Construction of Multi-Resolution Model of Pancreas Tumor

2021 ◽  
pp. 17-26
Author(s):  
Hidekata Hontani ◽  
Tomoshige Shimomura ◽  
Tatsuya Yokota ◽  
Mauricio Kugler ◽  
Tomonari Sei ◽  
...  
Keyword(s):  
Pancreas Tumor ◽  
Resolution Model

Three-Dimensional reconstruction of isolated centrosomes by automated electron tomography

1994 ◽  
Vol 52 ◽  
pp. 310-311
Author(s):  
M.B. Braunfeld ◽  
M. Moritz ◽  
B.M. Alberts ◽  
J.W. Sedat ◽  
D.A. Agard
Keyword(s):  
Electron Tomography ◽  
Three Dimensional ◽  
Vital Role ◽  
Complex Nature ◽  
Animal Cells ◽  
Microtubule Organizing Center ◽  
Nucleation Sites ◽  
Dimensional Reconstruction ◽  
Organizing Center ◽  
Resolution Model

In animal cells, the centrosome functions as the primary microtubule organizing center (MTOC). As such the centrosome plays a vital role in determining a cell's shape, migration, and perhaps most importantly, its division. Despite the obvious importance of this organelle little is known about centrosomal regulation, duplication, or how it nucleates microtubules. Furthermore, no high resolution model for centrosomal structure exists.We have used automated electron tomography, and reconstruction techniques in an attempt to better understand the complex nature of the centrosome. Additionally we hope to identify nucleation sites for microtubule growth.Centrosomes were isolated from early Drosophila embryos. Briefly, after large organelles and debris from homogenized embryos were pelleted, the resulting supernatant was separated on a sucrose velocity gradient. Fractions were collected and assayed for centrosome-mediated microtubule -nucleating activity by incubating with fluorescently-labeled tubulin subunits. The resulting microtubule asters were then spun onto coverslips and viewed by fluorescence microscopy.

scholarly journals Small Angle X-ray Scattering, Molecular Modeling, and Chemometric Studies from a Thrombin-Like (Lmr-47) Enzyme of Lachesis m. rhombeata Venom

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3930
Author(s):  
Salvatore Giovanni De-Simone ◽  
Guilherme Curty Lechuga ◽  
Paloma Napoleão-Pêgo ◽  
Larissa Rodrigues Gomes ◽  
David William Provance ◽  
...  
Keyword(s):  
Molecular Modeling ◽  
3D Structure ◽  
Ph Variation ◽  
Critical Elements ◽  
X Ray Scattering ◽  
Plot Analysis ◽  
Competitive Inhibitors ◽  
Resolution Model ◽  
Lachesis Muta ◽  
Lachesis Muta Rhombeata

Introduction: Snakebite envenomation is considered a neglected tropical disease, and SVTLEs critical elements are involved in serious coagulopathies that occur on envenoming. Although some enzymes of this group have been structurally investigated, it is essential to characterize other proteins to better understand their unique properties such as the Lachesis muta rhombeata 47 kDa (Lmr-47) venom serine protease. Methods: The structure of Lmr-47 was studied in solution, using SAXS, DLS, CD, and in silico by homology modeling. Molecular docking experiments simulated 21 competitive inhibitors. Results: At pH 8.0, Lmr-47 has an Rg of 34.5 ± 0.6 Å, Dmax of 130 Å, and SR of 50 Å, according to DLS data. Kratky plot analysis indicates a rigid shape at pH 8.0. Conversely, the pH variation does not change the center of mass’s intrinsic fluorescence, possibly indicating the absence of fluorescent amino acids in the regions affected by pH variation. CD experiments show a substantially random coiled secondary structure not affected by pH. The low-resolution model of Lmr-47 presented a prolate elongated shape at pH 8.0. Using the 3D structure obtained by molecular modeling, docking experiments identified five good and three suitable competitive inhibitors. Conclusion: Together, our work provided insights into the structure of the Lmr-47 and identified inhibitors that may enhance our understanding of thrombin-like family proteins.

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

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