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151. Derrida's Margins datasets

Author(s):
Chenoweth, Katie; Koeser, Rebecca Sutton; Altergott, Renée; Baron-Raiffe, Alexander; Bauer, Jean; Budak, Nick; Córdova, Chad; Hancock, Austin; Hicks, Benjamin; McElwee, Kevin; Vettier, Chloé
Abstract:
Derrida’s Margins <derridas-margins.princeton.edu> is a website and online research tool for annotations from the Library of Jacques Derrida, housed at Princeton University Library (PUL) <library.princeton.edu>. Jacques Derrida is one of the major figures of twentieth-century thought, and his library--which bears the traces of decades of close reading--represents a major intellectual archive. This project focused on annotations related to Derrida’s landmark 1967 work De la grammatologie (Of Grammatology).
Type:
Dataset
Issue Date:
29 October 2021

152. Derrida's Margins datasets

Author(s):
Chenoweth, Katie; Baron-Raiffe, Alexander; Sutton Koeser, Rebecca
Abstract:
Derrida’s Margins <derridas-margins.princeton.edu> is a website and online research tool for annotations from the Library of Jacques Derrida, housed at Princeton University Library (PUL) <library.princeton.edu>. Jacques Derrida is one of the major figures of twentieth-century thought, and his library--which bears the traces of decades of close reading--represents a major intellectual archive. This project focused on annotations related to Derrida’s landmark 1967 work De la grammatologie (Of Grammatology).
Type:
Dataset
Issue Date:
15 October 2021

163. Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in Helicobacter pylori

Author(s):
Taylor, Jenny A.; Bratton, Benjamin P.; Sichel, Sophie R.; Blair, Kris M.; Jacobs, Holly M.; DeMeester, Kristen E.; Kuru, Erkin; Gray, Joe; Biboy, Jacob; VanNieuwenhze, Michael S.; Vollmer, Waldemar; Grimes, Catherine L.; Shaevitz, Joshua W.; Salama, Nina R.
Abstract:
Helical cell shape is necessary for efficient stomach colonization by Helicobacter pylori, but the molecular mechanisms for generating helical shape remain unclear. We show that the helical centerline pitch and radius of wild-type H. pylori cells dictate surface curvatures of considerably higher positive and negative Gaussian curvatures than those present in straight- or curved-rod bacteria. Quantitative 3D microscopy analysis of short pulses with either N-acetylmuramic acid or D-alanine metabolic probes showed that cell wall growth is enhanced at both sidewall curvature extremes. Immunofluorescence revealed MreB is most abundant at negative Gaussian curvature, while the bactofilin CcmA is most abundant at positive Gaussian curvature. Strains expressing CcmA variants with altered polymerization properties lose helical shape and associated positive Gaussian curvatures. We thus propose a model where CcmA and MreB promote PG synthesis at positive and negative Gaussian curvatures, respectively, and that this patterning is one mechanism necessary for maintaining helical shape.
Type:
Dataset and Image
Issue Date:
April 2019

165. Drosophila melanogaster Long Timescale Data 2022

Author(s):
McKenze-Smith, Grace C.; Wolf, Scott W.
Abstract:
This dataset contains lifetime recordings and associated postural data and ethograms for 47 male Drosophila melanogaster.
Type:
Dataset and MovingImage
Issue Date:
September 2023

166. Dust and Starlight Maps for Galaxies in the KINGFISH Sample

Author(s):
G., Aniano; B.T., Draine; L.K, Hunt; K., Sandstrom; D., Calzetti; R.C, Kennicutt; D.A, Dale; M., Galametz; K.D, Gordon; A.K, Leroy; J. -D.T., Smith; H., Roussel; F., Walter; L., Armus; A.D., Bolatto; M., Boquien; A., Crocker; I., De Looze; J., Donovan Meyer; G., Helou; J., Hinz; B.D, Johnson; J., Koda; A., Miller; E., Montiel; E.J., Murphy; M., Relano; H.-W., Rix; E., Schinnerer; R., Skibba; M.G., Wolfire; C.W, Engelbracht
Type:
Dataset
Issue Date:
2019

167. Dynamic reconfiguration of the default mode network during narrative comprehension

Author(s):
Simony, Erez; Honey, Christopher; Chen, Janice; Lositsky, Olga; Yeshurun, Yaara; Wiesel, Ami; Hasson, Uri
Abstract:
Does the default mode network (DMN) reconfigure to encode information about the changing environment? This question has proven difficult, because patterns of functional connectivity reflect a mixture of stimulus-induced neural processes, intrinsic neural processes and non-neuronal noise. Here we introduce inter-subject functional correlation (ISFC), which isolates stimulus-dependent inter-regional correlations between brains exposed to the same stimulus. During fMRI, we had subjects listen to a real-life auditory narrative and to temporally scrambled versions of the narrative. We used ISFC to isolate correlation patterns within the DMN that were locked to the processing of each narrative segment and specific to its meaning within the narrative context. The momentary configurations of DMN ISFC were highly replicable across groups. Moreover, DMN coupling strength predicted memory of narrative segments. Thus, ISFC opens new avenues for linking brain network dynamics to stimulus features and behaviour.
Type:
Dataset
Issue Date:
18 July 2016

191. Enhancement of edge turbulence concomitant with ELM suppression during boron powder injection in EAST

Author(s):
Sun, Zhen; Maingi, Rajesh; Diallo, Ahmed; Xu, Wei; Qian, Yuzhong; Tritz, Kevin; Ye, Yang; Li, Chenglong; Xu, Zong; Wang, Yifeng; Ye, Kaixuan; Bortolon, A.; Nagy, Alex; Zhang, Ling; Duan, Yanmin; Lu, Zhiyuan; Wang, Huiqian; Shi, Tonghui; Zhao, Hailin; Wei, Gao; Xu, Jichan; Chen, Ran; Huang, Ming; Zuo, Guizhong; Xu, Guosheng; Gong, Xianzu; Hu, Jiansheng
Type:
Dataset
Issue Date:
2021

200. Fast animal pose estimation using deep neural networks

Author(s):
Pereira, Talmo D.; Aldarondo, Diego E.; Willmore, Lindsay; Kislin, Mikhail; Wang, Samuel S.-H.; Murthy, Mala; Shaevitz, Joshua W.
Abstract:
Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animal's body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAP's applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.
Type:
Dataset
Issue Date:
30 May 2018