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161. 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. Code and data from "Comparative genomic analysis reveals varying levels of mammalian adaptation to coronavirus infections"

Author(s):
King, Sean
Abstract:
Severe acute respiratory coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is of zoonotic origin. Evolutionary analyses assessing whether coronaviruses similar to SARS-CoV-2 infected ancestral species of modern-day animal hosts could be useful in identifying additional reservoirs of potentially dangerous coronaviruses. We reasoned that if a clade of species has been repeatedly exposed to a virus, then their proteins relevant for viral entry may exhibit adaptations that affect host susceptibility or response. We perform comparative analyses across the mammalian phylogeny of angiotensin-converting enzyme 2 (ACE2), the cellular receptor for SARS-CoV-2, in order to uncover evidence for selection acting at its binding interface with the SARS-CoV-2 spike protein. We uncover that in rodents there is evidence for adaptive amino acid substitutions at positions comprising the ACE2-spike interaction interface, whereas the variation within ACE2 proteins in primates and some other mammalian clades is not consistent with evolutionary adaptations. We also analyze aminopeptidase N (APN), the receptor for the human coronavirus 229E, a virus that causes the common cold, and find evidence for adaptation in primates. Altogether, our results suggest that the rodent and primate lineages may have had ancient exposures to viruses similar to SARS-CoV-2 and HCoV-229E, respectively. Included in this repository are the instructions and corresponding code required to build the dataset and run the analysis in the manuscript.
Type:
Dataset
Issue Date:
28 September 2021

164. Data corresponding to our paper on "Chemotactic smoothing of collective migration"

Author(s):
Bhattacharjee, Tapomoy; Amchin, Daniel; Alert, Ricard; Ott, Jenna; Datta, Sujit
Abstract:
Collective migration -- the directed, coordinated motion of many self-propelled agents -- is a fascinating emergent behavior exhibited by active matter that has key functional implications for biological systems. Extensive studies have elucidated the different ways in which this phenomenon may arise. Nevertheless, how collective migration can persist when a population is confronted with perturbations, which inevitably arise in complex settings, is poorly understood. Here, by combining experiments and simulations, we describe a mechanism by which collectively migrating populations smooth out large-scale perturbations in their overall morphology, enabling their constituents to continue to migrate together. We focus on the canonical example of chemotactic migration of Escherichia coli, in which fronts of cells move via directed motion, or chemotaxis, in response to a self-generated nutrient gradient. We identify two distinct modes in which chemotaxis influences the morphology of the population: cells in different locations along a front migrate at different velocities due to spatial variations in (i) the local nutrient gradient and in (ii) the ability of cells to sense and respond to the local nutrient gradient. While the first mode is destabilizing, the second mode is stabilizing and dominates, ultimately driving smoothing of the overall population and enabling continued collective migration. This process is autonomous, arising without any external intervention; instead, it is a population-scale consequence of the manner in which individual cells transduce external signals. Our findings thus provide insights to predict, and potentially control, the collective migration and morphology of cell populations and diverse other forms of active matter.
Type:
Dataset
Issue Date:
24 June 2021

165. Alternative energy vehicle deployment coupled with decarbonizing electricity generation amplifies climate, air quality and health co-benefits in China

Author(s):
Peng, Liqun; Liu, Feiqi; Zhou, Mi; Li, Mingwei; Zhang, Qiang; Mauzerall, Denise L.
Abstract:
China is the world's largest carbon emitter and suffers from severe air pollution. About one million deaths in China were attributable to air pollution in 2017. Alternative energy vehicles (AEVs), e.g. electric, hydrogen fuel cell, and natural gas vehicles, can help achieve both carbon emission mitigation and air quality improvement. However, climate, air quality and health co-benefit of AEVs powered by deeply decarbonized electricity generation remain poorly quantified. Here, we conduct a quantitative integrated assessment of the air quality, health, carbon emission mitigation and economic benefits of AEV deployment as the electricity grid decarbonizes in China. We find population-weighted annual PM2.5 and summer O3 concentration can decrease as large as 5.7μgm−3 and 4.9ppb. Annual avoided premature mortalities and years of life lost resulting from improved ambient air pollution can be as large as ~329,000 persons and ~1,611,000 years. We thus show that maximizing climate, air quality and health benefits of AEV deployment in China requires rapid decarbonization of the power system.
Type:
Dataset
Issue Date:
17 June 2021

166. Shakespeare and Company Project Dataset: Lending Library Books

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin Yun; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
This dataset includes information about approximately 6,000 books and other items with bibliographic data as well as summary information about when the item circulated in the Shakespeare and Company lending library and the number of times an item was borrowed or purchased.
Type:
Dataset
Issue Date:
29 January 2021

167. Shakespeare and Company Project Dataset: Lending Library Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin Yun; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Events dataset includes information about approximately 35,000 lending library events including membership activities such as subscriptions, renewals and reimbursements and book-related activities such as borrowing and purchasing. For events related to lending library cards that are available as digital surrogates, IIIF links are provided.
Type:
Dataset
Issue Date:
29 January 2021

168. Shakespeare and Company Project Dataset: Lending Library Members

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin Yun; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Members dataset includes information about approximately 5,600 members of Sylvia Beach's Shakespeare and Company lending library.
Type:
Dataset
Issue Date:
29 January 2021

169. Shakespeare and Company Project Dataset: Lending Library Members, Books, Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin Yun; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project makes three datasets available to download in CSV and JSON formats. The datasets provide information about lending library members; the books that circulated in the lending library; and lending library events, including borrows, purchases, memberships, and renewals. The datasets may be used individually or in combination site URLs are consistent identifiers across all three. The DOIs for each dataset are as follows: Members (https://doi.org/10.34770/nsa4-3t76); Books (https://doi.org/10.34770/079z-h206); Events (https://doi.org/10.34770/rtbp-kv40).
Type:
Dataset
Issue Date:
29 January 2021

172. CMT3D - Catalog

Author(s):
Sawade, Lucas; Beller, Stephen; Lei, Wenjie; Tromp, Jeroen
Type:
Dataset
Issue Date:
2021

173. CMT3D - Software

Author(s):
Sawade, Lucas; Lei, Wenjie; Beller, Stephen; Tromp, Jeroen
Type:
Dataset
Issue Date:
2021

176. CrvA and CrvB form a curvature-inducing module sufficient to induce cell shape complexity in Gram-negative bacteria

Author(s):
Martin, Nicholas R; Blackman, Edith; Bratton, Benjamin P; Chase, Katelyn J; Bartlett, Thomas M; Gitai, Zemer
Abstract:
Bacterial species have diverse cell shapes that enable motility, colonization, and virulence. The cell wall defines bacterial shape and is primarily built by two cytoskeleton-guided synthesis machines, the elongasome and the divisome. However, the mechanisms producing complex shapes, like the curved-rod shape of Vibrio cholerae, are incompletely defined. Previous studies have reported that species-specific regulation of cytoskeleton-guided machines enables formation of complex bacterial shapes such as cell curvature and cellular appendages. In contrast, we report that CrvA and CrvB are sufficient to induce complex cell shape autonomously of the cytoskeleton in V. cholerae. The autonomy of the CrvAB module also enables it to induce curvature in the Gram-negative species Escherichia coli, Pseudomonas aeruginosa, Caulobacter crescentus, and Agrobacterium tumefaciens. Using inducible gene expression, quantitative microscopy, and biochemistry we show that CrvA and CrvB circumvent the need for patterning via cytoskeletal elements by regulating each other to form an asymmetrically-localized, periplasmic structure that directly binds to the cell wall. The assembly and disassembly of this periplasmic structure enables dynamic changes in cell shape. Bioinformatics indicate that CrvA and CrvB may have diverged from a single ancestral hybrid protein. Using fusion experiments in V. cholerae, we find that a synthetic CrvA/B hybrid protein is sufficient to induce curvature on its own, but that expression of two distinct proteins, CrvA and CrvB, promotes more rapid curvature induction. We conclude that morphological complexity can arise independently of cell shape specification by the core cytoskeleton-guided synthesis machines.
Type:
Dataset
Issue Date:
2021