Stony Brook University

About: Stony Brook University is a education organization based out in Stony Brook, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32534 authors who have published 68218 publications receiving 3035131 citations. The organization is also known as: State University of New York at Stony Brook & SUNY Stony Brook.

Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms

Abstract: True physiological imaging of subcellular dynamics requires studying cells within their parent organisms, where all the environmental cues that drive gene expression, and hence the phenotypes that we actually observe, are present. A complete understanding also requires volumetric imaging of the cell and its surroundings at high spatiotemporal resolution, without inducing undue stress on either. We combined lattice light-sheet microscopy with adaptive optics to achieve, across large multicellular volumes, noninvasive aberration-free imaging of subcellular processes, including endocytosis, organelle remodeling during mitosis, and the migration of axons, immune cells, and metastatic cancer cells in vivo. The technology reveals the phenotypic diversity within cells across different organisms and developmental stages and may offer insights into how cells harness their intrinsic variability to adapt to different physiological environments.

Image Reconstruction is a New Frontier of Machine Learning

Abstract: Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of “Machine learning for image reconstruction.” This special issue is a sister issue of the special issue published in May 2016 of this journal with the theme “Deep learning in medical imaging” [item 2) in the Appendix]. While the previous special issue targeted medical image processing/analysis, this special issue focuses on data-driven tomographic reconstruction. These two special issues are highly complementary, since image reconstruction and image analysis are two of the main pillars for medical imaging. Together we cover the whole workflow of medical imaging: from tomographic raw data/features to reconstructed images and then extracted diagnostic features/readings.

The stability of error-related brain activity with increasing trials.

Abstract: The error-related negativity (ERN) and error positivity (Pe) are increasingly being examined as neural correlates of response monitoring. The minimum number of error trials included in grand averages varies across studies; indeed, there has not been a systematic investigation on the number of trials required to obtain a stable ERN and Pe. In the current study, the ERN and Pe were quantified as two random trials were added to participants' (N=53) ERP averages. Adding trials increased the correlation with the grand average ERN and Pe; however, high correlations (rs>.80) were obtained with only 6 trials. Internal reliability of the ERN and Pe reached moderate levels after 6 and 2 trials and the signal-to-noise ratio of the ERN and Pe did not change after 8 and 4 trials, respectively. Combined, these data suggest that the ERN and Pe can be quantified using a minimum of between 6 and 8 error trials.

Statistically Significant Detection of Linguistic Change

Abstract: We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track its linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book Ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.

A common lipid links Mfn-mediated mitochondrial fusion and SNARE-regulated exocytosis

Abstract: Fusion of vesicles into target membranes during many types of regulated exocytosis requires both SNARE-complex proteins and fusogenic lipids, such as phosphatidic acid. Mitochondrial fusion is less well understood but distinct, as it is mediated instead by the protein Mitofusin (Mfn). Here, we identify an ancestral member of the phospholipase D (PLD) superfamily of lipid-modifying enzymes that is required for mitochondrial fusion. Mitochondrial PLD (MitoPLD) targets to the external face of mitochondria and promotes trans-mitochondrial membrane adherence in a Mfn-dependent manner by hydrolysing cardiolipin to generate phosphatidic acid. These findings reveal that although mitochondrial fusion and regulated exocytic fusion are mediated by distinct sets of protein machinery, the underlying processes are unexpectedly linked by the generation of a common fusogenic lipid. Moreover, our findings suggest a novel basis for the mitochondrial fragmentation observed during apoptosis.