John Innes Centre researchers have shown that calcium waves are not a primary response, but rather they are a secondary response to a wave of amino acids released from the wound.

These findings challenge established thinking on long distance plant signalling molecules and the mechanisms by which information travels from the point of stress through living and non-living plant tissues.

It has been observed for many years that wounding, and other trauma, initiates calcium waves that travel both short distances from cell to cell, and longer distances from leaf to leaf.

These calcium waves are reminiscent of signalling seen in the nerves in mammals, but as plants do not have nerve cells the mechanism by which this happens has been in question.

The new findings which appear in Scientific Advances suggest that when a cell is wounded, it releases a wave of glutamate, an amino acid. As this wave travels through plant tissues, it activates calcium channels in the membranes of the cells it passes. This activation appears like a calcium wave but is a passive response, or “readout” of the moving glutamate signal.

Previous hypotheses to explain how calcium waves pass through plant cells involved active mechanisms to propagate the calcium signal. These hypotheses relied on the signal propagating along the cell membrane, or via a pressure wave in the xylem, but there was no explanation for how the response passed from one cell to the next.

Dr Christine Faulkner, Group Leader at the John Innes Centre said, “Every time active propagation models were presented, I would question how this wave travelled from cell to cell. It appeared to me that there was a hole in the theory, and this research uncovers a new mechanism that shows that the calcium wave isn’t what it seems.”

Dr Faulkner’s group specializes in the study of plasmodesmata, the channels or bridges that connect cells and the team speculated that a wound signal would travel from cell to cell through plasmodesmata. However, using quantitative imaging techniques, data modelling and genetics they found that the mobile signal is a glutamate wave that travels outside of cells, along the cell walls.

“The glutamate and calcium waves are connected — glutamate triggers the calcium response. You could imagine it with an analogy of a corridor. The glutamate rushes down the corridor and as it passes a door it kicks it open. The calcium response is the door opening. Up to now the assumption has been that what moved down the corridor was hydraulic pressure or a series of propagating chemical reactions, but our study shows that this is not the case,” said Dr Faulkner.

First author of the study Dr Analisa Bellandi said, “We’ve shown that calcium waves are synchronous with glutamate waves, and their dynamics match transmission by diffusion and flow. This research makes us rethink what we know. I am hoping that our research will inspire debate and allow people to take a fresh perspective on data which has been in the field for a long time.”

Diffusion and bulk flow of amino acids mediate calcium waves in plants appears in Science Advances.

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Materials provided by John Innes Centre. Note: Content may be edited for style and length.

Women with a first-degree family relative diagnosed with breast cancer, who are otherwise at average risk, are often advised to get screened 10 years earlier than the relative’s diagnosis age. However, there is little evidence to support the long-standing recommendation.

UC Davis Comprehensive Cancer Center researcher Diana Miglioretti joined Danielle Durham, with the Department of Radiology at University of North Carolina at Chapel Hill, and five other researchers on the study. They analyzed data from the Breast Cancer Surveillance Consortium on screening mammograms conducted from 1996-2016 to evaluate when screenings should begin for women with a family history of breast cancer.

More than 300,000 women were included in the national study. Researchers compared cumulative 5-year breast cancer incidence among women with and without a first-degree family history of breast cancer by relative’s age at diagnosis and screening age.

“The study concluded that a woman with a relative diagnosed at or before age 45 may wish to consider, in consultation with her doctor, initiating screening 5-8 years earlier than their relative’s diagnosis age, rather than a decade earlier. That puts them at a risk that is equal to that of an average-risk woman who is age 50, which is the most recommended age for starting mammograms,” said Durham.

BRCA gene mutation carriers may benefit from starting screenings earlier. Women ages 30-39 with more than one first-degree relative diagnosed with breast cancer may wish to consider genetic counseling.

Increasing the age for initiating screening could reduce the potential harms of starting breast cancer screenings too early. These include increased radiation exposure and false positive results that require women to return to the clinic for diagnostic imaging and possibly invasive procedures, but do not result in a breast cancer diagnosis. The earlier a woman starts receiving mammograms, the more screenings they will undergo over their lifetime — and that increases the chances of experiencing these harms.

“Mammography also may not perform as well in younger women because they are more likely to have dense breasts which increase the difficulty of finding cancer on the images and results in more false-positives,” Miglioretti said.

The other authors on this study include Linn A. Abraham, Kaiser Permanente Washington Health Research Institute; Megan C. Roberts, UNC Eshelman School of Pharmacy; Carly P. Khan, Patient-Centered Outcomes Research Institute; Robert A. Smith, American Cancer Society and Karla Kerlikowske, UCSF Health. Miglioretti is an affiliate investigator with UC Davis Center for Healthcare Policy and Research and Kaiser Permanente Washington Health Research.

The study was supported through funding by the Cancer Prevention Fellowship Program, Division of Cancer Prevention and the National Cancer Institute (NCI) at the National Institutes of Health. Data collection by the Breast Cancer Surveillance Consortium was funded by the NCI (grant numbers P01CA154292, U54CA163303 and PCS-1504-30370).

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Materials provided by University of California – Davis Health. Original written by Stephanie Winn. Note: Content may be edited for style and length.

MIT researchers have created a new method for computing directly on these devices, which drastically reduces this latency. Their technique shifts the memory-intensive steps of running a machine-learning model to a central server where components of the model are encoded onto light waves.

The waves are transmitted to a connected device using fiber optics, which enables tons of data to be sent lightning-fast through a network. The receiver then employs a simple optical device that rapidly performs computations using the parts of a model carried by those light waves.

This technique leads to more than a hundredfold improvement in energy efficiency when compared to other methods. It could also improve security, since a user’s data do not need to be transferred to a central location for computation.

This method could enable a self-driving car to make decisions in real-time while using just a tiny percentage of the energy currently required by power-hungry computers. It could also allow a user to have a latency-free conversation with their smart home device, be used for live video processing over cellular networks, or even enable high-speed image classification on a spacecraft millions of miles from Earth.

“Every time you want to run a neural network, you have to run the program, and how fast you can run the program depends on how fast you can pipe the program in from memory. Our pipe is massive — it corresponds to sending a full feature-length movie over the internet every millisecond or so. That is how fast data comes into our system. And it can compute as fast as that,” says senior author Dirk Englund, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and member of the MIT Research Laboratory of Electronics.

Joining Englund on the paper is lead author and EECS grad student Alexander Sludds; EECS grad student Saumil Bandyopadhyay, Research Scientist Ryan Hamerly, as well as others from MIT, the MIT Lincoln Laboratory, and Nokia Corporation. The research will be published in Science.

Lightening the load

Neural networks are machine-learning models that use layers of connected nodes, or neurons, to recognize patterns in datasets and perform tasks, like classifying images or recognizing speech. But these models can contain billions of weight parameters, which are numeric values that transform input data as they are processed. These weights must be stored in memory. At the same time, the data transformation process involves billions of algebraic computations, which require a great deal of power to perform.

The process of fetching data (the weights of the neural network, in this case) from memory and moving them to the parts of a computer that do the actual computation is one of the biggest limiting factors to speed and energy efficiency, says Sludds.

“So our thought was, why don’t we take all that heavy lifting — the process of fetching billions of weights from memory — move it away from the edge device and put it someplace where we have abundant access to power and memory, which gives us the ability to fetch those weights quickly?” he says.

The neural network architecture they developed, Netcast, involves storing weights in a central server that is connected to a novel piece of hardware called a smart transceiver. This smart transceiver, a thumb-sized chip that can receive and transmit data, uses technology known as silicon photonics to fetch trillions of weights from memory each second.

It receives weights as electrical signals and imprints them onto light waves. Since the weight data are encoded as bits (1s and 0s) the transceiver converts them by switching lasers; a laser is turned on for a 1 and off for a 0. It combines these light waves and then periodically transfers them through a fiber optic network so a client device doesn’t need to query the server to receive them.

“Optics is great because there are many ways to carry data within optics. For instance, you can put data on different colors of light, and that enables a much higher data throughput and greater bandwidth than with electronics,” explains Bandyopadhyay.

Trillions per second

Once the light waves arrive at the client device, a simple optical component known as a broadband “Mach-Zehnder” modulator uses them to perform super-fast, analog computation. This involves encoding input data from the device, such as sensor information, onto the weights. Then it sends each individual wavelength to a receiver that detects the light and measures the result of the computation.

The researchers devised a way to use this modulator to do trillions of multiplications per second, which vastly increases the speed of computation on the device while using only a tiny amount of power.

“In order to make something faster, you need to make it more energy efficient. But there is a trade-off. We’ve built a system that can operate with about a milliwatt of power but still do trillions of multiplications per second. In terms of both speed and energy efficiency, that is a gain of orders of magnitude,” Sludds says.

They tested this architecture by sending weights over an 86-kilometer fiber that connects their lab to MIT Lincoln Laboratory. Netcast enabled machine-learning with high accuracy — 98.7 percent for image classification and 98.8 percent for digit recognition — at rapid speeds.

“We had to do some calibration, but I was surprised by how little work we had to do to achieve such high accuracy out of the box. We were able to get commercially relevant accuracy,” adds Hamerly.

Moving forward, the researchers want to iterate on the smart transceiver chip to achieve even better performance. They also want to miniaturize the receiver, which is currently the size of a shoe box, down to the size of a single chip so it could fit onto a smart device like a cell phone.

The research is funded, in part, by NTT Research, the National Science Foundation, the Air Force Office of Scientific Research, the Air Force Research Laboratory, and the Army Research Office.