Activated Polymorphonuclear Leukocytes Rapidly Synthesize Retinoic Acid Receptor-α: A Mechanism for Translational Control of Transcriptional Events

In addition to releasing preformed granular proteins, polymorphonuclear leukocytes (PMNs) synthesize chemokines and other factors under transcriptional control. Here we demonstrate that PMNs express an inducible transcriptional modulator by signal-dependent activation of specialized mechanisms that regulate messenger RNA (mRNA) translation. HL-60 myelocytic cells differentiated to surrogate PMNs respond to activation by platelet activating factor by initiating translation and with appearance of specific mRNA transcripts in polyribosomes. cDNA array analysis of the polyribosome fraction demonstrated that retinoic acid receptor (RAR)-α, a transcription factor that controls the expression of multiple genes, is one of the polyribosome-associated transcripts. Quiescent surrogate HL60 PMNs and primary human PMNs contain constitutive message for RAR-α but little or no protein. RAR-α protein is rapidly synthesized in response to platelet activating factor under the control of a specialized translational regulator, mammalian target of rapamycin, and is blocked by the therapeutic macrolide rapamycin, events consistent with features of the 5′ untranslated region of the transcript. Newly synthesized RAR-α modulates production of interleukin-8. Rapid expression of a transcription factor under translational control is a previously unrecognized mechanism in human PMNs that indicates unexpected diversity in gene regulation in this critical innate immune effector cell

QuickSel: Quick Selectivity Learning with Mixture Models

Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshed by re-scanning the data as the underlying data changes. Since frequent scans are costly, these statistics are often stale and lead to poor selectivity estimates. As an alternative to scans, query-driven histograms have been proposed, which refine the histograms based on the actual selectivities of the observed queries. Unfortunately, these approaches are either too costly to use in practice---i.e., require an exponential number of buckets---or quickly lose their advantage as they observe more queries. In this paper, we propose a selectivity learning framework, called QuickSel, which falls into the query-driven paradigm but does not use histograms. Instead, it builds an internal model of the underlying data, which can be refined significantly faster (e.g., only 1.9 milliseconds for 300 queries). This fast refinement allows QuickSel to continuously learn from each query and yield increasingly more accurate selectivity estimates over time. Unlike query-driven histograms, QuickSel relies on a mixture model and a new optimization algorithm for training its model. Our extensive experiments on two real-world datasets confirm that, given the same target accuracy, QuickSel is 34.0x-179.4x faster than state-of-the-art query-driven histograms, including ISOMER and STHoles. Further, given the same space budget, QuickSel is 26.8%-91.8% more accurate than periodically-updated histograms and samples, respectively

Long-term coding of personal and universal associations underlying the memory web in the human brain

Neurons in the medial temporal lobe (MTL), a critical area for declarative memory, have been shown to change their tuning in associative learning tasks. Yet, it is unclear how durable these neuronal representations are and if they outlast the execution of the task. To address this issue, we studied the responses of MTL neurons in neurosurgical patients to known concepts (people and places). Using association scores provided by the patients and a web-based metric, here we show that whenever MTL neurons respond to more than one concept, these concepts are typically related. Furthermore, the degree of association between concepts could be successfully predicted based on the neurons’ response patterns. These results provide evidence for a long-term involvement of MTL neurons in the representation of durable associations, a hallmark of human declarative memory

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

Impact of different food label formats on healthiness evaluation and food choice of consumers: a randomized-controlled study

Abstract Background Front of pack food labels or signpost labels are currently widely discussed as means to help consumers to make informed food choices. It is hoped that more informed food choices will result in an overall healthier diet. There is only limited evidence, as to which format of a food label is best understood by consumers, helps them best to differentiate between more or less healthy food and whether these changes in perceived healthiness result in changes of food choice. Methods In a randomised experimental study in Hamburg/Germany 420 adult subjects were exposed to one of five experimental conditions: (1) a simple "healthy choice" tick, (2) a multiple traffic light label, (3) a monochrome Guideline Daily Amount (GDA) label, (4) a coloured GDA label and (5) a "no label" condition. In the first task they had to identify the healthier food items in 28 pair-wise comparisons of foods from different food groups. In the second task they were asked to select food portions from a range of foods to compose a one-day's consumption. Differences between means were analysed using ANOVAs. Results Task I: Experimental conditions differed significantly in the number of correct decisions (p Conclusion Different food label formats differ in the understanding of consumers. The current study shows, that German adults profit most from the multiple traffic light labels. Perceived healthiness of foods is influenced by this label format most often. Nevertheless, such changes in perceived healthiness are unlikely to influence food choice and consumption. Attempts to establish the informed consumer with the hope that informed choices will be healthier choices are unlikely to change consumer behaviour and will not result in the desired contribution to the prevention of obesity and diet related diseases.</p

On the thermodynamic origin of metabolic scaling

This work has been funded by projects AYA2013-48623-C2-2, FIS2013-41057-P, CGL2013-46862-C2-1-P and SAF2015-65878-R from the Spanish Ministerio de Economa y Competitividad and PrometeoII/2014/086, PrometeoII/2014/060 and PrometeoII/2014/065 from the Generalitat Valenciana (Spain). BL acknowledges funding from a Salvador de Madariaga fellowship, and L.L. acknowledges funding from EPSRC Early Career fellowship EP/P01660X/1