לוח שנה משולב

Zoom Lecture: Link: https://weizmann.zoom.us/j/98578269625

Magnetometry on the nanoscale range stands to benefit from quantum-enhanced sensing techniques, as these can potentially overcome classical noise limits. Specifically in our group we use a single nitrogen-vacancy center as an atomic-sized quantum sensor, with uT(nT) magnetic field sensitivity for dc(ac) fields.

Yet our measurement technique does rely on classical averaging due to a relatively poor signal-to-noise ratio. In this respect, a real-time response and feedback during signal acquisition based on (quantum) phase estimation promises to significantly reduce averaging time by using prior information obtained during the measurement.

I will present our current efforts in this direction, with the aim of performing adaptive sensing of nanoscale magnetic fields. Both static (dc) and dynamic (ac) problems will be addressed.

Finally, since this is after all a magnetic resonance seminar, I will present the relevant research context pertaining to electron spin resonance of small molecules and explain how we intend to reach the single molecule limit with this quantum sensing technology.

Zoom Lecture: https://weizmann.zoom.us/j/99058507421


Magnetic Resonance Imaging (MRI) is a superb imaging modality that provides high-quality images of the human body. However, one of its major limitations is the long acquisition time, which hinders the MRI clinical use. The acquisition time can be shortened by acquiring less data; however, this requires suitable methods for accurate image reconstruction from subsampled data, which is acquired with a sub-Nyquist rate.
In this seminar, four novel methods for image reconstruction from subsampled data will be presented. These methods build upon the well-established frameworks of Parallel Imaging (PI) and Compressed Sensing (CS), utilize a-priori knowledge about data sparsity, and address current limitations of PI-CS methods. The first two methods accelerate static MRI scans by introducing the Convolution-based Reconstruction (CORE) framework, which offers a parameter-free non-iterative reconstruction. Experiments with in-vivo 7T brain data demonstrated that these methods perform comparably to the well-established GRAPPA and l1-SPIRiT methods, with the advantage of shorter computation times and reduced need for parameter calibration. The next two developed methods accelerate dynamic MRI scans that provide temperature monitoring in High Intensity Focused Ultrasound (MRgHIFU) thermal ablation treatments. The developed methods enable rapid MR monitoring by reconstructing temperature changes from subsampled data. Validation experiments were performed with in-vivo data from clinical treatments of prostate cancer in humans; these showed that the proposed methods significantly outperform two state-of-the-art methods in the temperature reconstruction task

Ortholog protein complexes are responsible for equivalent functions in different organisms. However, during evolution, each organism adapts to meet its physiological needs and the environmental challenges imposed by its niche. This selection pressure leads to structural diversity in protein complexes, which are often difficult to specify. Here, we describe a multilevel experimental approach based on native mass spectrometry (MS) tools for elucidating the structural preservation and variations among highly related protein complexes. The 20S proteasome, an essential protein degradation machinery, served as our model system. We compare between four different eukaryotic 20S proteasomes: yeast (Saccharomyces cerevisiae) and mammals (rat - Rattus norvegicus, rabbit - Oryctolagus cuniculus and human - HEK293 cells). Our study revealed that out of four different orthologs the yeast complex, and not those in mammals, was the largest in size and displayed the greatest degree of kinetic stability. Moreover, we also identified a new proteoform of the PSMA7 subunit that resides within the rat and rabbit complexes, which to our knowledge have not been previously described. Altogether, our strategy enables elucidation of the unique structural properties of protein complexes that are highly similar to one another, a framework that is valid not only to ortholog protein complexes, but also for other highly related protein assemblies.