2.01, 95% CI 1.45-2.77). Meta-regression showed a highly significant difference between subgroups in both meta-analyses (p <0.001). Conclusion: There seems to be no association between hypermobility and joint pain in Europeans. There does seem to be an association in Afro-Asians; however, there was a high heterogeneity. It is unclear whether this is due to differences in ethnicity, nourishment, climate or study design.

The Antibiotic Resistance and Prescribing in European Children Project: A Neonatal and Pediatric Antimicrobial Web-based Point Prevalence Survey in 73 Hospitals Worldwide

BACKGROUND: The neonatal and pediatric antimicrobial point prevalence survey (PPS) of the Antibiotic Resistance and Prescribing in European Children project (http://www.arpecproject.eu/) aims to standardize a method for surveillance of antimicrobial use in children and neonates admitted to the hospital within Europe. This article describes the audit criteria used and reports overall country-specific proportions of antimicrobial use. An analytical review presents methodologies on antimicrobial use.

METHODS: A 1-day PPS on antimicrobial use in hospitalized children was organized in September 2011, using a previously validated and standardized method. The survey included all inpatient pediatric and neonatal beds and identified all children receiving an antimicrobial treatment on the day of survey. Mandatory data were age, gender, (birth) weight, underlying diagnosis, antimicrobial agent, dose and indication for treatment. Data were entered through a web-based system for data-entry and reporting, based on the WebPPS program developed for the European Surveillance of Antimicrobial Consumption project.

RESULTS: There were 2760 and 1565 pediatric versus 1154 and 589 neonatal inpatients reported among 50 European (n = 14 countries) and 23 non-European hospitals (n = 9 countries), respectively. Overall, antibiotic pediatric and neonatal use was significantly higher in non-European (43.8%; 95% confidence interval [CI]: 41.3-46.3% and 39.4%; 95% CI: 35.5-43.4%) compared with that in European hospitals (35.4; 95% CI: 33.6-37.2% and 21.8%; 95% CI: 19.4-24.2%). Proportions of antibiotic use were highest in hematology/oncology wards (61.3%; 95% CI: 56.2-66.4%) and pediatric intensive care units (55.8%; 95% CI: 50.3-61.3%).

CONCLUSIONS: An Antibiotic Resistance and Prescribing in European Children standardized web-based method for a 1-day PPS was successfully developed and conducted in 73 hospitals worldwide. It offers a simple, feasible and sustainable way of data collection that can be used globally.

Genome-wide association study reveals greater polygenic loading for schizophrenia in cases with a family history of illness

Genome-wide association studies (GWAS) of schizophrenia have yielded more than 100 common susceptibility variants, and strongly support a substantial polygenic contribution of a large number of small allelic effects. It has been hypothesized that familial schizophrenia is largely a consequence of inherited rather than environmental factors. We investigated the extent to which familiality of schizophrenia is associated with enrichment for common risk variants detectable in a large GWAS. We analyzed single nucleotide polymorphism (SNP) data for cases reporting a family history of psychotic illness (N = 978), cases reporting no such family history (N = 4,503), and unscreened controls (N = 8,285) from the Psychiatric Genomics Consortium (PGC1) study of schizophrenia. We used a multinomial logistic regression approach with model-fitting to detect allelic effects specific to either family history subgroup. We also considered a polygenic model, in which we tested whether family history positive subjects carried more schizophrenia risk alleles than family history negative subjects, on average. Several individual SNPs attained suggestive but not genome-wide significant association with either family history subgroup. Comparison of genome-wide polygenic risk scores based on GWAS summary statistics indicated a significant enrichment for SNP effects among family history positive compared to family history negative cases (Nagelkerke's R(2 ) = 0.0021; P = 0.00331; P-value threshold <0.4). Estimates of variability in disease liability attributable to the aggregate effect of genome-wide SNPs were significantly greater for family history positive compared to family history negative cases (0.32 and 0.22, respectively; P = 0.031). We found suggestive evidence of allelic effects detectable in large GWAS of schizophrenia that might be specific to particular family history subgroups. However, consideration of a polygenic risk score indicated a significant enrichment among family history positive cases for common allelic effects. Familial illness might, therefore, represent a more heritable form of schizophrenia, as suggested by previous epidemiological studies.

REporting and calibration of post-bomb C-14 data

The definitive paper by Stuiver and Polach (1977) established the conventions for reporting of 14C data for chronological and geophysical studies based on the radioactive decay of 14C in the sample since the year of sample death or formation. Several ways of reporting 14C activity levels relative to a standard were also established, but no specific instructions were given for reporting nuclear weapons testing (post-bomb) 14C levels in samples. Because the use of post-bomb 14C is becoming more prevalent in forensics, biology, and geosciences, a convention needs to be adopted. We advocate the use of fraction modern with a new symbol F14C to prevent confusion with the previously used Fm, which may or may not have been fractionation corrected. We also discuss the calibration of post-bomb 14C samples and the available datasets and compilations, but do not give a recommendation for a particular dataset.

Introduction to Domain-Driven Data Mining Special Section

In the last decade, data mining has emerged as one of the most dynamic and lively areas in information technology. Although many algorithms and techniques for data mining have been proposed, they either focus on domain independent techniques or on very specific domain problems. A general requirement in bridging the gap between academia and business is to cater to general domain-related issues surrounding real-life applications, such as constraints, organizational factors, domain expert knowledge, domain adaption, and operational knowledge. Unfortunately, these either have not been addressed, or have not been sufficiently addressed, in current data mining research and development.Domain-Driven Data Mining (D3M) aims to develop general principles, methodologies, and techniques for modeling and merging comprehensive domain-related factors and synthesized ubiquitous intelligence surrounding problem domains with the data mining process, and discovering knowledge to support business decision-making. This paper aims to report original, cutting-edge, and state-of-the-art progress in D3M. It covers theoretical and applied contributions aiming to: 1) propose next-generation data mining frameworks and processes for actionable knowledge discovery, 2) investigate effective (automated, human and machine-centered and/or human-machined-co-operated) principles and approaches for acquiring, representing, modelling, and engaging ubiquitous intelligence in real-world data mining, and 3) develop workable and operational systems balancing technical significance and applications concerns, and converting and delivering actionable knowledge into operational applications rules to seamlessly engage application processes and systems.