lin-36, a Class B Synthetic Multivulva Gene, Encodes a Novel Protein Jeffrey H. Thomas and H. Robert Horvitz, HHMI, Dept. Biology, MIT, Cambridge, MA 02139, USA
It is an honor and a great pleasure to introduce Dr. Robert Horvitz to you as the 1998 recipient of the Alfred Sloan Prize of the General Motors Cancer Research Foundation. Let me begin by telling you a little bit about Bob's
The original version of this article [1] unfortunately contained a mistake. The author list contained a spelling error for the author Hannah V. McCue. The original article has been corrected for this error. The corrected author list is given below:Xi Chen, Hannah V. McCue, Shi Quan Wong, Sudhanva S. Kashyap, Brian C. Kraemer, Jeff W. Barclay, Robert D. Burgoyne and Alan Morgan
While the analysis of mitochondrial morphology has emerged as an important tool in the study of mitochondrial function, efficient quantification of mitochondrial microscopy images presents a difficult task and bottleneck for statistically robust conclusions. Here, we present the Mitochondrial Segmentation Network (MitoSegNet), a pretrained deep learning segmentation model that enables researchers to easily exploit the power of deep learning for the quantification of mitochondrial morphology (Fischer, Besora-Casals et al. 2020). The MitoSegNet was generated by training a modified fully convolutional neural network with fluorescent microscopy, maximum-intensity projection images, depicting mitochondria in body wall muscle cells of adult C. elegans worms. We tested the performance of MitoSegNet against three feature-based segmentation algorithms and the machine-learning segmentation tool Ilastik. MitoSegNet outperformed all other methods in both pixelwise and morphological segmentation accuracy. We successfully applied MitoSegNet to unseen fluorescence microscopy images of mitoGFP expressing mitochondria in wild-type and catp-6ATP13A2 mutant C. elegans adults. Additionally, MitoSegNet was capable of accurately segmenting mitochondria in HeLa cells treated with fragmentation inducing reagents. We provide MitoSegNet for all operating systems as an easy-to-use graphical user interface tool that combines segmentation with morphological analysis. Reference Fischer, C. A., L. Besora-Casals, S. G. Rolland, S. Haeussler, K. Singh, M. Duchen, B. Conradt and C. Marr (2020). "MitoSegNet: Easy-to-use Deep Learning Segmentation for Analyzing Mitochondrial Morphology." iScience 23(10).
For the first time, scientists have the nearly complete genetic instructions for an animal that, like humans, has a nervous system, digests food, and reproduces sexually. The 97-million-base genome of the tiny roundworm Caenorhabditis elegans was deciphered by an international team led by Robert Waterston and John Sulston. The work was reported in a special issue of the journal Science (December 11, 1998) that featured six articles describing the history and significance of the accomplishment and some early sequence-analysis results.
Historically, peptidergic substances (in the form of neurosecretions) were linked to moulting in nematodes. More recently, there has been a renewal of interest in nematode neurobiology, initially triggered by studies demonstrating the localization of peptide immunoreactivities to the nervous system. Here, David Brownlee, Ian Fairweather, Lindy Holden-Dye and Robert Walker will review progress on the isolation of nematode neuropeptides and efforts to unravel their physiological actions and inactivation mechanisms. Future avenues for research are suggested and the potential exploitation of peptidergic pathways in future therapeutic strategies
Within the past few years researchers have finally begun to be able to peer inside a hitherto impenetrable black box, namely, the development of complex organisms. The genes that control the commitment of embryonic cells to specific fates are now being found and characterized. A case in point is reported in this issue of Science (p. 409). Victor Ambros of Harvard University and H. Robert Horvitz of Massachusetts Institute of Technology have identified genes that affect the timing of developmental events in the roundworm Caenorhabditis elegans.
The overwhelming complexity of higher organisms can make it hard to know where to begin to understand them. The three scientists who share this year's Nobel prize for physiology or medicine, Sydney Brenner (Salk Institute, La Jolla, CA, USA), John Sulston (Wellcome Trust Sanger Institute, Hinxton, UK), and Robert Horvitz (Massachusetts Institute of Technology, Boston, MA, USA), all chose to study a far simpler organisms - the nematode worm Caenorhabditis elegans. Although multicellular, this organism reproduces rapidly and is transparent, so that each developmental stage can be seen clearly without the need for dissection.
In the next five years, molecular biology will get its first look at the complete genetic code of a multicellular animal. The Caenorhabditis elegans genome sequencing project, a collaboration between Robert Waterston's group in St. Louis and John Sulston's group in Cambridge, is currently on schedule towards its goal of obtaining the complete sequence of this organism and all its estimated 15,000 to 20,000 genes by 1998. By that time, we should also know the complete genome sequence of a few other organisms as well, including the prokaryote Escherichia coli and the single-celled eukaryote Saccharomyces