Parameter Range Reduction in ODE Models in the Presence of Partial Data Sets

2015 ◽  
pp. 419-424
Author(s):  
Andrew Skelton ◽  
Allan R. Willms
Keyword(s):  
Parameter Range ◽  
Data Sets ◽  
Range Reduction ◽  
Ode Models ◽  
Partial Data

scholarly journals Everyday curation? Attending to data, records and record keeping in the practices of self-monitoring

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172091827 ◽  
Author(s):  
Kate Weiner ◽  
Catherine Will ◽  
Flis Henwood ◽  
Rosalind Williams
Keyword(s):  
Digital Data ◽  
Data Sets ◽  
Important Work ◽  
Self Monitoring ◽  
Record Keeping ◽  
Data Practices ◽  
Partial Data ◽  
The Everyday ◽  
Record Data ◽  
Index Weight

This paper is concerned with everyday data practices, considering how people record data produced through self-monitoring. The analysis unpacks the relationships between taking a measure, and making and reviewing records. The paper is based on an interview study with people who monitor their blood pressure and/or body mass index/weight. Animated by discussions of ‘data power’ which are, in part, predicated on the flow and aggregation of data, we aim to extend important work concerning the everyday constitution of digital data. In the paper, we adopt and develop the idea of curation as a theory of attention. We introduce the idea of discerning work to characterise the skilful judgements people make about which readings they record, how readings are presented, and about the records they retain and those they discard. We suggest self-monitoring produces partial data, both in the sense that it embodies these judgements, and also because monitoring might be conducted intermittently. We also extend previous analyses by exploring the broad set of materials, digital and analogue, networked and not networked, involved in record keeping to consider the different ways these contributed to regulating attention to self-monitoring. By paying attention to which data is recorded and the occasions when data is not recorded, as well as the ways data is recorded, the research provides specificity to the different ways in which self-monitoring data may or may not flow or contribute to big data sets. We argue that ultimately our analysis contributes to nuancing our understanding of ‘data power’.

Time-resolved diffraction studies on glycogen phosphorylase b

1992 ◽  
Vol 340 (1657) ◽  
pp. 245-261 ◽  
Keyword(s):  
Glycogen Phosphorylase ◽  
Pyridoxal Phosphate ◽  
Data Sets ◽  
Laue Diffraction ◽  
Time Resolved ◽  
X Ray ◽  
Glycogen Phosphorylase B ◽  
Partial Data ◽  
Spread Function ◽  
Diode Array Spectrophotometer

Glycogen phosphorylase catalyses the reversible phosphorylation of glycogen to give glucose-1-phosphate in a reaction mechanism promoted by the 5'-phosphate of the cofactor pyridoxal phosphate. The reaction with the small substrate heptenitol has been probed using Laue diffraction at the Synchrotron Radiation Source, Daresbury. The reaction was initiated following photolysis from a caged phosphate compound 3,5-dinitrophenylphosphate (DNPP). In measurements on photolysis in the crystal using a diode array spectrophotometer approximately 7 mM cage (and hence phosphate) was released from a 21 mM solution with five flashes from a xenon flash lamp. In an experiment with the home source it was shown that DNPP is stable in the crystal under conditions of X-ray measurements and that on flashing sufficient phosphate is released to promote catalysis within 24 h. In a similar experiment with the synchrotron and Laue diffraction, data were recorded before and then 3 min, 15 min and 1 h after initiation of the reaction. Theoretical analysis of the point spread function arising from partial data-sets, numerical calculations with ideal data and the experimental results have shown the importance of low-resolution terms for the interpretation of Laue difference maps. Inclusion of terms obtained from unscrambling the wavelength harmonic overlaps led to significant improvement. The maps showed heptenitol bound at the catalytic site but no evidence for catalysis under these conditions. A rational for the lack of reaction and suggestions for future experiments with improved data are outlined.

scholarly journals High-throughput in situ experimental phasing

2020 ◽  
Vol 76 (8) ◽  
pp. 790-801 ◽  
Author(s):  
Joshua M. Lawrence ◽  
Julien Orlans ◽  
Gwyndaf Evans ◽  
Allen M. Orville ◽  
James Foadi ◽  
...  
Keyword(s):  
Heavy Atom ◽  
Data Sets ◽  
Human Intervention ◽  
Macromolecular Crystallography ◽  
New Approach ◽  
Partial Data ◽  
Experimental Phasing ◽  
Anomalous Signal ◽  
First Time

In this article, a new approach to experimental phasing for macromolecular crystallography (MX) at synchrotrons is introduced and described for the first time. It makes use of automated robotics applied to a multi-crystal framework in which human intervention is reduced to a minimum. Hundreds of samples are automatically soaked in heavy-atom solutions, using a Labcyte Inc. Echo 550 Liquid Handler, in a highly controlled and optimized fashion in order to generate derivatized and isomorphous crystals. Partial data sets obtained on MX beamlines using an in situ setup for data collection are processed with the aim of producing good-quality anomalous signal leading to successful experimental phasing.

scholarly journals MeshAndCollect: an automated multi-crystal data-collection workflow for synchrotron macromolecular crystallography beamlines

2015 ◽  
Vol 71 (11) ◽  
pp. 2328-2343 ◽  
Author(s):  
Ulrich Zander ◽  
Gleb Bourenkov ◽  
Alexander N. Popov ◽  
Daniele de Sanctis ◽  
Olof Svensson ◽  
...  
Keyword(s):  
Hierarchical Cluster ◽  
Data Sets ◽  
Macromolecular Crystallography ◽  
X Ray Diffraction ◽  
Data Set ◽  
X Ray ◽  
Partial Data ◽  
Final Data ◽  
Dispersion Technique

Here, an automated procedure is described to identify the positions of many cryocooled crystals mounted on the same sample holder, to rapidly predict and rank their relative diffraction strengths and to collect partial X-ray diffraction data sets from as many of the crystals as desired. Subsequent hierarchical cluster analysis then allows the best combination of partial data sets, optimizing the quality of the final data set obtained. The results of applying the method developed to various systems and scenarios including the compilation of a complete data set from tiny crystals of the membrane protein bacteriorhodopsin and the collection of data sets for successful structure determination using the single-wavelength anomalous dispersion technique are also presented.

scholarly journals Inference of horizontal genetic transfer from molecular data: an approach using the bootstrap.

Genetics ◽  
1992 ◽  
Vol 131 (3) ◽  
pp. 753-760 ◽  
Author(s):  
J G Lawrence ◽  
D L Hartl
Keyword(s):  
Horizontal Transfer ◽  
Phylogenetic Trees ◽  
Fragment Length Polymorphism ◽  
Genetic Material ◽  
Molecular Data ◽  
Data Sets ◽  
Similarity Coefficients ◽  
Genetic Transfer ◽  
Statistical Framework ◽  
Partial Data

Abstract Inconsistencies in taxonomic relationships implicit in different sets of nucleic acid sequences potentially result from horizontal transfer of genetic material between genomes. A nonparametric method is proposed to determine whether such inconsistencies are statistically significant. A similarity coefficient is calculated from ranked pairwise identities and evaluated against a distribution of similarity coefficients generated from resampled data. Subsequent analyses of partial data sets, obtained by the elimination of individual taxa, identify particular taxa to which the significance may be attributed, and can sometimes help in distinguishing horizontal genetic transfer from inconsistencies due to convergent evolution or variation in evolutionary rate. The method was successfully applied to data sets that were not found to be significantly different with existing methods that use comparisons of phylogenetic trees. The new statistical framework is also applicable to the inference of horizontal transfer from restriction fragment length polymorphism distributions and protein sequences.

scholarly journals Model Selection and Evaluation Based on Emerging Infectious Disease Data Sets including A/H1N1 and Ebola

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Wendi Liu ◽  
Sanyi Tang ◽  
Yanni Xiao
Keyword(s):  
Model Selection ◽  
Infectious Diseases ◽  
Sierra Leone ◽  
Turning Point ◽  
Emerging Infectious Diseases ◽  
Model Parameters ◽  
Data Sets ◽  
Final Size ◽  
Ode Models ◽  
Reproduction Numbers

The aim of the present study is to apply simple ODE models in the area of modeling the spread of emerging infectious diseases and show the importance of model selection in estimating parameters, the basic reproduction number, turning point, and final size. To quantify the plausibility of each model, given the data and the set of four models including Logistic, Gompertz, Rosenzweg, and Richards models, the Bayes factors are calculated and the precise estimates of the best fitted model parameters and key epidemic characteristics have been obtained. In particular, for Ebola the basic reproduction numbers are 1.3522 (95% CI (1.3506, 1.3537)), 1.2101 (95% CI (1.2084, 1.2119)), 3.0234 (95% CI (2.6063, 3.4881)), and 1.9018 (95% CI (1.8565, 1.9478)), the turning points are November 7,November 17, October 2, and November 3, 2014, and the final sizes until December 2015 are 25794 (95% CI (25630, 25958)), 3916 (95% CI (3865, 3967)), 9886 (95% CI (9740, 10031)), and 12633 (95% CI (12515, 12750)) for West Africa, Guinea, Liberia, and Sierra Leone, respectively. The main results confirm that model selection is crucial in evaluating and predicting the important quantities describing the emerging infectious diseases, and arbitrarily picking a model without any consideration of alternatives is problematic.

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