scholarly journals When two bad data sets are better than one

2014 ◽  
Vol 70 (a1) ◽  
pp. C185-C185
Author(s):  
Stef Smeets ◽  
Lynne McCusker ◽  
Christian Baerlocher ◽  
Dan Xie ◽  
Stacey Zones ◽  
...  
Keyword(s):  
Rietveld Refinement ◽  
Poor Quality ◽  
Data Sets ◽  
Red Cell ◽  
Structure Directing Agent ◽  
Cell Parameters ◽  
Combining Data ◽  
Unit Cells ◽  
High Silica ◽  
High Degree

High-resolution synchrotron X-ray powder diffraction (SXPD) data alone are sometimes not enough to solve the structure of a complex polycrystalline material. Such was the case for the high-silica zeolites SSZ-61 and SSZ-87, where combining data from different sources, in particular XPD and electron microscopy, was vital to success. For SSZ-61, the SXPD data feature broad peaks and a resolution of ca. 1.2 Å. Although the pattern could be indexed, structure determination failed both with the charge flipping routine in SUPERFLIP [1] and with the zeolite-specific program FOCUS [2]. The unit cell parameters and HRTEM images indicated a relationship with ZSM-12 (MTW) and SSZ-59 (SFN), so several models derived from these two frameworks were built. Eventually, after considering Si-29 MAS NMR data and the size of the organic structure directing agent (SDA), a framework model that fits all the data emerged. To complete the structure, the SDA was included as a rigid-body, and its location and orientation optimized using simulated annealing. Subsequent Rietveld refinement confirmed the structure. In contrast to SSZ-61, the SXPD pattern for SSZ-87 was quite good, and it could be indexed with a C-centered cell. However, structure solution failed, probably because of the very high degree of reflection overlap (93%). Therefore, rotation electron diffraction (RED) data [3] were collected, but they proved to be of low resolution and poor quality. Only 2 of the 7 data sets could be indexed, and these had different unit cells. Neither fit the XPD pattern directly. The problem was traced to large errors in the RED cell parameters, and eventually one RED cell could be transformed to one similar to the SXPD cell. The RED data with this cell was only 15% complete up to a resolution of 1.22 Å. Even so, the structure could be solved using a recently developed version of FOCUS that works with ED data. The SDA was found as for SSZ-61, and the structure then confirmed by Rietveld refinement.

Image Inpainting for Missing Values in Observational Climate Datasets Using Partial Convolutions in a cuDNN

2020 ◽  
Author(s):  
Christopher Kadow ◽  
David Hall ◽  
Uwe Ulbrich
Keyword(s):  
Missing Values ◽  
Coupled Model ◽  
Image Inpainting ◽  
Climate Science ◽  
Data Sets ◽  
Learning Technology ◽  
Climate Change Research ◽  
The Past ◽  
Combining Data ◽  
High Degree

<p>Nowadays climate change research relies on climate information of the past. Historic climate records of temperature observations form global gridded datasets like HadCRUT4, which is investigated e.g. in the IPCC reports. However, record combining data-sets are sparse in the past. Even today they contain missing values. Here we show that machine learning technology can be applied to refill these missing climate values in observational datasets. We found that the technology of image inpainting using partial convolutions in a CUDA accelerated deep neural network can be trained by large Earth system model experiments from NOAA reanalysis (20CR) and the Coupled Model Intercomparison Project phase 5 (CMIP5). The derived deep neural networks are capable to independently refill added missing values of these experiments. The analysis shows a very high degree of reconstruction even in the cross-reconstruction of the trained networks on the other dataset. The network reconstruction reaches a better evaluation than other typical methods in climate science. In the end we will show the new reconstructed observational dataset HadCRUT4 and discuss further investigations.</p>

Structure of the M2CuWO6 System, with M = Ba or Sr

1992 ◽  
Vol 7 (4) ◽  
pp. 228-230 ◽  
Author(s):  
Bokhimi
Keyword(s):  
Rietveld Refinement ◽  
Crystalline Structure ◽  
Powder Diffraction ◽  
Perovskite Structure ◽  
Room Temperature ◽  
Space Group ◽  
X Ray ◽  
Cell Parameters ◽  
Unit Cells

AbstractThe crystalline structure of the M2CuWO6 phases with M = Ba or Sr was obtained from X-ray powder diffraction at room temperature with CuKα radiation. The phases are isostructural with tetragonal unit cells, space group I4/m and Z = 2. The parameters for the Sr2CuWO6 phase are: Mr = 518.6, a = 5.42693(5) Å, c = 8.4087(1) Å, V = 247.65(1) Å3, Dx = 6.954 Mg/m3, μ = 77.75 mm−1, F(000) = 454, R = 0.0375 for 24 reflections; the parameters for the Ba2CuWO6 phase are: Mr = 618.1, a = 5.56392(8) Å, c = 8.6274(1) Å, V = 267.08(1) Å3, Dx = 7.683 Mg/m3, μ = 157.0 mm−1, F(000) = 526, R = 0.0506 for 27 reflections. Cell parameters were obtained from Rietveld refinement. The crystalline structure is based on the perovskite structure. It is laminar with ordered alternating WO6 deformed octahedra and CuO2 planar squares along the [110] direction, joined by corners and rotated perpendicular to the [001] direction. The samples are electrica insulators.

scholarly journals Improved reproducibility of unit-cell parameters in macromolecular cryocrystallography by limiting dehydration during crystal mounting

2014 ◽  
Vol 70 (8) ◽  
pp. 2111-2124 ◽  
Author(s):  
Christopher Farley ◽  
Geoffry Burks ◽  
Thomas Siegert ◽  
Douglas H. Juers
Keyword(s):  
Crystal Size ◽  
Ambient Air ◽  
Unit Cell ◽  
Data Sets ◽  
Unit Cell Parameters ◽  
Cell Parameters ◽  
Ambient Relative Humidity ◽  
Combining Data ◽  
Low Humidity ◽  
Cell Variation

In macromolecular cryocrystallography unit-cell parameters can have low reproducibility, limiting the effectiveness of combining data sets from multiple crystals and inhibiting the development of defined repeatable cooling protocols. Here, potential sources of unit-cell variation are investigated and crystal dehydration during loop-mounting is found to be an important factor. The amount of water lost by the unit cell depends on the crystal size, the loop size, the ambient relative humidity and the transfer distance to the cooling medium. To limit water loss during crystal mounting, a threefold strategy has been implemented. Firstly, crystal manipulations are performed in a humid environment similar to the humidity of the crystal-growth or soaking solution. Secondly, the looped crystal is transferred to a vial containing a small amount of the crystal soaking solution. Upon loop transfer, the vial is sealed, which allows transport of the crystal at its equilibrated humidity. Thirdly, the crystal loop is directly mounted from the vial into the cold gas stream. This strategy minimizes the exposure of the crystal to relatively low humidity ambient air, improves the reproducibility of low-temperature unit-cell parameters and offers some new approaches to crystal handling and cryoprotection.

scholarly journals Monitoring protein precipitates by in-house X-ray powder diffraction

2013 ◽  
Vol 28 (S2) ◽  
pp. S458-S469 ◽  
Author(s):  
Kenny Ståhl ◽  
Christian G. Frankær ◽  
Jakob Petersen ◽  
Pernille Harris
Keyword(s):  
Powder Diffraction ◽  
Protein Crystal ◽  
Data Sets ◽  
Crystal Forms ◽  
X Ray ◽  
Cell Parameters ◽  
X Ray Scattering ◽  
Source Organism ◽  
Peak Asymmetry ◽  
Overlapping Peaks

Powder diffraction from protein powders using in-house diffractometers is an effective tool for identification and monitoring of protein crystal forms and artifacts. As an alternative to conventional powder diffractometers a single crystal diffractometer equipped with an X-ray micro-source can be used to collect powder patterns from 1 µl samples. Using a small-angle X-ray scattering (SAXS) camera it is possible to collect data within minutes. A streamlined program has been developed for the calculation of powder patterns from pdb-coordinates, and includes correction for bulk-solvent. A number of such calculated powder patterns from insulin and lysozyme have been included in the powder diffraction database and successfully used for search-match identification. However, the fit could be much improved if peak asymmetry and multiple bulk-solvent corrections were included. When including a large number of protein data sets in the database some problems can be foreseen due to the large number of overlapping peaks in the low-angle region, and small differences in unit cell parameters between pdb-data and powder data. It is suggested that protein entries are supplied with more searchable keywords as protein name, protein type, molecular weight, source organism etc. in order to limit possible hits.

scholarly journals Unbiased least-squares modification of Stokes’ formula

2020 ◽  
Vol 94 (9) ◽  
Author(s):  
Lars E. Sjöberg
Keyword(s):  
Gravity Data ◽  
Harmonic Series ◽  
Local Error ◽  
Data Sets ◽  
Spherical Cap ◽  
Local Variance ◽  
Error Covariance ◽  
Stokes Formula ◽  
Gravity Signal ◽  
High Degree

Abstract As the KTH method for geoid determination by combining Stokes integration of gravity data in a spherical cap around the computation point and a series of spherical harmonics suffers from a bias due to truncation of the data sets, this method is based on minimizing the global mean square error (MSE) of the estimator. However, if the harmonic series is increased to a sufficiently high degree, the truncation error can be considered as negligible, and the optimization based on the local variance of the geoid estimator makes fair sense. Such unbiased types of estimators, derived in this article, have the advantage to the MSE solutions not to rely on the imperfectly known gravity signal degree variances, but only the local error covariance matrices of the observables come to play. Obviously, the geoid solution defined by the local least variance is generally superior to the solution based on the global MSE. It is also shown, at least theoretically, that the unbiased geoid solutions based on the KTH method and remove–compute–restore technique with modification of Stokes formula are the same.

A powerful structure-directing agent for the synthesis of nanosized Al- and high-silica zeolite Beta in alkaline medium

2011 ◽  
Vol 142 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Olivier Larlus ◽  
Svetlana Mintova ◽  
Stephen T. Wilson ◽  
Richard R. Willis ◽  
Hayim Abrevaya ◽  
...  
Keyword(s):  
Alkaline Medium ◽  
Zeolite Beta ◽  
Structure Directing Agent ◽  
High Silica

Crystallization and preliminary X-ray studies of sialidase L from the leech Macrobdella decora

1998 ◽  
Vol 54 (1) ◽  
pp. 111-113 ◽  
Author(s):  
Yu Luo ◽  
Min-yuan Chou ◽  
Su-chen Li ◽  
Yu-teh Li ◽  
Ming Luo
Keyword(s):  
Escherichia Coli ◽  
Unit Cell ◽  
Data Sets ◽  
Molecular Replacement ◽  
Unit Cell Parameters ◽  
Solvent Content ◽  
X Ray ◽  
Cell Parameters ◽  
Mercury Derivative ◽  
Macrobdella Decora

Functional monomeric 83 kDa sialidase L, a NeuAcα2→3Gal-specific sialidase from Macrobdella leech, was expressed in Escherichia coli and readily crystallized by a macroseeding technique. The crystal belongs to space group P1 with unit-cell parameters a = 46.4, b = 69.3, c = 72.5 Å, α = 113.5, β = 95.4 and γ = 107.3°. There is one molecule per unit cell, giving a Vm = 2.4 Å3 Da−1 and a solvent content of 40%. Native and mercury-derivative data sets were collected to 2.0 Å resolution. Threading and molecular-replacement calculations confirmed the existence of a bacterial sialidase-like domain.

scholarly journals Crystallization and X-ray diffraction analysis of native and selenomethionine-substituted PhyH-DI fromBacillussp. HJB17

2017 ◽  
Vol 73 (11) ◽  
pp. 607-611
Author(s):  
Fang Lu ◽  
Bei Zhang ◽  
Yong Liu ◽  
Ying Song ◽  
Gangxing Guo ◽  
...  
Keyword(s):  
Unit Cell ◽  
Diffusion Method ◽  
Data Sets ◽  
Asymmetric Unit ◽  
X Ray Diffraction ◽  
Unit Cell Parameters ◽  
Solvent Content ◽  
Space Group ◽  
X Ray ◽  
Cell Parameters

Phytases are phosphatases that hydrolyze phytates to less phosphorylatedmyo-inositol derivatives and inorganic phosphate. β-Propeller phytases, which are very diverse phytases with improved thermostability that are active at neutral and alkaline pH and have absolute substrate specificity, are ideal substitutes for other commercial phytases. PhyH-DI, a β-propeller phytase fromBacillussp. HJB17, was found to act synergistically with other single-domain phytases and can increase their efficiency in the hydrolysis of phytate. Crystals of native and selenomethionine-substituted PhyH-DI were obtained using the vapour-diffusion method in a condition consisting of 0.2 Msodium chloride, 0.1 MTris pH 8.5, 25%(w/v) PEG 3350 at 289 K. X-ray diffraction data were collected to 3.00 and 2.70 Å resolution, respectively, at 100 K. Native PhyH-DI crystals belonged to space groupC121, with unit-cell parametersa = 156.84,b = 45.54,c = 97.64 Å, α = 90.00, β = 125.86, γ = 90.00°. The asymmetric unit contained two molecules of PhyH-DI, with a corresponding Matthews coefficient of 2.17 Å3 Da−1and a solvent content of 43.26%. Crystals of selenomethionine-substituted PhyH-DI belonged to space groupC2221, with unit-cell parametersa = 94.71,b= 97.03,c= 69.16 Å, α = β = γ = 90.00°. The asymmetric unit contained one molecule of the protein, with a corresponding Matthews coefficient of 2.44 Å3 Da−1and a solvent content of 49.64%. Initial phases for PhyH-DI were obtained from SeMet SAD data sets. These data will be useful for further studies of the structure–function relationship of PhyH-DI.

scholarly journals Dynamic changes in murine erythropoiesis from birth to adulthood: implications for the study of murine models of anemia

2020 ◽  
Vol 5 (1) ◽  
pp. 16-25
Author(s):  
Lixiang Chen ◽  
Jie Wang ◽  
Jing Liu ◽  
Hua Wang ◽  
Christopher D. Hillyer ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Steady State ◽  
Hemoglobin Concentration ◽  
Red Cell ◽  
Murine Models ◽  
Erythropoietic Activity ◽  
Dynamic Changes ◽  
Newborn Mice ◽  
Mean Cell Volume ◽  
Cell Parameters

Abstract Liver, spleen, and bone marrow are 3 key erythropoietic tissues in mammals. In the mouse, the liver is the predominant site of erythropoiesis during fetal development, the spleen responds to stress erythropoiesis, and the bone marrow is involved in maintaining homeostatic erythropoiesis in adults. However, the dynamic changes and respective contributions of the erythropoietic activity of these tissues from birth to adulthood are incompletely defined. Using C57BL/6 mice, we systematically examined the age-dependent changes in liver, spleen, and bone marrow erythropoiesis following birth. In addition to bone marrow, the liver and spleen of newborn mice sustain an active erythropoietic activity that is gradually lost during first few weeks of life. While the erythropoietic activity of the liver is lost 1 week after birth, that of the spleen is maintained for 7 weeks until the erythropoietic activity of the bone marrow is sufficient to sustain steady-state adult erythropoiesis. Measurement of the red cell parameters demonstrates that these postnatal dynamic changes are reflected by varying indices of circulating red cells. While the red cell numbers, hemoglobin concentration, and hematocrit progressively increase after birth and reach steady-state levels by week 7, reticulocyte counts decrease during this time period. Mean cell volume and mean cell hemoglobin progressively decrease and reach steady state by week 3. Our findings provide comprehensive insights into developmental changes of murine erythropoiesis postnatally and have significant implications for the appropriate interpretation of findings from the variety of murine models used in the study of normal and disordered erythropoiesis.

scholarly journals T-SNE visualization of large-scale neural recordings

2016 ◽  
Author(s):  
George Dimitriadis ◽  
Joana Neto ◽  
Adam R. Kampff
Keyword(s):  
Large Scale ◽  
New Technologies ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Data Sets ◽  
Neural Recordings ◽  
Sorting Problem ◽  
Feature Spaces ◽  
High Degree

AbstractElectrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention [22, 23], but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method [27] as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid [23] and paired juxtacellular/extracellular recordings [15]. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curration of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

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