About Lauren Mason

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So far Lauren Mason has created 126 blog entries.

Congrats to Calina Glynn for Being Selected as a 2020-2021 Audree Fowler Fellow in Protein Science

Calina Glynn (Callie) is a fifth year Biochemistry, Molecular and Structural Biology (BMSB) student in Professor Jose Rodriguez’s group. Prior to coming to UCLA in 2016, Callie received her B.A. in Biochemistry and Molecular Biology from Boston University, where she studied Fe-S cluster binding proteins with Dr. Deborah Perlstein.

Callie’s graduate work focuses on uncovering the structures of prion fibrils that bestow them with unique biophysical properties. Prion diseases arise via the self-templated misfolding of the soluble prion protein into pathogenic protease, denaturant, and heat resistant prion fibrils (PrPSc). Callie has uncovered the structure of a protease and denaturant-resistant human prion fibril that explains the unique biophysical properties characteristic of PrPSc using cryo-electron microscopy. Callie aims to uncover differences in the favored fold, stability, and seeding ability of fibrils from disease-associated variants of the human prion protein and other mammalian prion proteins whose aggregation leads to disease.

The title of Callie’s Fowler Fellow talk is “Structures at the Core of Mammalian Prions”.

Congrats to Dr. Marcus Gallagher-Jones for Receiving a 2021 UCLA Postdoctoral Research Award

Dr. Marcus Gallagher-Jones joined Professor Jose Rodriguez’s group in 2017. In the Rodriguez group he has developed pioneering methods in electron diffraction and determining important structures. “Everywhere he goes, Marcus makes a lasting positive impression on colleagues,” said Rodriguez. “He is highly regarded in our structural biology group, is an active member of our NSF-sponsored science and technology center. In short, Marcus is an outstanding colleague and an exceptional young scientist and leader”. Marcus received his bachelor’s degree in Molecular Biology and Biochemistry from Durham University (2010) and his Ph.D. in Molecular Biophysics from the University of Liverpool (2015). While his Ph.D. was awarded by Liverpool, Marcus conducted his thesis work half-way around the world, at one of the most powerful X-ray lasers in the world – the Japanese X-ray free electron laser facility (XFEL) and synchrotron source (Spring-8).

Congrats to Leyla Kabuli for Receiving the 2021 University Medal from UC Berkeley

Leyla Kabuli, a senior graduating in music and electrical engineering and computer sciences, is the winner of the 2021 University Medal from UC Berkeley. The 150-year-old University Medal recognizes a graduating student’s outstanding research, public service and strength of character, and comes with a cash prize. Leyla is currently working in Laura Waller’s research group and will continue with the Waller group as a graduate student in fall 2021.

By her senior year, she was fielding offers of full graduate fellowships from Berkeley, Stanford and MIT. She’s sticking with Berkeley for graduate school. “I might be biased, but Berkeley has the best electrical engineering program in the country,” says Kabuli, who was born in Berkeley and raised in Davis, California. She also credits the campus’s culture, diversity and grit for her decision to accept the Berkeley Fellowship for Graduate Study, which provides financial support for five years. As top graduating senior, Kabuli, 21, a simultaneous degree student in EECS and music, with a perfect 4.0 GPA, will speak this Saturday, May 15, to thousands of her peers, in cap and gown, at a campus-wide virtual commencement ceremony.

 

Congrats to Charles Dove for Receiving a Hertz Fellowship

Charles Dove uses artificial intelligence (AI) to harness the physics of light. A PhD student in electrical engineering at the University of California, Berkeley, Charles uses principles from machine learning and differentiable programming to create new methods for the simulation and fully automatic design of light-based technology. This capability would enable significant growth in the scale, scope, and capabilities of nearly all light-based technology, including biomedical imaging, cellular manipulation and characterization, optical telecommunications, photonic quantum computing, and LIDAR.

A researcher in AI since his freshman year at Clemson University, Charles is the inventor of multiple technologies that combine electromagnetic wave physics and machine learning. His method for the efficient recovery of blood-flow information from scattered laser light is currently being evaluated for potential use in optometry and brain surgery, and his method for 3D artificial vision through multi-frequency scattering offers a promising and practical alternative to conventional LIDAR.

Atomic structures determined from digitally defined nanocrystalline regions

Three-dimensional (3D) structures of molecules determined from nanoscale regions of crystalline arrays could potentially illuminate the subtle differences that engender crystal defects or the multiple states accessible to subpopulations of molecules within an ensemble. A step toward this goal involves the extraction of meaningful diffraction data from 3D regions on the nanoscale. This is achieved using a near-parallel electron beam designed to illuminate sub-10nm regions of a sample. Scanning such a beam across a sample allows for digital logic to be applied to the measured data, facilitating the expostfacto assortment of information and reduction from desired 3D subvolumes.

A STROBE team from UCLA, UC Berkeley and LBNL collaborated to determine the first molecular structures determined by 4DSTEM. The structures were determined from a digitally defined subregion of a nanocrystal. After collecting TB of data, the team obtained reconstructions that revealed the atomic structure of a peptide, and showed that radiation damage imparted on the sample during data collection was not prohibitive for structure determination. Compared to other approaches, the approach allows for a much greater degree of control and obviates the need for spatial separation of samples. New methods, algorithms, enhanced microscopes and advanced sample preparation techniques developed by the STROBE collaboration were key to enabling the success of this project.

Compressive and adaptive nano imaging for enhanced speed and content

Scattering scanning near-field optical microscopy (s-SNOM) provides for spectroscopic imaging from molecular to quantum materials with few nanometer deep sub-diffraction limited spatial resolution. However, conventional acquisition methods are often too slow to fully capture a large field of view spatio-spectral dataset. Through this collaboration, STROBE researchers, at CU Boulder and the ALS –Berkeley, demonstrated how the data acquisition time and sampling rate can be significantly reduced while maintaining or even enhancing the physical or chemical image information content. The novel data acquisition and mathematical concepts implemented are based on advanced data compressed sampling, matrix completion, and adaptive random sampling. This research is of particular interest in synchrotron based nano-imaging facilities. This work paves the way to true spatio-spectral chemical and materials nano-spectroscopy with a reduction of sampling rate by up to 30 times.

New phase retrieval methods enabled by the world’s fastest electron detector

The need for rapid and accurate image analysis is increasing in electron microscopy studies of nanomaterials. With newly developed fast, high-efficiency electron detectors and automated imaging protocols, incorporating electron microscopy into high throughput materials design efforts is becoming possible. These new capabilities strongly motivate automated methods to extract relevant structural features, such as nanoparticle size, shape, and defect content, from high resolution transmission electron microscopy (HRTEM) data to link these features to bulk properties and study the influence of heterogeneity on bulk behavior. In general, protocols that surpass the accuracy of traditional image analysis and do not require time-consuming manual analysis are needed. Recent advances in image interpretation using deep learning using machine learning make it a promising route toward automatic interpretation of HRTEM micrographs.

In this STROBE collaboration, we demonstrate a pipeline to detect and classify regions of interest in HRTEM micrographs. Our pipeline uses a convolutional neural net (CNN) to identify crystalline regions (nanoparticles) from an amorphous background in the images, and then feeds individual regions of interest into a random forest classifier to detect whether or not they contain a crystallographic defect. Our CNN has a lightweight U-Net architecture and accurately segments a diverse population of nanoparticles with only a small number of training images. After segmentation, individual nanoparticle regions can be isolated and fed directly into existing python tools to extract size and shape statistics. To detect the presence of defects in nanoparticle regions, we implement a random forest classifier. We demonstrated the random forest classifier’s ability to detect stacking faults in the CdSe subset of identified nanoparticles. Both the CNN and classifier demonstrate state of the art performance at their respective tasks. While this work focuses on HRTEM images of nanoparticles supported on a carbon substrate, in principle the tool can be used to detect any regions of crystallinity in HRTEM data.

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