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Hackster.io

Nordic nRF9160 DK // Unboxing

We check out this cool kit from Nordic: a multi-sensor cellular IoT prototyping platform for hardware engineers. Easily connect Arduino shields and standalone sensors to the nRF Connect for Cloud platform (nrfcloud.com).

Where the Thingy:91 device (previously: https://www.youtube.com/watch?v=tLUKgDT2V9g) comes with built-in sensors and a consumer-ready interface, the nRF9160 DK empowers you to prototype apps with your own custom hardware.

nRF9160 DK materials:
// https://www.nordicsemi.com/Software-and-tools/Development-Kits/nRF9160-DK
// https://www.nordicsemi.com/Products/Low-power-cellular-IoT/nRF9160

Thingy:91 materials:
// https://www.nordicsemi.com/Software-and-tools/Prototyping-platforms/Nordic-Thingy-91/
// https://www.hackster.io/glowascii/getting-started-with-the-nordic-thingy-91-mac-8d44e5

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ScienceDaily

AI and single-cell genomics

Traditional single-cell sequencing methods help to reveal insights about cellular differences and functions — but they do this with static snapshots only rather than time-lapse films. This limitation makes it difficult to draw conclusions about the dynamics of cell development and gene activity. The recently introduced method “RNA velocity” aims to reconstruct the developmental trajectory of a cell on a computational basis (leveraging ratios of unspliced and spliced transcripts). This method, however, is applicable to steady-state populations only. Researchers were therefore looking for ways to extend the concept of RNA velocity to dynamic populations which are of crucial importance to understand cell development and disease response.

Single-cell velocity

Researchers from the Institute of Computational Biology at Helmholtz Zentrum München and the Department of Mathematics at TUM developed “scVelo” (single-cell velocity). The method estimates RNA velocity with an AI-based model by solving the full gene-wise transcriptional dynamics. This allows them to generalize the concept of RNA velocity to a wide variety of biological systems including dynamic populations.

“We have used scVelo to reveal cell development in the endocrine pancreas, in the hippocampus, and to study dynamic processes in lung regeneration — and this is just the beginning,” says Volker Bergen, main creator of scVelo and first author of the corresponding study in Nature Biotechnology.

With scVelo researchers can estimate reaction rates of RNA transcription, splicing and degradation without the need of any experimental data. These rates can help to better understand the cell identity and phenotypic heterogeneity. Their introduction of a latent time reconstructs the unknown developmental time to position the cells along the trajectory of the underlying biological process. That is particularly useful to better understand cellular decision making. Moreover, scVelo reveals regulatory changes and putative driver genes therein. This helps to understand not only how but also why cells are developing the way they do.

Empowering personalized treatments

AI-based tools like scVelo give rise to personalized treatments. Going from static snapshots to full dynamics allows researchers to move from descriptive towards predictive models. In the future, this might help to better understand disease progression such as tumor formation, or to unravel cell signaling in response to cancer treatment.

“scVelo has been downloaded almost 60,000 times since its release last year. It has become a stepping-stone tooltowards the kinetic foundation for single-cell transcriptomics,” adds Prof. Fabian Theis, who conceived the study and serves as Director at the Institute for Computational Biology at Helmholtz Zentrums München and Chair for Mathematical Modeling of Biological Systems at TUM.

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Hackster.io

Nordic Thingy:91 // MCU Monday

Let’s unbox the Nordic Thingy:91, a hybrid cellular IoT device combining LTE-M Narrowband IoT (via the nRF9160 SiP) with short-range BLE, ZigBee, and other protocols (using the nRF52840 SoC). Pre-programmed for asset tracking, this module has a full complement of sensors – a GPS receiver, accelerometers for impact and orientation, light sensing, air quality, and more – plus user interfaces such as a programmable button, buzzer, and LEDs.

In our next video, we’ll connect the Thingy:91 to nRF Connect for Cloud, an online dashboard where you can manage devices and sensor data. Stay tuned!

// https://www.nordicsemi.com/Software-and-tools/Prototyping-platforms/Nordic-Thingy-91
// https://www.nordicsemi.com/Products/Low-power-cellular-IoT/nRF9160
// https://nrfcloud.com
// More info: https://www.youtube.com/playlist?list=PLx_tBuQ_KSqGtprnjJeiscwhPgYM0CkIS

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Hackster.io

SoraHealth, ft. Alejandro Sánchez Gutiérrez // Hackster Café

Alejandro’s “SoraHealth” project won the Impact Prize in our Cellular IoT Challenge! He created an alert button that contains a GPS tracker, and automatically sends an email to loved ones if the owner presses the button. Check out the cool enclosure, hear the beautiful story of this invention and its inspiration, and see version 2, as Alejandro continues to use his biomedical skills for good.

// https://www.hackster.io/Alejandro_S_G/sorahealth-ad3b7d
// https://www.hackster.io/contests/soracomcontest

Categories
ScienceDaily

Tool for rapid breakdown of cellular proteins

Cellular functions depend on the functionality of proteins, and these functions are disturbed in diseases. A core aim of cell biological research is to determine the functions of individual proteins and how their disturbances result in disease.

One way to study protein functions is to examine the effects of rapidly removing them from cells. During the past years, researchers have developed several techniques to achieve this. One of these techniques is known as AID, or auxin-inducible degron. This method utilises the signalling of a class of plant hormones known as auxins to rapidly deplete individual proteins from cells.

The research group headed by Academy Professor Elina Ikonen at the University of Helsinki increased the speed and improved the hormone-dependency of the AID technique in human cells. The researchers were able to degrade the targeted cellular proteins within minutes.

In addition, the researchers expanded the potential uses of the technique to encompass several types of proteins. The method can also be employed in the acute degradation of proteins whose long-term absence cannot be tolerated by cells.

The study was published in the distinguished Nature Methods journal.

“The technique we have developed is useful primarily in research, but thanks to advances in gene technology it also has potential for novel diagnostic and therapeutic methods,” Elina Ikonen states.

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ScienceDaily

Forecasting failure in disordered materials

Disordered materials — such as cellular foams, fiber and polymer networks — are popular in applications ranging from architecture to biomedical scaffolding. Predicting when and where these materials may fail could impact not only those materials currently in use, but also future designs. Researchers from North Carolina State University and the University of California Los Angeles were able to forecast likely points of failure in two-dimensional disordered laser-cut lattices without needing to study detailed states of the material.

The interior of disordered materials is formed by a network of connections between slender beams that intersect at various points — or nodes — throughout the material. Their structure allows for both compression and deformation, enabling them to withstand different types of force.

Estelle Berthier, postdoctoral researcher at NC State and lead author of a paper describing the research, set out to determine whether it is possible to predict where failure is most likely to occur in a disordered network. Berthier and co-author Karen Daniels, professor of physics at NC State, generated lattices based on the contact networks observed within granular materials and looked at a property known as geodesic edge betweenness centrality (GEBC).

“The importance of an edge in a network is in terms of its ability to connect different parts of network using the shortest path,” Berthier says. “In our model lattice, when you connect each node of the network taking the shortest path, you use one of these beams, or edges. If you go through a particular edge a lot, then that edge has high centrality. Think about using the shortest path, or road, between two cities. The centrality value is the most popular road on that shortest path.”

In collaboration with UCLA mathematician Mason Porter, the researchers used a computer algorithm to calculate the GEBC for the lattice and found that edges with a higher centrality value than the mean were the most likely to fail.

“If you have higher traffic on a particular road, then there’s more wear and tear,” Berthier says. “Similarly, a higher centrality value means that a particular path within the material is dealing with more force ‘traffic,’ and should be monitored more closely or perhaps shored up in some way.”

The researchers found that the GEBC values alone were enough to identify failure sites in the material.

“One of the things that surprised me about the results was that the calculations don’t require us to know any of the materials’ properties, just how the parts have been connected together,” Daniels says. “Of course, we can make the predictions even stronger by including information about the physical interactions in our calculations.”

The research appears in Proceedings of the National Academy of Sciences and was supported by the James S. McDonnell Foundation.

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