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Automatic hippocampus imaging, with about 20 minutes of cloud computing per scan.


Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso-as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject’s hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper, we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.

Keywords: Brain Imaging Data Standards; computational anatomy; deep learning; hippocampal subfields; hippocampus; human; image segmentation; magnetic resonance imaging; neuroscience.

https://img.particlenews.com/image.php?url=2Ghcdg_0kAkXsy000 Tumor cells traverse many different types of fluids as they travel through the body. Christoph Burgstedt/Science Photo Library via Getty Images.

This article was originally featured on The Conversation.

Cell migration, or how cells move in the body, is essential to both normal body function and disease progression. Cell movement is what allows body parts to grow in the right place during early development, wounds to heal and tumors to become metastatic.

Even though the clinical efficacy of antibody-based therapeutics has been established, no methods that involve the de novo design of antibodies with wet lab validation are available.

About the study

A recent study, posted in the bioRxiv* preprint server, used generative AI models to develop de novo design antibodies against three distinct targets in a zero-shot fashion. A zero-shot designing method involves designing an antibody to bind to an antigen without follow-up optimization. The newly designed process has been termed de novo, meaning proteins (antibodies) were designed from first principles or from scratch.

In this interview, News Medical speaks to Assistant Professor Ryan Jackson about his latest work, published in tandem Nature papers, detailing the discovery of a new CRISPR immune system.

Please can you introduce yourself and tell us about your professional background?

I am an Assistant Professor at Utah State University (USU). I use biochemical and structural techniques to understand how the molecules that perform the reactions of life function. I’ve been working in the CRISPR field since 2011. I started as a postdoc in Blake Wiedenheft’s lab at Montana State University, and in 2016 I started my own research lab at USU. I earned both of my degrees (a B.S. in Biology and a Ph.D. in Biochemistry) from USU, so joining the faculty was like coming home. My research lab specializes in determining the structure and function of newly discovered and obscure CRISPR systems.

The FAA said normal operations were “resuming gradually” after ordering a nationwide pause on all domestic departures until 9 a.m. on Wednesday morning following a computer failure that has impacted flights around the country.

“The ground stop has been lifted,” officials said at about 8:50 a.m. ET. “We continue to look into the cause of the initial problem”

Departures were resuming at about 8:15 a.m. ET at two of the nation’s busiest hubs — Newark and Atlanta — FAA officials said on Twitter, adding, “We expect departures to resume at other airports at 9 a.m. ET.”