BILCO: An Efficient Algorithm for Joint Alignment of Time Series

BILCO (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!

The 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.

The 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