BackgroundThe recent developments in microarray technology has allowed for the simultaneous measurement of gene expression levels. The large amount of captured data challenges conventional statistical tools for analysing and finding inherent correlations between genes and samples. The unsupervised clustering approach is often used, resulting in the development of a wide variety of algorithms. Typical clustering algorithms require selecting certain parameters to operate, for instance the number of expected clusters, as well as defining a similarity measure to quantify the distance between data points. The diffraction‐based clustering algorithm however is designed to overcome this necessity for user‐defined parameters, as it is able to automatically search the data for any underlying structure.MethodsThe diffraction‐based clustering algorithm presented in this paper is tested using five well‐known expression datasets pertaining to cancerous tissue samples. The clustering results are then compared to those results obtained from conventional algorithms such as the k‐means, fuzzy c‐means, self‐organising map, hierarchical clustering algorithm, Gaussian mixture model and density‐based spatial clustering of applications with noise (DBSCAN). The performance of each algorithm is measured using an average external criterion and an average validity index.ResultsThe diffraction‐based clustering algorithm is shown to be independent of the number of clusters as the algorithm searches the feature space and requires no form of parameter selection. The results show that the diffraction‐based clustering algorithm performs significantly better on the real biological datasets compared to the other existing algorithms.ConclusionThe results of the diffraction‐based clustering algorithm presented in this paper suggest that the method can provide researchers with a new tool for successfully analysing microarray data.

Theoretical and experimental investigations into the thermal excitation of liquid paramagnetic contrast agents using the spin resonance relaxation mechanism are presented. The electronic spin-lattice relaxation time τ1e of gadolinium-based contrast agents, which is estimated at 0.1 ns, is ten orders of magnitude faster than the relaxation time of protons in water. The shorter relaxation time is found to significantly increase the rate of thermal energy deposition. To the authors’ knowledge this is the first study of gadolinium based contrast agents in a liquid state used as thermal agents. Analysis shows that when τ1e and other experimental parameters are optimally selected, a maximum theoretical heating rate of 29.4 °C.s−1 could be achieved which would suffice for clinical thermal ablation of neoplasms. The experimental results show a statistically significant thermal response for two out of the four contrast agents tested. The results are compared to the simulated estimates via analysis of a detailed model of the system. While these experimentally determined temperature rises are small and thus of no clinical utility, their presence supports the theoretical analysis and strongly suggests that the chemical structure of the selected compounds plays an important role in this mechanism of heat deposition. There exists an opportunity for the development of alternative gadolinium-based compounds with an order of magnitude longer τ1e in a diluted form to be used as an efficient hyperthermia agent for clinical use.

PurposeThis paper investigates the dynamics between state affect and trusting cognitive beliefs on post-adoptive information technology (IT) use behaviors in the form of intention to explore and deep structure usage. That state affect can influence behaviors is recognized in practice. In fact, some studies examine the impact of affective constructs, but the way state affect impacts how individuals use IT remains largely unexplored. The authors theorize that state affect, in the form of positive and negative affect, will influence trusting cognitive beliefs regarding an IT artifact (in terms of perceived helpfulness, capability and reliability) and, more importantly, directly influence intention to explore and deep structure usage.Design/methodology/approachThe authors test the model using a sample of 357 IT users. Survey items were derived from established measures, and the data were analyzed using structural equation modeling.FindingsResults of this study suggest that positive affect and negative affect impact trusting cognitive beliefs. Trusting cognitive beliefs positively impact intention to explore with an IT and deep structure usage of an IT. Even in the presence of trusting beliefs, though, positive affect directly impacts intention to explore. Positive affect and negative affect both also have various indirect, mediated effects on intention to explore and deep structure usage.Originality/valueIn order to maximize value from workplace IT, the results suggest managers foster an authentic, positive work environment in order to harness or redirect employees' emotional energies.

No abstract