mProX™ Human TTK Stable Cell Line

Lukasczyk, Jonas. et al. "ExTreeM: Scalable Augmented Merge Tree Computation via Extremum Graphs." IEEE transactions on visualization and computer graphics, 2023.
In recent years, merge trees have become a popular method for visualizing and analyzing complex datasets due to their effectiveness in simplifying the data. This paper presents the ExTreeM-Algorithm, a scalable approach for computing merge trees using extremum graphs. The algorithm first computes the extremum graph G for an input scalar field f defined on a cell complex K, and then derives the unaugmented merge tree for f on G. This results in faster computation times as G typically has fewer cells than K. To further speed up the process, ExTreeM includes a specialized procedure for computing merge trees on extremum graphs. A fully augmented merge tree, or a merge tree domain segmentation of K, can also be obtained in a separate post-processing step. All steps of the ExTreeM algorithm can be executed efficiently in parallel and its correctness is formally proven. The algorithm is tested on various publicly available datasets and demonstrates a significant improvement in speed, requiring less memory and showing strong scaling behavior compared to existing algorithms in TTK and VTK-m software libraries.
Lukasczyk, Jonas. et al. "ExTreeM: Scalable Augmented Merge Tree Computation via Extremum Graphs." IEEE transactions on visualization and computer graphics, 2023.
Pubmed: 37871087   DOI: 10.1109/TVCG.2023.3326526

Sebák, Fanni, et al. "Assignment of the disordered, proline-rich N-terminal domain of the tumour suppressor p53 protein using 1HN and 1Hα-detected NMR measurements." Biomolecular NMR Assignments (2023): 1-6.
The protein p53 has a significant role in the suppression of tumors and is well-known for its importance in this area. Studies have revealed that mutations in the p53 gene are frequently observed during oncogenic transformation. Researchers are continuously conducting studies to identify methods for targeting disordered proteins and protein regions in order to develop effective cancer therapies. However, obtaining atomic level information is crucial for these studies. The N-terminal region of p53 is especially challenging to analyze due to its high content of proline residues, including repetitive Pro-Ala motifs, which lack amide protons in the 1H-NMR spectrum.
Sebák, Fanni, et al. "Assignment of the disordered, proline-rich N-terminal domain of the tumour suppressor p53 protein using 1HN and 1Hα-detected NMR measurements." Biomolecular NMR Assignments (2023): 1-6.
Pubmed: 37861971   DOI: 10.1007/s12104-023-10160-4