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An international team has succeeded in propagating a commercial hybrid rice strain as a clone through seeds with 95 percent efficiency. This could lower the cost of hybrid rice seed, making high-yielding, disease resistant rice strains available to low-income farmers worldwide. The work was published Dec. 27 in Nature Communications.

First-generation hybrids of crop plants often show higher performance than their parent strains, a phenomenon called hybrid vigor. But this does not persist if the hybrids are bred together for a second generation. So when farmers want to use high-performing hybrid plant varieties, they need to purchase new seed each season.

Rice, the staple crop for half the world’s population, is relatively costly to breed as a hybrid for a yield improvement of about 10 percent. This means that the benefits of rice hybrids have yet to reach many of the world’s farmers, said Gurdev Khush, adjunct professor emeritus in the Department of Plant Sciences at the University of California, Davis. Working at the International Rice Research Institute from 1967 until retiring to UC Davis in 2002, Khush led efforts to create new rice high-yield rice varieties, work for which he received the World Food Prize in 1996.

Fake scientific abstracts and research papers generated using OpenAI’s highly-advanced chatbox ChatGPT fooled scientists into thinking they were real reports nearly one-third of the time, according to a new study, as the eerily human-like program raises eyebrows over the future of artificial intelligence.

Researchers at Northwestern University and the University of Chicago instructed ChatGPT to generate fake research abstracts based on 10 real ones published in medical journals, and fed the fakes through two detection programs that attempted to distinguish them from real reports.

ChatGPT created completely original scientific abstracts based on fake numbers, and stumped reviewers nearly one-third of the time.

Continue reading “Fake Scientific Abstracts Written By ChatGPT Fooled Scientists, Study Finds” »

The agency is placing more holds on trials to protect patients and ensure studies are designed properly, a Wall Street Journal review found.

Portable, low-field strength MRI systems have the potential to transform neuroimaging – provided that their low spatial resolution and low signal-to-noise (SNR) ratio can be overcome. Researchers at Harvard Medical School are harnessing artificial intelligence (AI) to achieve this goal. They have developed a machine learning super-resolution algorithm that generates synthetic images with high spatial resolution from lower resolution brain MRI scans.

The convolutional neural network (CNN) algorithm, known as LF-SynthSR, converts low-field strength (0.064 T) T1-and T2-weighted brain MRI sequences into isotropic images with 1 mm spatial resolution and the appearance of a T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) acquisition. Describing their proof-of-concept study in Radiology, the researchers report that the synthetic images exhibited high correlation with images acquired by 1.5 T and 3.0 T MRI scanners.

Morphometry, the quantitative size and shape analysis of structures in an image, is central to many neuroimaging studies. Unfortunately, most MRI analysis tools are designed for near-isotropic, high-resolution acquisitions and typically require T1-weighted images such as MP-RAGE. Their performance often drops rapidly as voxel size and anisotropy increase. As the vast majority of existing clinical MRI scans are highly anisotropic, they cannot be reliably analysed with existing tools.

Just published from my son.

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.

Continue reading “Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold” »

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.

Continue reading “A look at how cancer cells move and metastatize could help doctors stop them from spreading” »

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.

A breakthrough in quantum research – the first detection of excitons (electrically neutral quasiparticles) in a topological insulator has been achieved by an international team of scientists collaborating within the Würzburg-Dresden Cluster of Excellence ct.qmat. This discovery paves the way for a new generation of light-driven computer chips and quantum technologies. It was enabled thanks to smart material design in Würzburg, the birthplace of topological insulators. The findings have been published in the journal Nature Communications.

<em>Nature Communications</em> is a peer-reviewed, open-access, multidisciplinary, scientific journal published by Nature Portfolio. It covers the natural sciences, including physics, biology, chemistry, medicine, and earth sciences. It began publishing in 2010 and has editorial offices in London, Berlin, New York City, and Shanghai.