News

AQuA2 has been released! A fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals

AQuA2AQuA2 (Activity Quantification and Analysis 2) is a fast, accurate, and versatile data analysis platform built upon advanced machine learning techniques. AQuA2 allows for the quantification of spatiotemporal signals across biosensors, cell types, organs, animal models, and imaging modalities. Developed by Axel Nimmerjahn and Guoqiang Yu’s groups, AQuA2 is available for MATLAB and as a Fiji plugin.

Find out more about AQuA2: https://www.biorxiv.org/content/10.1101/2024.05.02.592259v1

Download and installation instructions: https://github.com/yu-lab-vt/AQuA2?tab=readme-ov-file

The Salk Institute promotes neuroscientist Axel Nimmerjahn

Axel NimmerjahnIn recognition of his notable and innovative contributions to science, the Salk Institute’s Axel Nimmerjahn, PhD, has been promoted from associate professor to full professor. Axel Nimmerjahn leads the U19 A-Team Program and is the director of Salk’s Waitt Advanced Biophotonics Center. With his lab, he has developed and applied new tools to visualize cells’ structural and functional dynamics in the brain and spinal cord under healthy and diseased conditions, including tiny wearable microscopes allowing scientists to observe cellular activities and molecular signaling in real-time in behaving animals. The Nimmerjahn lab’s research focuses on brain and spinal cord function, neuro-immune interactions, and diseases such as Alzheimer’s disease, neuroinflammation, spinal cord injury, brain cancer, stroke, and pain.

Read the full story: https://www.salk.edu/news-release/the-salk-institute-promotes-neuroscientist-axel-nimmerjahn/.

Cagla Eroglu elected to American Academy of Arts & Science

Eroglu_CaglaCagla Eroglu, PhD, who is leading the U19 A-Team Program’s effort to uncover the role of astrocytes in goal-oriented behaviors, is one of the investigators elected to the American Academy of Arts & Sciences for 2024. The academy’s current members represent innovative thinkers in every field and profession. The 250 members elected in 2024 are being recognized for their excellence and invited to uphold the Academy’s mission of engaging across disciplines and divides.

Read the full story: https://medschool.duke.edu/news/eroglu-elected-american-academy-arts-sciences.

Daniel Quintero awarded with prestigious NSF Fellowship

Daniel QuinteroDaniel Quintero, a graduate student in Cagla Eroglu’s laboratory at Duke University and a member of the A-Team group, has received a five-year fellowship from the NSF Graduate Research Fellowship Program (GRFP). Daniel is actively investigating the cellular and molecular mechanisms by which astrocytes, the star-shaped glial cells of the brain, influence sleep. The U19 A-Team Leadership extends its congratulations to Daniel!

More information: https://medschool.duke.edu/news/duke-graduate-students-awarded-prestigious-nsf-fellowships.

BILCO: An Efficient Algorithm for Joint Alignment of Time Series

BILCOBILCO (BIdirectional pushing with Linear Component Operations) is an efficient algorithm, developed by Guoqiang Yu’s group at Virginia Tech, for solving joint alignment problems of time series and min-cut for GTW (graphical time warping) graphs. BILCO has the same theoretical time complexity as the best popular methods, such as HIPR (highest-label push-relabel), but it provides a significant empirical efficiency boost without sacrificing the accuracy of joint alignment. On thousands of datasets under various simulated scenarios and real application cases (calculate signal propagation calculation, exact depth information extraction, and signature identification), BILCO shows around 10 to 50 times speed improvement and only cost 1/10 memory compared with the best peer methods IBFS (incremental breadth-first search), HPF (Hochbaum’s pseudo flow), BK (Boykov-kolmogorov), and HIPR.

More information about BLICO: https://github.com/yu-lab-vt/BILCO.

Congrats to the U19 Astrocyte-Team!

TrophyThe U19 Astrocyte-Team (A-Team) members have made significant advancements in their field, resulting in a total of 14 awards and fellowships in the first two years of the U19 A-Team Program. The U19 A-Team Leadership extends their congratulations for their remarkable accomplishments. They also acknowledge and appreciate the invaluable contribution of the A-Team Scholars in expanding our understanding of the crucial roles played by astrocytes in neural circuit operation, complex behaviors, and brain computation.

Recipients from the four institutions part of the U-19 A-Team are listed here.

Synbot: An open-source image analysis software for automated quantification of synapses

SynapseThe precise formation of neuronal connections, known as synapses, is essential for proper brain function. As a result, understanding the mechanisms of synaptogenesis has been a major focus in cellular and molecular neuroscience. Immunohistochemistry, coupled with light microscopy, is commonly used to label and visualize synapses. However, quantifying the number of synaptic contacts from light microscopy images has traditionally been a challenging and time-consuming task, with variable results between experimenters.

To overcome these limitations, Cagla Eroglu’s group at Duke University has developed SynBot, a new open- source Image-based software. SynBot addresses the technical bottlenecks of traditional synapse quantification analysis by automating several stages of the process and incorporates the machine learning algorithm ilastik, which enables accurate thresholding for synaptic puncta identification. Additionally, the software’s code is easily modifiable, allowing users to adapt it to their specific needs.

Find out more about Synbot: https://pubmed.ncbi.nlm.nih.gov/37425715/
Download and installation instructions: https://github.com/Eroglu-Lab/Syn_Bot

Check out AQuA (Astrocyte Quantification and Analysis), a tool to detect signaling events

AQUA logo
AQuA (Astrocyte Quantification and Analysis) is an effective tool to detect signaling events from microscopic time-lapse imaging data of astrocytes or other cell types. The algorithm, developed by Guoqiang Yu’s group at Virginia Tech, is data-driven and based on machine learning principles and can be applied across model organisms, fluorescent indicators, experimental modalities, cell types, and imaging parameters.

More information about AQuA: https://github.com/yu-lab-vt/AQuA

A prototype for a web-based service version of AQuA is also under development. Please stay tuned!