TFIT: A Photometry Package Using Prior Information for Mixed‐Resolution Data Sets

2007 ◽  
Vol 119 (861) ◽  
pp. 1325-1344 ◽  
Author(s):  
Victoria G. Laidler ◽  
Casey Papovich ◽  
Norman A. Grogin ◽  
Rafal Idzi ◽  
Mark Dickinson ◽  
...  
Keyword(s):  
Prior Information ◽  
Data Sets ◽  
Mixed Resolution ◽  
Resolution Data

scholarly journals A Multiplicative Regularizer Augmented with Spatial Priors for Microwave Imaging

2020 ◽  
Author(s):  
Nozhan Bayat ◽  
Puyan Mojabi
Keyword(s):  
Experimental Data ◽  
Total Variation ◽  
Prior Information ◽  
Microwave Imaging ◽  
Minimal Change ◽  
Data Sets ◽  
Quantitative Accuracy ◽  
L2 Norm ◽  
Imaging Algorithms ◽  
Multiplicative Regularization

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account<br>structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from<br>microwave imaging.

scholarly journals An overview of heavy-atom derivatization of protein crystals

2016 ◽  
Vol 72 (3) ◽  
pp. 303-318 ◽  
Author(s):  
Ashley C. W. Pike ◽  
Elspeth F. Garman ◽  
Tobias Krojer ◽  
Frank von Delft ◽  
Elisabeth P. Carpenter
Keyword(s):  
Heavy Atom ◽  
Protein Crystals ◽  
Data Sets ◽  
Low Resolution ◽  
Heavy Atoms ◽  
Density Maps ◽  
Resolution Electron ◽  
First Choice ◽  
First Time ◽  
Resolution Data

Heavy-atom derivatization is one of the oldest techniques for obtaining phase information for protein crystals and, although it is no longer the first choice, it remains a useful technique for obtaining phases for unknown structures and for low-resolution data sets. It is also valuable for confirming the chain trace in low-resolution electron-density maps. This overview provides a summary of the technique and is aimed at first-time users of the method. It includes guidelines on when to use it, which heavy atoms are most likely to work, how to prepare heavy-atom solutions, how to derivatize crystals and how to determine whether a crystal is in fact a derivative.

scholarly journals A Multiplicative Regularizer Augmented with Spatial Priors for Microwave Imaging

2020 ◽  
Author(s):  
Nozhan Bayat ◽  
Puyan Mojabi
Keyword(s):  
Experimental Data ◽  
Total Variation ◽  
Prior Information ◽  
Microwave Imaging ◽  
Minimal Change ◽  
Data Sets ◽  
Quantitative Accuracy ◽  
L2 Norm ◽  
Imaging Algorithms ◽  
Multiplicative Regularization

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account<br>structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from<br>microwave imaging.

scholarly journals A Multiplicative Regularizer Augmented with Spatial Priors for Microwave Imaging

2020 ◽  
Author(s):  
Nozhan Bayat ◽  
Puyan Mojabi
Keyword(s):  
Experimental Data ◽  
Total Variation ◽  
Prior Information ◽  
Microwave Imaging ◽  
Minimal Change ◽  
Data Sets ◽  
Quantitative Accuracy ◽  
L2 Norm ◽  
Imaging Algorithms ◽  
Multiplicative Regularization

The standard weighted L2 norm total variation multiplicative regularization (MR) term originally developed for microwave imaging algorithms is modified to take into account<br>structural prior information, also known as spatial priors (SP), about the object being imaged. This modification adds one extra term to the integrand of the standard MR, thus, being referred to as an augmented MR (AMR). The main advantage of the proposed approach is that it requires a minimal change to the existing microwave imaging algorithms that are already equipped with the MR. Using two experimental data sets, it is shown that the proposed AMR (i) can handle partial SP, and (ii) can, to some extent, enhance the quantitative accuracy achievable from<br>microwave imaging.

scholarly journals Quantitative 3D imaging of the cranial microvascular environment at single-cell resolution

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Alexandra N. Rindone ◽  
Xiaonan Liu ◽  
Stephanie Farhat ◽  
Alexander Perdomo-Pantoja ◽  
Timothy F. Witham ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Blood Vessels ◽  
3D Imaging ◽  
Conditional Knockout ◽  
Cranial Bone ◽  
Data Sets ◽  
Bone Biology ◽  
High Resolution Data ◽  
Resolution Data

AbstractVascularization is critical for skull development, maintenance, and healing. Yet, there remains a significant knowledge gap in the relationship of blood vessels to cranial skeletal progenitors during these processes. Here, we introduce a quantitative 3D imaging platform to enable the visualization and analysis of high-resolution data sets (>100 GB) throughout the entire murine calvarium. Using this technique, we provide single-cell resolution 3D maps of vessel phenotypes and skeletal progenitors in the frontoparietal cranial bones. Through these high-resolution data sets, we demonstrate that CD31hiEmcnhi vessels are spatially correlated with both Osterix+ and Gli1+ skeletal progenitors during postnatal growth, healing, and stimulated remodeling, and are concentrated at transcortical canals and osteogenic fronts. Interestingly, we find that this relationship is weakened in mice with a conditional knockout of PDGF-BB in TRAP+ osteoclasts, suggesting a potential role for osteoclasts in maintaining the native cranial microvascular environment. Our findings provide a foundational framework for understanding how blood vessels and skeletal progenitors spatially interact in cranial bone, and will enable more targeted studies into the mechanisms of skull disease pathologies and treatments. Additionally, our technique can be readily adapted to study numerous cell types and investigate other elusive phenomena in cranial bone biology.

scholarly journals Paired refinement under the control of PAIREF

IUCrJ ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 681-692
Author(s):  
Martin Malý ◽  
Kay Diederichs ◽  
Jan Dohnálek ◽  
Petr Kolenko
Keyword(s):  
High Resolution ◽  
Diffraction Data ◽  
Structure Refinement ◽  
Data Sets ◽  
High Resolution Data ◽  
Density Maps ◽  
New Feature ◽  
And Control ◽  
The Impact ◽  
Resolution Data

Crystallographic resolution is a key characteristic of diffraction data and represents one of the first decisions an experimenter has to make in data evaluation. Conservative approaches to the high-resolution cutoff determination are based on a number of criteria applied to the processed X-ray diffraction data only. However, high-resolution data that are weaker than arbitrary cutoffs can still result in the improvement of electron-density maps and refined structure models. Therefore, the impact of reflections from resolution shells higher than those previously used in conservative structure refinement should be analysed by the paired refinement protocol. For this purpose, a tool called PAIREF was developed to provide automation of this protocol. As a new feature, a complete cross-validation procedure has also been implemented. Here, the design, usage and control of the program are described, and its application is demonstrated on six data sets. The results prove that the inclusion of high-resolution data beyond the conventional criteria can lead to more accurate structure models.

High-Resolution Data Sets Unravel the Effects of Sources and Meteorological Conditions on Nitrate and Its Gas-Particle Partitioning

2019 ◽  
Vol 53 (6) ◽  
pp. 3048-3057 ◽  
Author(s):  
Xurong Shi ◽  
Athanasios Nenes ◽  
Zhimei Xiao ◽  
Shaojie Song ◽  
Haofei Yu ◽  
...  
Keyword(s):  
High Resolution ◽  
Meteorological Conditions ◽  
Data Sets ◽  
High Resolution Data ◽  
Resolution Data

Topic Tracking Based on Identifying Proper Number of the Latent Topics in Documents

2012 ◽  
Vol 16 (5) ◽  
pp. 611-618 ◽  
Author(s):  
Midori Serizawa ◽  
◽  
Ichiro Kobayashi
Keyword(s):  
Dirichlet Process ◽  
Prior Information ◽  
Latent Dirichlet Allocation ◽  
Experimental Results ◽  
Data Sets ◽  
Hierarchical Dirichlet Process ◽  
Proper Number ◽  
Latent Topics ◽  
Topic Tracking ◽  
Do So

In this paper, we propose a method for detecting and tracking topics of newspaper articles based on the latent semantics of the documents. We use Latent Dirichlet Allocation (LDA) to extract latent topics. In using LDA, we have to provide the number of latent topics in target documents in advance. To do so, perplexity is widely used as a metric for estimating the number of latent topics in documents. As a solution, we estimate the number of latent topics without any prior information in the case of using Hierarchical Dirichlet Process LDA (HDP-LDA). We propose a method to estimate the number of latent topics in target documents based on calculating the similarity among extracted topics, and conduct an experiment with three data sets to compare the method with the above two representative methods, i.e., HDP-LDA and LDA using perplexity. From experimental results, we confirmed that our method can provide results similar to that of HDP-LDA. We also detect and track topics by means of our proposed method and confirm that our method is useful.

Sign in / Sign up

Export Citation Format

Share Document