Many people are familiar with the pocket depth measurements that occur in the dentist’s office. The dental technician pokes her periodontal probe into a patient’s gum line to measure how deep the probe will go. This is repeated tooth by tooth until the entire mouth is covered. Although inexpensive, probing depth measurements are error prone and suffer from poor reproducibility, largely due to variation in probing force. Indeed, a recent meta-analysis showed that a range of a variation of 20-fold. Other error sources include variation in the insertion point, probe angulation, the patient’s overall gingival health, and the presence of calculus. Thus, the examination is subject to large errors with inter-operator variation as high as 40%. These error sources can result in poor patient treatment and, hence, poor patient outcomes. This variation also compromises epidemiologic studies and makes it difficult to compare outcomes among dentists or among populations. Given these limitations, new tools are urgently needed to improve this procedure.

Researchers at UCI have developed a method for enhancing existing hydrogen gas sensors, leading to as much as a 20-fold improvement in sensor response and recovery times.

The inventors at the University of California, Irvine, have developed an automated, easy-to-use digital PCR system that can be used at the time of sample collection, making it highly effective in microbial pathogen analysis in resource-limited settings and extreme conditions.

An addictive manufacturing method used to create lightweight materials with tunable physical properties.

Current device pointing systems, which control the movement of cursors on screens, suffer from several drawbacks which often preclude their use by individuals with special needs or medical conditions. This UCI invention describes a simple, inexpensive “head mouse” that, in combination with proprietary software, tracks the position of the head relative to the body, allowing for full control of a pointing device.

UCLA researchers in the Department of Mechanical and Aerospace Engineering have developed a machine learning platform to virtually screen combinatorial drug therapies.

A hybrid integrated optical amplifier that offers a significant reduction in cost, size, weight and power.

Elves is a computer expert system for X-ray crystallography of biological macromolecules. Elves automates and accelerates every step of X-ray data analysis, from processing X-ray diffraction images to guiding and refining a molecular model. Elves requires the use of CCP4, which must be obtained under separate license from a third party. Elves also uses common data analysis programs such as Mosflm. Elves also has novel functionalities, such as Spotter to identify and display particular diffraction spots, sendhome to transmit data frames over the internet, and table1.com to tabulate statistics for publication. Elves sequentially runs each step of X-ray structure determination using a generalized regimen. The problem-solving strategy is based on empirical rules and procedures for overcoming common problems. Optimized parameters are passed from each program to the next. These values can be evaluated by the user or simply accepted automatically at each stage. The overall effect is to systematize, optimize and accelerate the process of X-ray structure analysis in biomedical research. Elves can accept English language inputs or shell script inputs. After locating the data and the necessary programs on the file system, Elves writes program-input scripts, runs programs, examines the output and iteratively repeats this cycle to optimize input parameters. This optimization is generally beyond the capability of human users of the underlying programs. This systematic X-ray data analysis reduces the frequency of mistakes. In addition, Elves operates faster and more reliably than a human user, even when the user employs a graphical interface. Complicated analytical calculations that can take months for human users to complete can be performed in a matter of hours using Elves. To date, Elves has been used to help determine the crystal structure of over fourteen proteins. Novel structures have been solved in as little as five hours from the start of X-ray data collection at a synchrotron source; a typical investigator not using Elves would take weeks to months to complete the same work.