Researchers at University of California San Diego School of Medicine report that detection of "copy editing" by a stem cell enzyme called ADAR1, which is active in more than 20 tumor types, may provide a kind of molecular radar for early detection of malignancies and represent a new therape ... more
Machine learning technique speeds up crystal structure determination
Nanoengineers at the University of California San Diego have developed a computer-based method that could make it less labor-intensive to determine the crystal structures of various materials and molecules, including alloys, proteins and pharmaceuticals. The method uses a machine learning algorithm, similar to the type used in facial recognition and self-driving cars, to independently analyze electron diffraction patterns, and do so with at least 95% accuracy.
A team led by UC San Diego nanoengineering professor Kenneth Vecchio and his Ph.D. student Kevin Kaufmann, who is the first author of the paper, developed the new approach. Their method involves using a scanning electron microscope (SEM) to collect electron backscatter diffraction (EBSD) patterns. Compared to other electron diffraction techniques, such as those in transmission electron microscopy (TEM), SEM-based EBSD can be performed on large samples and analyzed at multiple length scales. This provides local sub-micron information mapped to centimeter scales. For example, a modern EBSD system enables determination of fine-scale grain structures, crystal orientations, relative residual stress or strain, and other information in a single scan of the sample.
However, the drawback of commercial EBSD systems is the software's inability to determine the atomic structure of the crystalline lattices present within the material being analyzed. This means a user of the commercial software must select up to five crystal structures presumed to be in the sample and then the software attempts to find probable matches to the diffraction pattern. The complex nature of the diffraction pattern often causes the software to find false structure matches in the user selected list. As a result, the accuracy of the existing software's determination of the lattice type is dependent on the operator's experience and prior knowledge of their sample.
The method that Vecchio's team developed does this all autonomously, as the deep neural network independently analyzes each diffraction pattern to determine the crystal lattice, out of all possible lattice structure types, with a high degree of accuracy (greater than 95%).
A wide range of research areas including pharmacology, structural biology, and geology are expected to benefit from using similar automated algorithms to reduce the amount of time required for crystal structural identification, researchers said.
- crystal structures
- scanning electron m…
- electron diffraction
By stacking and connecting layers of stretchable circuits on top of one another, engineers have developed an approach to build soft, pliable "3D stretchable electronics" that can pack a lot of functions while staying thin and small in size. As a proof of concept, a team led by the Universit ... more
By doping alumina crystals with neodymium ions, engineers at the University of California San Diego have developed a new laser material that is capable of emitting ultra-short, high-power pulses--a combination that could potentially yield smaller, more powerful lasers with superior thermal ... more
A team of mechanical engineers at the University of California San Diego has successfully used acoustic waves to move fluids through small channels at the nanoscale. The breakthrough is a first step toward the manufacturing of small, portable devices that could be used for drug discovery an ... more
- 1Virtual screening for active substances against the coronavirus
- 2analytica 2020 is postponed
- 3FDA Provides Emergency Use Authorization to PerkinElmer for COVID-19 Testing
- 4Roche’s cobas SARS-CoV-2 Test to detect novel coronavirus receives FDA Emergency Use Authorization
- 5The digital laboratory live and tangible
- 6Pool testing of SARS-CoV-02 samples increases worldwide test capacities many times over
- 7Thermo Fisher Scientific to Acquire QIAGEN
- 8Smartphone lab finds coronavirus in saliva
- 9Bosch develops rapid test for COVID-19
- 10Abbott Receives FDA Emergency Use Authorization and Launches Test to Detect Novel Coronavirus
- New blood test detects wide range of cancers
- Staining Cycles with Black Holes
- FDA Provides Emergency Use Authorization to PerkinElmer for COVID-19 Testing
- New device quickly detects harmful bacteria in blood
- Abbott Receives FDA Emergency Use Authorization and Launches Test to Detect Novel Coronavirus