Segregated tunneling-percolation model for transport nonuniversality

We propose a theory of the origin of transport nonuniversality in disordered insulating-conducting compounds based on the interplay between microstructure and tunneling processes between metallic grains dispersed in the insulating host. We show that if the metallic phase is arranged in quasi-one dimensional chains of conducting grains, then the distribution function of the chain conductivities g has a power-law divergence for g -> 0 leading to nonuniversal values of the transport critical exponent t. We evaluate the critical exponent t by Monte Carlo calculations on a cubic lattice and show that our model can describe universal as well nonuniversal behavior of transport depending on the value of few microstructural parameters. Such segregated tunneling-percolation model can describe the microstructure of a quite vast class of materials known as thick-film resistors which display universal or nonuniversal values of t depending on the composition.Comment: 8 pages, 5 figures (Phys. Rev. B - 1 August 2003)(fig1 replaced

Measuring surface-area-to-volume ratios in soft porous materials using laser-polarized xenon interphase exchange NMR

We demonstrate a minimally invasive nuclear magnetic resonance (NMR) technique that enables determination of the surface-area-to-volume ratio (S/V) of soft porous materials from measurements of the diffusive exchange of laser-polarized 129Xe between gas in the pore space and 129Xe dissolved in the solid phase. We apply this NMR technique to porous polymer samples and find approximate agreement with destructive stereological measurements of S/V obtained with optical confocal microscopy. Potential applications of laser-polarized xenon interphase exchange NMR include measurements of in vivo lung function in humans and characterization of gas chromatography columns.Comment: 14 pages of text, 4 figure

Piezoresistivity and conductance anisotropy of tunneling-percolating systems

Percolating networks based on interparticle tunneling conduction are shown to yield a logarithmic divergent piezoresistive response close to the critical point as long as the electrical conductivity becomes nonuniversal. At the same time, the piezoresistivity or, equivalently, the conductivity anisotropy exponent $\lambda$ remains universal also when the conductive exponent is not, suggesting a purely geometric origin of $\lambda$. We discuss our results in relation to the nature of transport for a variety of materials such as carbon-black--polymer composites and RuO_2-glass systems which show nonuniversal transport properties and coexistence between tunneling and percolating behaviors.Comment: 6 pages, 3 figures, Added discussion on experiment

Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

The final publication is available at Springer via http://dx.doi.org/DOI 10.1007/s10618-014-0378-6. Published online.Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen–Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.The work by C Saez has been supported by an Erasmus Lifelong Learning Programme 2013 Grant. This work has been supported by own IBIME funds. The authors thank Dr. Gregor Stiglic, from the Univeristy of Maribor, Slovenia, for his support on the NHDS data.Sáez Silvestre, C.; Pereira Rodrigues, P.; Gama, J.; Robles Viejo, M.; García Gómez, JM. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery. 28:1-1. doi:10.1007/s10618-014-0378-6S1128Aggarwal C (2003) A framework for diagnosing changes in evolving data streams. In Proceedings of the International Conference on Management of Data ACM SIGMOD, pp 575–586Amari SI, Nagaoka H (2007) Methods of information geometry. American Mathematical Society, Providence, RIArias E (2014) United states life tables, 2009. Natl Vital Statist Rep 62(7): 1–63Aspden P, Corrigan JM, Wolcott J, Erickson SM (2004) Patient safety: achieving a new standard for care. Committee on data standards for patient safety. The National Academies Press, Washington, DCBasseville M, Nikiforov IV (1993) Detection of abrupt changes: theory and application. Prentice-Hall Inc, Upper Saddle River, NJBorg I, Groenen PJF (2010) Modern multidimensional scaling: theory and applications. Springer, BerlinBowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the Kernel approach with S-plus illustrations (Oxford statistical science series). Oxford University Press, OxfordBrandes U, Pich C (2007) Eigensolver methods for progressive multidimensional scaling of large data. In: Kaufmann M, Wagner D (eds) Graph drawing. Lecture notes in computer science, vol 4372. Springer, Berlin, pp 42–53Brockwell P, Davis R (2009) Time series: theory and methods., Springer series in statisticsSpringer, BerlinCesario SK (2002) The “Christmas Effect” and other biometeorologic influences on childbearing and the health of women. J Obstet Gynecol Neonatal Nurs 31(5):526–535Chakrabarti K, Garofalakis M, Rastogi R, Shim K (2001) Approximate query processing using wavelets. VLDB J 10(2–3):199–223Cruz-Correia RJ, Pereira Rodrigues P, Freitas A, Canario Almeida F, Chen R, Costa-Pereira A (2010) Data quality and integration issues in electronic health records. Information discovery on electronic health records, pp 55–96Csiszár I (1967) Information-type measures of difference of probability distributions and indirect observations. Studia Sci Math Hungar 2:299–318Dasu T, Krishnan S, Lin D, Venkatasubramanian S, Yi K (2009) Change (detection) you can believe. In: Finding distributional shifts in data streams. In: Proceedings of the 8th international symposium on intelligent data analysis: advances in intelligent data analysis VIII, IDA ’09. Springer, Berlin, pp 21–34Endres D, Schindelin J (2003) A new metric for probability distributions. IEEE Trans Inform Theory 49(7):1858–1860Gama J, Gaber MM (2007) Learning from data streams: processing techniques in sensor networks. Springer, BerlinGama J, Medas P, Castillo G, Rodrigues P (2004) Learning with drift detection. In: Bazzan A, Labidi S (eds) Advances in artificial intelligence—SBIA 2004., Lecture notes in computer scienceSpringer, Berlin, pp 286–295Gama J (2010) Knowledge discovery from data streams, 1st edn. Chapman & Hall, LondonGehrke J, Korn F, Srivastava D (2001) On computing correlated aggregates over continual data streams. SIGMOD Rec 30(2):13–24Guha S, Shim K, Woo J (2004) Rehist: relative error histogram construction algorithms. In: Proceedings of the thirtieth international conference on very large data bases VLDB, pp 300–311Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques. Morgan Kaufmann, Elsevier, Burlington, MAHowden LM, Meyer JA, (2011) Age and sex composition. 2010 Census Briefs US Department of Commerce. Economics and Statistics Administration, US Census BureauHrovat G, Stiglic G, Kokol P, Ojstersek M (2014) Contrasting temporal trend discovery for large healthcare databases. Comput Methods Program Biomed 113(1):251–257Keim DA (2000) Designing pixel-oriented visualization techniques: theory and applications. IEEE Trans Vis Comput Graph 6(1):59–78Kifer D, Ben-David S, Gehrke J (2004) Detecting change in data streams. In: Proceedings of the thirtieth international conference on Very large data bases, VLDB Endowment, VLDB ’04, vol 30, pp 180–191Klinkenberg R, Renz I (1998) Adaptive information filtering: Learning in the presence of concept drifts. In: Workshop notes of the ICML/AAAI-98 workshop learning for text categorization. AAAI Press, Menlo Park, pp 33–40Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biolog Cybern 43(1):59–69Lin J (1991) Divergence measures based on the Shannon entropy. IEEE Trans Inform Theory 37:145–151Mitchell TM, Caruana R, Freitag D, McDermott J, Zabowski D (1994) Experience with a learning personal assistant. Commun ACM 37(7):80–91Mouss H, Mouss D, Mouss N, Sefouhi L (2004) Test of page-hinckley, an approach for fault detection in an agro-alimentary production system. In: Proceedings of the 5th Asian Control Conference, vol 2, pp 815–818National Research Council (2011) Explaining different levels of longevity in high-income countries. The National Academies Press, Washington, DCNHDS (2010) United states department of health and human services. Centers for disease control and prevention. National center for health statistics. National hospital discharge survey codebookNHDS (2014) National Center for Health Statistics, National Hospital Discharge Survey (NHDS) data, US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville, Maryland. http://www.cdc.gov/nchs/nhds.htmPapadimitriou S, Sun J, Faloutsos C (2005) Streaming pattern discovery in multiple time-series. In: Proceedings of the 31st international conference on very large data bases, VLDB endowment, VLDB ’05, pp 697–708Parzen E (1962) On estimation of a probability density function and mode. Ann Math Statist 33(3):1065–1076Ramsay JO, Silverman BW (2005) Functional data analysis. Springer, New YorkRodrigues P, Correia R (2013) Streaming virtual patient records. In: Krempl G, Zliobaite I, Wang Y, Forman G (eds) Real-world challenges for data stream mining. University Magdeburg, Otto-von-Guericke, pp 34–37Rodrigues P, Gama J, Pedroso J (2008) Hierarchical clustering of time-series data streams. IEEE Trans Knowl Data Eng 20(5):615–627Rodrigues PP, Gama Ja (2010) A simple dense pixel visualization for mobile sensor data mining. In: Proceedings of the second international conference on knowledge discovery from sensor data, sensor-KDD’08. Springer, Berlin, pp 175–189Rodrigues PP, Gama J, Sebastiã o R (2010) Memoryless fading windows in ubiquitous settings. In Proceedings of ubiquitous data mining (UDM) workshop in conjunction with the 19th european conference on artificial intelligence—ECAI 2010, pp 27–32Rodrigues PP, Sebastiã o R, Santos CC (2011) Improving cardiotocography monitoring: a memory-less stream learning approach. In: Proceedings of the learning from medical data streams workshop. Bled, SloveniaRubner Y, Tomasi C, Guibas L (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vision 40(2):99–121Sebastião R, Gama J (2009) A study on change detection methods. In: 4th Portuguese conference on artificial intelligenceSebastião R, Gama J, Rodrigues P, Bernardes J (2010) Monitoring incremental histogram distribution for change detection in data streams. In: Gaber M, Vatsavai R, Omitaomu O, Gama J, Chawla N, Ganguly A (eds) Knowledge discovery from sensor data, vol 5840., Lecture notes in computer science. Springer, Berlin, pp 25–42Sebastião R, Silva M, Rabiço R, Gama J, Mendonça T (2013) Real-time algorithm for changes detection in depth of anesthesia signals. Evol Syst 4(1):3–12Sáez C, Martínez-Miranda J, Robles M, García-Gómez JM (2012) O rganizing data quality assessment of shifting biomedical data. Stud Health Technol Inform 180:721–725Sáez C, Robles M, García-Gómez JM (2013) Comparative study of probability distribution distances to define a metric for the stability of multi-source biomedical research data. In: Engineering in medicine and biology society (EMBC), 2013 35th annual international conference of the IEEE, pp 3226–3229Sáez C, Robles M, García-Gómez JM (2014) Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statist Method Med Res (forthcoming)Shewhart WA, Deming WE (1939) Statistical method from the viewpoint of quality control. Graduate School of the Department of Agriculture, Washington, DCShimazaki H, Shinomoto S (2010) Kernel bandwidth optimization in spike rate estimation. J Comput Neurosci 29(1–2):171–182Solberg LI, Engebretson KI, Sperl-Hillen JM, Hroscikoski MC, O’Connor PJ (2006) Are claims data accurate enough to identify patients for performance measures or quality improvement? the case of diabetes, heart disease, and depression. Am J Med Qual 21(4):238–245Spiliopoulou M, Ntoutsi I, Theodoridis Y, Schult R (2006) monic: modeling and monitoring cluster transitions. In: Proceedings of the 12th ACm SIGKDD international conference on knowledge discovery and data mining, KDD ’06. ACm, New York, NY, pp 706–711Stiglic G, Kokol P (2011) Interpretability of sudden concept drift in medical informatics domain. In Proceedings of the 2010 IEEE international conference on data mining workshops, pp 609–613Torgerson W (1952) Multidimensional scaling: I theory and method. Psychometrika 17(4):401–419Wang RY, Strong DM (1996) Beyond accuracy: what data quality means to data consumers. J Manage Inform Syst 12(4):5–33Weiskopf NG, Weng C (2013) M ethods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 20(1):144–151Wellings K, Macdowall W, Catchpole M, Goodrich J (1999) Seasonal variations in sexual activity and their implications for sexual health promotion. J R Soc Med 92(2):60–64Westgard JO, Barry PL (2010) Basic QC practices: training in statistical quality control for medical laboratories. Westgard Quality Corporation, Madison, WIWidmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69–10

Diagnostic findings in sinonasal aspergillosis in dogs in the United Kingdom: 475 cases (2011-2021).

ObjectivesTo describe the diagnostic tests used and their comparative performance in dogs diagnosed with sinonasal aspergillosis in the United Kingdom. A secondary objective was to describe the signalment, clinical findings and common clinicopathologic abnormalities in sinonasal aspergillosis.Materials and methodsA multi-centre retrospective survey was performed involving 23 referral centres in the United Kingdom to identify dogs diagnosed with sinonasal aspergillosis from January 2011 to December 2021. Dogs were included if fungal plaques were seen during rhinoscopy or if ancillary testing (via histopathology, culture, cytology, serology or PCR) was positive and other differential diagnoses were excluded.ResultsA total of 662 cases were entered into the database across the 23 referral centres. Four hundred and seventy-five cases met the study inclusion criteria. Of these, 419 dogs had fungal plaques and compatible clinical signs. Fungal plaques were not seen in 56 dogs with turbinate destruction that had compatible clinical signs and a positive ancillary test result. Ancillary diagnostics were performed in 312 of 419 (74%) dogs with observed fungal plaques permitting calculation of sensitivity of cytology as 67%, fungal culture 59%, histopathology 47% and PCR 71%.Clinical significanceThe sensitivities of ancillary diagnostics in this study were lower than previously reported challenging the clinical utility of such tests in sinonasal aspergillosis. Treatment and management decisions should be based on a combination of diagnostics including imaging findings, visual inspection, and ancillary testing, rather than ancillary tests alone