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In 1901, Paula married fellow Worpswede painter Otto Modersohn. During extended visits to Paris, she was heavily influenced by the work of Cézanne and Gauguin. During her nine years as a working artist, Paula created more than four hundred paintings, and at least one thousand drawings and graphic works [7]. During her lifetime, her work was both ridiculed and praised, but was also largely overshadowed by that of her husband, Otto. In the years since her death, Paula's work has come to be regarded as of much greater importance to the history of art. In many ways, Paula was a forerunner of our age. In an era when young cultivated women were taught to play a little piano, cook creatively or paint a few watercolours, Paula stands out for her search for freedom and her uncommon spirit of adventure, together with her gift for painting.
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During law school, Alaap worked with the U.S. Department of Health and Human Services (HHS), Office of General Counsel, where he provided legal counsel and support to all agencies and programs under the Public Health Division of HHS. He began his legal career at Epstein Becker Green and later served as Senior Counsel and Chief Privacy and Security Officer at an oncology membership society, where he strengthened enterprise-wide privacy and security, helped launch a Big Data company focused on improving quality of care by harnessing real world cancer patient medical information, and built data sharing trust networks among the oncology community, before rejoining the firm in October 2017.
Kara Clauser, GIS Specialist Kara(she/her) works on GIS analyses, cartography and web mapping to assist various Center campaigns. She holds a master's of science in geographic information systems technology and bachelor's of science in ecology and evolutionary biology from the University of Arizona.
Dipika Kadaba, Environmental Scientist and Communicator Dipika (she/her) is an ecologist who works through data visualization to communicate on environmental science issues for the Center's programs. She began her career as a veterinarian researching environmental epidemiology, studied conservation in Duke's Master of Environmental Management program, and now uses technology and design for interdisciplinary communication.
PivotCharts provide graphical representations of the data in their associated PivotTables. PivotCharts are also interactive. When you create a PivotChart, the PivotChart Filter Pane appears. You can use this filter pane to sort and filter the PivotChart's underlying data. Changes that you make to the layout and data in an associated PivotTable are immediately reflected in the layout and data in the PivotChart and vice versa.
Source data Standard charts are linked directly to worksheet cells, while PivotCharts are based on their associated PivotTable's data source. Unlike a standard chart, you cannot change the chart data range in a PivotChart's Select Data Source dialog box.
You can use data from a Excel worksheet as the basis for a PivotTable or PivotChart. The data should be in list format, with column labels in the first row, which Excel will use for Field Names. Each cell in subsequent rows should contain data appropriate to its column heading, and you shouldn't mix data types in the same column. For instance, you shouldn't mix currency values and dates in the same column. Additionally, there shouldn't be any blank rows or columns within the data range.
OLAP source data When you retrieve source data from an OLAP database or a cube file, the data is returned to Excel only as a PivotTable or a PivotTable that has been converted to worksheet functions. For more information, see Convert PivotTable cells to worksheet formulas.
Displaying new data brought in by refresh Refreshing a PivotTable can also change the data that is available for display. For PivotTables based on worksheet data, Excel retrieves new fields within the source range or named range that you specified. For reports based on external data, Excel retrieves new data that meets the criteria for the underlying query or data that becomes available in an OLAP cube. You can view any new fields in the Field List and add the fields to the report.
Knowledge of transcriptional regulation of metabolism comes from different sources. At a low level, from the bottom up, some of the regulatory proteins that control the transcription of sets of metabolic genes are known [2]. At a higher level, from the top down, gene expression data provides a picture of what genes are being transcribed at a particular time, and hence which enzymes are probably active in the cell [3]. Both of these types of knowledge can be used to refine metabolic networks under given conditions.
Here we use gene expression data in combination with objective functions to create functional models despite potentially noisy data. We describe the use of genome-scale transcriptomic data to constrain reactions in both bacteria and human cells, enabling context-specific metabolic networks to be reconstructed and compared. We quantitatively define the consistency of gene expression data with assumed functional states of a cell, demonstrating agreement with physiological data. Context-specific metabolic networks will be virtually essential to accurately model human metabolism due to the variety of cell types and their corresponding metabolic processes.
The approach to the construction of context-specific metabolic networks is termed Gene Inactivity Moderated by Metabolism and Expression (GIMME) and is illustrated in Figure 1. As inputs, the algorithm requires: (1) a set of gene expression data, (2) the genome-scale reconstruction, and (3) one or more Required Metabolic Functionalities (RMF) that the cell is assumed to achieve. Preliminary tests (not shown) suggest that proteomic data can be substituted for expression profiling data. Given these three inputs the algorithm produces a list of reactions in the network that are predicted to be active and an inconsistency score (IS) that quantitatively classifies the disagreement between the gene expression data and the assumed objective function. This inconsistency score is converted to a normalized consistency score (NCS), allowing for relative comparisons of how well each gene expression data set agrees with a particular metabolic function.
We have used the GIMME algorithm to produce context-specific metabolic networks for E. coli for several different conditions and to compare inconsistency scores for different strains of the bacterium. We show that the inconsistency scores agree with experimental data in nearly all cases. Gene expression data from different conditions of E. coli growth are the input data, and the independent validation data is phenotypic data describing the relative growth and product secretion.
The gene expression data used to construct the models consists of CEL files containing the data described in [17], normalized using GCRMA [14]. The data was mapped from genes to reactions using the gene-protein-reaction associations from the reconstruction [18]. The threshold (xcutoff) was set at 12, meaning that reactions assigned a normalized value greater than 12 are assumed to be present; similar results were noted at other thresholds. The RMF was growth on a given carbon source, and the context-specific metabolic networks were forced to grow no less than 90% of optimal growth. Because the evolved strains nearly always grow better than wild-type strains on a variety of carbon sources [19], metabolic networks for optimal growth on nine carbon sources were constructed. The results are shown in Figure 3 (glycerol evolved strains) and Figure 4 (lactate evolved strains). For these figures, the inconsistency scores were used to calculate normalized consistency scores (see Materials and Methods for details); a higher normalized consistency score indicates that the gene expression profile is more consistent with the objective. The figures show that the gene expression state of evolved strains are always more consistent with growth on the nine substrates, paralleling the phenotypic findings from [19] in nearly all cases. These findings demonstrate that the evolved strains have gene expression states that are more consistent (than wild type strains) with usage of the optimal networks for growth on a variety of carbon sources.
These three datasets were originally gathered for purposes completely distinct from creating context-specific metabolic networks, just as the E. coli datasets described earlier were. Nevertheless, they can be interpreted in the context of a genome-scale metabolic network towards this end. All three datasets were collected using Affymetrix (Santa Clara, CA) gene expression arrays. The GB dataset used U133+ 2.0 arrays, while the GI and FO datasets used U133A arrays. While the arrays are similar, the U133+ 2.0 array is able to provide reliable trancriptomic data for 179 reactions beyond what the U133A array can provide. The coverage of these arrays in terms of model reactions is shown in Figure 9. Each probeset that corresponded to a metabolic gene was mapped to that gene, provided that the annotation information for that particular array type indicated that the probeset sequence was unique to either that gene or a closely related gene. Probesets with sequences that correspond to multiple, unrelated genes were ignored. The values associated with the expression of genes were mapped to reactions through the gene-protein-reaction associations, as described earlier and in Materials and Methods. 2ff7e9595c
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