Discovering Exclusive Patterns in Frequent Sequences

This paper presents a new concept for pattern discovery in frequent sequences with potentially interesting applications. Based on data mining, the approach aims to discover exclusive sequential patterns (ESP) by checking the relative exclusion of patterns across data sequences. ESP mining pursues the post-processing of sequential patterns and augments existing work on structural relations patterns mining. A three phase ESP mining method is proposed together with component algorithms, where a running worked example explains the process. Experiments are performed on real-world and synthetic datasets which showcase the results of ESP mining and demonstrate its effectiveness, illuminating the theories developed. An outline case study in workflow modelling gives some insight into future applicability

Graph-based Modelling of Concurrent Sequential Patterns

Structural relation patterns have been introduced recently to extend the search for complex patterns often hidden behind large sequences of data. This has motivated a novel approach to sequential patterns post-processing and a corresponding data mining method was proposed for Concurrent Sequential Patterns (ConSP). This article refines the approach in the context of ConSP modelling, where a companion graph-based model is devised as an extension of previous work. Two new modelling methods are presented here together with a construction algorithm, to complete the transformation of concurrent sequential patterns to a ConSP-Graph representation. Customer orders data is used to demonstrate the effectiveness of ConSP mining while synthetic sample data highlights the strength of the modelling technique, illuminating the theories developed

Sequential Patterns Post-processing for Structural Relation Patterns Mining

Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been extensively studied in the literature, and there exists a diversity of algorithms. However, more complex structural patterns are often hidden behind sequences. This article begins with the introduction of a model for the representation of sequential patterns—Sequential Patterns Graph—which motivates the search for new structural relation patterns. An integrative framework for the discovery of these patterns–Postsequential Patterns Mining–is then described which underpins the postprocessing of sequential patterns. A corresponding data-mining method based on sequential patterns postprocessing is proposed and shown to be effective in the search for concurrent patterns. From experiments conducted on three component algorithms, it is demonstrated that sequential patterns-based concurrent patterns mining provides an efficient method for structural knowledge discover

A Novel Approach to Knowledge Discovery and Representation in Biological Databases.

Extraction of motifs from biological sequences is among the frontier research issues in bioinformatics, with sequential patterns mining becoming one of the most important computational techniques in this area. A number of applications motivate the search for more structured patterns and concurrent protein motif mining is considered here. This paper builds on the concept of structural relation patterns and applies the Concurrent Sequential Patterns (ConSP) mining approach to biological databases. Specifically, an original method is presented using support vectors as the data structure for the extraction of novel patterns in protein sequences. Data modelling is pursued to represent the more interesting concurrent patterns visually. Experiments with real-world protein datasets from the UniProt and NCBI databases highlight the applicability of the ConSP methodology in protein data mining and modelling. The results show the potential for knowledge discovery in the field of protein structure identification. A pilot experiment extends the methodology to DNA sequences to indicate a future direction

Effects of fenofibrate on renal function in patients with type 2 diabetes mellitus: the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) Study

Abstract Aims/hypothesis Fenofibrate caused an acute, sustained plasma creatinine increase in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) and Action to Control Cardiovascular Risk in Diabetes (ACCORD) studies. We assessed fenofibrate’s renal effects in a FIELD washout sub-study. Methods Type 2 diabetic patients (n=9795) aged 50 to 75 years were randomly assigned to fenofibrate (n=4895) or placebo (n=4900) for 5 years, after 6 weeks fenofibrate run-in. Albuminuria (urinary albumin:creatinine ratio) measured at baseline, year 2 and close-out) and estimated GFR, measured 4 to 6 monthly according to the Modification of Diet in Renal Disease study, were pre-specified endpoints. Plasma creatinine was re-measured 8 weeks after treatment cessation at close-out (washout sub-study, n=661). Analysis was by intention-to-treat. Results During fenofibrate run-in, plasma creatinine increased by 10.0 µmol/l (p<0.001), but quickly reversed on placebo assignment. It remained higher on fenofibrate than on placebo, but the chronic rise was slower (1.62 µmol/l vs 1.89 µmol/l annually, p=0.01), with less estimated GFR loss (1.19 vs 2.03 ml min−1 1.73 m−2 annually, p<0.001). After washout, estimated GFR had fallen less from baseline on fenofibrate (1.9 ml min−1 1.73 m−2, p=0.065) than on placebo (6.9 ml min−1 1.73 m−2, p<0.001), sparing 5.0 ml min−1 1.73 m−2 (95% CI 2.3-7.7, p<0.001). Greater preservation of estimated GFR with fenofibrate was observed during greater reduction over the active run-in period (pre-randomisation) of triacylglycerol (n=186 vs 170) and baseline hypertriacylglycerolaemia (n=89 vs 80) alone, or combined with low HDL-cholesterol (n=71 vs 60). Fenofibrate reduced urine albumin concentrations and hence albumin:creatinine ratio by 24% vs 12% (p<0.001; mean difference 14% [95% CI 9-18]; p<0.001), with 14% less progression and 18% more albuminuria regression (p<0.001) than in participants on placebo. End-stage renal event frequency was similar (n=21 vs 26, p=0.48). Conclusions/interpretation Fenofibrate reduced albuminuria and slowed estimated GFR loss over 5 years, despite initially and reversibly increasing plasma creatinine. Fenofibrate may delay albuminuria and GFR impairment in type 2 diabetes patients. Confirmatory studies are merited. Trial registration: ISRCTN64783481 Funding: The study was funded by grants from Laboratoires Fournier, Dijon, France (now part of Solvay and Abbott Pharmaceuticals) and the NHMRC of Australia.Laboratoires Fournier, Dijon, France (now part of Solvay and Abbott Pharmaceuticals

Effects of fenofibrate on renal function in patients with type 2 diabetes mellitus: the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) Study

Abstract Aims/hypothesis Fenofibrate caused an acute, sustained plasma creatinine increase in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) and Action to Control Cardiovascular Risk in Diabetes (ACCORD) studies. We assessed fenofibrate’s renal effects in a FIELD washout sub-study. Methods Type 2 diabetic patients (n=9795) aged 50 to 75 years were randomly assigned to fenofibrate (n=4895) or placebo (n=4900) for 5 years, after 6 weeks fenofibrate run-in. Albuminuria (urinary albumin:creatinine ratio) measured at baseline, year 2 and close-out) and estimated GFR, measured 4 to 6 monthly according to the Modification of Diet in Renal Disease study, were pre-specified endpoints. Plasma creatinine was re-measured 8 weeks after treatment cessation at close-out (washout sub-study, n=661). Analysis was by intention-to-treat. Results During fenofibrate run-in, plasma creatinine increased by 10.0 µmol/l (p<0.001), but quickly reversed on placebo assignment. It remained higher on fenofibrate than on placebo, but the chronic rise was slower (1.62 µmol/l vs 1.89 µmol/l annually, p=0.01), with less estimated GFR loss (1.19 vs 2.03 ml min−1 1.73 m−2 annually, p<0.001). After washout, estimated GFR had fallen less from baseline on fenofibrate (1.9 ml min−1 1.73 m−2, p=0.065) than on placebo (6.9 ml min−1 1.73 m−2, p<0.001), sparing 5.0 ml min−1 1.73 m−2 (95% CI 2.3-7.7, p<0.001). Greater preservation of estimated GFR with fenofibrate was observed during greater reduction over the active run-in period (pre-randomisation) of triacylglycerol (n=186 vs 170) and baseline hypertriacylglycerolaemia (n=89 vs 80) alone, or combined with low HDL-cholesterol (n=71 vs 60). Fenofibrate reduced urine albumin concentrations and hence albumin:creatinine ratio by 24% vs 12% (p<0.001; mean difference 14% [95% CI 9-18]; p<0.001), with 14% less progression and 18% more albuminuria regression (p<0.001) than in participants on placebo. End-stage renal event frequency was similar (n=21 vs 26, p=0.48). Conclusions/interpretation Fenofibrate reduced albuminuria and slowed estimated GFR loss over 5 years, despite initially and reversibly increasing plasma creatinine. Fenofibrate may delay albuminuria and GFR impairment in type 2 diabetes patients. Confirmatory studies are merited. Trial registration: ISRCTN64783481 Funding: The study was funded by grants from Laboratoires Fournier, Dijon, France (now part of Solvay and Abbott Pharmaceuticals) and the NHMRC of Australia.Laboratoires Fournier, Dijon, France (now part of Solvay and Abbott Pharmaceuticals

Outcomes of Two Trials of Oxygen-Saturation Targets in Preterm Infants

BACKGROUN

Studies on the Autonomic Functional State in Urolithiasis Report II. Metabolism of Serum Potassium and Calcium in Urolithiasis

In paper I of this series the author reported that the function of the autonomic nervous system in urolithiasis was in the state of autonomic dystonia especially hypertonia of parasympathetic nervous system by the pharmacodynamic tests and mechanical tests. And then the hypertonic state of parasympathetic nervous system was more over recognized in post operative condition. In the present paper, in order to examine serum potassium and calcium levels related to the function of the autonomic nervous system, the author estimated in 20 urolithiasis both before and after the removal of calculous or affected kidney. Serum potassium and calcium levels were measured by the flame photometer. The results were as follows : 1) In 20 healthy adults (male 8, female 12), the average potassium value was 4.47± 0.29 mEq/L and that of calcium was 4.64±0.34 mEq/L and K/Ca-quotient was 0.96±0.11. 2) In 20 urolithiasis (male 17, female 3), the average potassium value was 4.25±0.43 mEq/L and that of calcium value was 5.05±0.91 mEq/L and K/Ca-quotient was 0.84±0.18. In urolithiasis the serum potassium levels lower than in healthy adults, on the other hand the serum calcium levels higher than in healthy adults, and then K/Ca-quotient tended to decrease. 3) Aft e r operative stress in urolithiasis the serum potassium level slightly tended to increase, and the serum calcium level transitiorily tended to decrease, and then K/Ca-quotient rose in slight degree. 4) 20 days after the removal of calculous or affected kidney, the serum potassium level rose in slight degree and the serum calcium level fell in slight degree, and then K/Ca-quotient have tendency to increase. 5) By the injection of adrena l i ne the serum potassium level in urolithiasis tended to increase, on the other hand the serum calcium level tended to decrease. K/Ca-quotient tended to increse. When pilocarpine was injected the serum potassium level tented to decrease and the serum calcium level tended to increase. K/Ca-quotient tended to decrease. These changes were still recognized after the removal of calculous or affected kidney. 6) In urolithiasis, those who had a high K/Ca-quotient were more likely to have sympathicotone adrenaline reaction on the curve of the blood pressure after adrenaline injection, and those with a low K/Ca-quotient were likely to have vagotone adrenaline reaction. From these results the author concluded that urinary stone-formation is many cases in state of autonomic dystonia especially hypertonia of parasympathetic nervous system upon electrolyte metabolism

Moving towards 24-hour support

The academic institution of today is becoming increasingly involved in the electronic delivery of programmes to learners who are geographically dispersed. At the same time, Internet use from home by internal and external students is rapidly expanding. The surge in demand for 24-hour access to IT-based facilities by students and staff off-campus has made the expansion of current services a strategic imperative. This paper suggests a particular solution to the problem of meeting the growing needs of remote users through extending information services by innovative, As Internet use from home increases, a greater need arises to service the remote user. Access to electronic mail has become essential for many students and staff in higher education 24 hours a day, 7 days a week, and there is an increasing reliance on remote Web-based access to information services more generally

Data Mining Tools and their Role in Knowledge Management

Knowledge Management (KM) as a field of study in the social technology space is driven both by the practical needs of organisations and interactions between related broad areas including cognitive sciences, information sciences, economics and management sciences (Suresh et al. 2006). Technologies play a key role in delivering and supporting KM services, and this paper will focus on knowledge discovery in relation to the process of knowledge creation in the industrial setting. As a subset of Knowledge Discovery in Databases (KDD), data mining has been defined as the "nontrivial extraction of implicit, previously unknown and potentially useful information from data". Two strands of KM are identifying existing knowledge and creating new knowledge – data mining offers organisations the facilities to discover, organise, check and analyse their body of knowledge. Data Mining (DM) tools use data to build a model of the real world and the result of this modelling is a description of patterns and relationships in data, which can be used in pursuit of the primary data mining goals, i.e. prediction and description. Describing patterns and relationships in a complex dataset can provide the knowledge that guides future business actions. There are a range of data mining techniques for dealing with large-scale databases and sophisticated algorithms are incorporated into commercial software. This paper brings together some existing frameworks and schemes to present a set of criteria for DM tools with the aim of assisting industrial users and researchers in the selection process. PolyAnalyst from Megaputer Intelligence is used as a case study here to highlight the approach to evaluation of functionality and usability in the context of the particular business goal