System Bits: Oct. 9

Sensing with light pulses
In a development expected to be useful in applications including distance measurement, molecular fingerprinting and ultrafast sampling, EPFL researchers have found a way to implement an optical sensing system by using spatial multiplexing, a technique originally developed in optical-fiber communication, which produces three independent streams of ultrashort optical pulses using a single continuous-wave laser and a single optical microresonator.

The team reported this method is far simpler than existing technologies, and given that ultrashort optical pulse applications rely not only on a single stream of pulses – also known as “optical frequency combs” – but require two or even three of them, these multi-comb approaches significantly speed up acquisition time over conventional techniques.

Artist’s rendering of multiplexed optical pulses in a crystalline resonator.
Source: EPFL

Trains of short optical pulses are typically produced by large pulsed laser sources, and multi-comb applications therefore require several such lasers, often at prohibitive costs and complexity. Furthermore, the relative timing of pulse trains and their phases must be very well synchronized, which requires active electronics that synchronize the lasers, they said.

In a new paper, the research team of Tobias J. Kippenberg at EPFL, together with the group of Michael Gorodetsky at the Russian Quantum Centre, has developed a much simpler method to generate multiple frequency combs. The technology uses small devices called “optical microresonators” to create optical frequency combs instead of conventional pulsed lasers.

Using this multiplexing scheme, the team demonstrated several applications, such as dual-comb spectroscopy, or rapid optical sampling. The acquisition time could be adjusted between a fraction of a millisecond to 100 nanoseconds.

The authors are now working on developing a new demonstration with the triple-comb source.

The researchers said the technology can be integrated with both photonic elements and silicon microchips, and that establishing multi-comb generation on a chip may catalyze a wide variety of applications such as integrated spectrometers or LIDAR, and could make optical sensing far more accessible.

ML algorithm + individual data = personalized recommendations to reduce blood pressure
Using wearable off-the-shelf technology and machine learning, UC San Diego researchers have developed a method to predict an individual’s blood pressure and provide personalized recommendations to lower it based on this data.

The researchers believe this is the first work investigating daily blood pressure prediction and its relationship to health behavior data collected by wearables.

Source: UCSD

According to Sujit Dey, co-author of the paper and Director of the Center for Wireless Communications at UC San Diego’s Jacobs School of Engineering where he’s a professor in the Department of Electrical and Computer Engineering, when doctors tell their patients to make a lot of significant lifestyle changes—exercise more, sleep better, lower their salt intake etc.—it can be overwhelming, and compliance is not very high.

“What if we could pinpoint the one health behavior that most impacts an individual’s blood pressure, and have them focus on that one goal, instead,” Dey asked.

Dey and co-author Po-Han Chiang, a graduate student in the Mobile Systems Design Lab in the Department of Electrical and Computer Engineering at UC San Diego’s Jacobs School of Engineering, collected sleep, exercise and blood pressure data from eight patients over 90 days using a FitBit Charge HR and Omron Evolv wireless blood pressure monitor. Using machine learning and this data from existing wearable devices, they developed an algorithm to predict the users’ blood pressure and show which particular health behaviors affected it most.

This study affirmed the importance of personalized data over generalized information. While many health databases add large amounts of patient data into one model, considering all patients together to make health suggestions, the personalized information in this study was more effective, the team asserted.

Smartphone app identifies bacteria in one hour
UC Santa Barbara researchers have developed a cell phone app and lab kit to allow a smartphone to identify bacteria from patients anywhere in the world. With the new app, the team said doctors will be able to diagnose diseases and prescribe the appropriate antibiotic within a one-hour office visit, meaning faster recovery — and lower treatment costs — for patients.

The researchers report that the detection system succeeded in achieving rapid diagnosis of urinary tract infections — among the most common type of infection globally. The app uses a smartphone’s camera to measure a chemical reaction and determines a diagnosis in about an hour — and the simple, low-cost test can be performed in the world’s most remote locations. 

image caption
A custom-built app transforms a smartphone into a stand-alone device for rapid diagnosis and treatment of urinary tract infections.
Source: UCSB

The project was led by professors Michael Mahan of UC Santa Barbara and Tom Soh of Stanford University, along with Santa Barbara Cottage Hospital physicians Jeffrey Fried, M.D. and Lynn Fitzgibbons, M.D. Additional collaborators include UCSB scientists Lucien Barnes, Douglas Heithoff, Scott Mahan, Gary Fox and Jamey Marth — who is also also a professor at Sanford Burnham Prebys Medical Discovery Institute (SBP) — as well as Cottage Hospital scientists Andrea Zambrano, M.D., and Jane Choe.

The free, custom-built app was developed for the Android operating system and can be downloaded and installed from the Google Play Store. Upon opening the app, the user is presented with an option for a step-by-step tutorial prior to running test samples.