Scientists have developed an automated tool for mapping the movement of particles inside cells that may accelerate research in many fields, a new study in eLife reports.

The movements of tiny molecules, proteins and cellular components throughout the body play an important role in health and disease. For example, they contribute to brain development and the progression of some diseases. The new tool, built with cutting-edge machine learning technology, will make tracking these movements faster, easier and less prone to bias.

Currently, scientists may use images called kymographs, which represent the movement of particles in time and space, for their analyses of particle movements. These kymographs are extracted from time-lapse videos of particle movements recorded using microscopes. The analysis needs to be done manually, which is both slow and vulnerable to unconscious biases of the researcher.

“We used the power of machine learning to solve this long-standing problem by automating the tracing of kymographs,” says lead author Maximilian Jakobs, a Ph.D. student in the Department of Physiology, Development and Neuroscience at the University of Cambridge, UK.

The team developed the software, dubbed ‘KymoButler’, to automate the process. The software uses deep learning technology, which tries to mimic the networks in the brain to allow software to learn and become more proficient at a task over time and multiple attempts. They then tested KymoButler using both artificial and real data from scientists studying the movement of an array of different particles.

“We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of biological systems,” Jakobs explains. The software could also complete analyses in under one minute that would take an expert 1.5 hours.

KymoButler is available for other researchers to download and use at kymobutler.deepmirror.ai. Senior author Kristian Franze, Reader in Neuronal Mechanics at the University of Cambridge, expects the software will continue to improve as it analyses more types of data. Researchers using the tool will be given the option of anonymously uploading their kymographs to help the team continue developing the software.

“We hope our tool will prove useful for others involved in analysing small particle movements, whichever field they may work in,” says Franze, whose lab is devoted to understanding how physical interactions between cells and their environment shape the development and regeneration of the brain.

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A dataops team will help you get the most out of your data. Here’s how people, processes, technology, and culture bring it all together Have you noticed that most organizations are trying to do a lot more with their data?

Businesses are investing heavily in data science programs, self-service business intelligence tools, artificial intelligence programs, and organizational efforts to promote data-driven decision making. Some are developing customer facing applications by embedding data visualizations into web and mobile products or collecting new forms of data from sensors (Internet of Things), wearables, and third-party APIs. Still others are harnessing intelligence from unstructured data sources such as documents, images, videos, and spoken language.

Much of the work around data and analytics is on delivering value from it. This includes dashboards, reports, and other data visualizations used in decision making; models that data scientists create to predict outcomes; or applications that incorporate data, analytics, and models.

What has sometimes been undervalued is all the underlying data operations work, or dataops, that it takes before the data is ready for people to analyze and format into applications to present to end users.

Dataops includes all the work to source, process, cleanse, store, and manage data. We’ve used complicated jargon to represent different capabilities such as data integration, data wrangling, ETL (extract, transform and load), data prep, data quality, master data management, data masking, and test data management.

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The challenge to analyze earthquake signals with optimum precision grows along with the amount of available seismic data. At the Karlsruhe Institute of Technology (KIT), researchers have deployed a neural network to determine the arrival-time of seismic waves and thus precisely locate the epicenter of the earthquake. In their report in the Seismological Research Lettersjournal, they point out that Artificial Intelligence is able to evaluate the data with the same precision as an experienced seismologist.

For precisely locating an earthquake event, it is critical to determine the exact arrival-time of the majority of seismic waves at the seismometer station (the so-called phase arrival). Without this knowledge, further accurate seismological evaluations are not possible. Such evaluations can be very useful in predicting aftershocks that sometimes cause more serious damage than the initial main earthquake. By precisely locating the epicenter, even physical processes occurring deep inside the Earth can better be distinguished, and this, in turn, allows inference about the structure of the Earth’s interior. “Our results show that Artificial Intelligence can significantly improve earthquake analysis—not only with the support of large data volumes, but also if only a limited dataset is available,” explains Professor Andreas Rietbrock from the Geophysical Institute (GPI) at KIT.

The evaluation of the recorded seismograms, which is called phase picking, helps determine the arrival-times of the individual phases. Traditionally, this is a manual procedure. The precision in manual phase picking may be affected by the subjectivity of the seismologist in charge. Most notably, however, a manual evaluation meanwhile requires unacceptable time and staff resources, due to the growing amount of seismic data and the higher density of the seismometer networks. Automated evaluation has become necessary in order to leverage all available data quickly. Indeed, the phase picking algorithms developed so far are not able to deliver the precision achieved with manual picking by an experienced seismologist—due to the extreme complexity of the formation and propagation of earthquakes, with many physical processes acting on the seismic wave field.

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