pandas-dev/pandas: Pandas 1.2.4

Reback, Jeff; McKinney, Wes; jbrockmendel,; Bossche, Joris Van den; Augspurger, Tom; Cloud, Phillip; Hawkins, Simon; gfyoung,; Sinhrks,; Roeschke, Matthew; Klein, Adam; Petersen, Terji; Tratner, Jeff; She, Chang; Ayd, William; Naveh, Shahar; Patrick,; Garcia, Marc; Schendel, Jeremy; Hayden, Andy; Saxton, Daniel; Jancauskas, Vytautas; Gorelli, Marco; Shadrach, Richard; McMaster, Ali; Battiston, Pietro; Seabold, Skipper; Dong, Kaiqi; chris-b,; h-vetinari,

doi:10.5281/zenodo.4681666

Cited by 28 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Theano library was used as its backend and a Titan GPU was utilized for fast neural network training. The pandas library ( Reback et al., 2021 ) was utilized to first load and preprocess sgRNA sequences, which were each encoded as a vector of 20 one-hot vectors: A(1,0,0,0), C(0,1,0,0), G(0,0,1,0), and T(0,0,0,1). Hyperas ( https://github.com/maxpumperla/hyperas ) was utilized to optimize the hyperparameters of each model architecture, and mean squared error was used to select the best model.…”

Section: Methodsmentioning

confidence: 99%

An integrated pipeline for mammalian genetic screening

Kramme

Plesa

Wang

et al. 2021

Cell Reports Methods

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

An integrated pipeline for mammalian genetic screening

Kramme

Plesa

Wang

et al. 2021

Cell Reports Methods

View full text Add to dashboard Cite

“…Gardner-Altman paired mean difference estimation plots were generated using DABEST software suite 107 . All other figures were prepared using the Seaborn 108 and Matplotlib 109 Python visualization libraries, and the pandas 110,111…”

Section: Visualization and Statisticsmentioning

confidence: 99%

Blebs Promote Cell Survival by Assembling Oncogenic Signaling Hubs

Driscoll

et al. 2021

Preprint

View full text Add to dashboard Cite

For most human cells, anchorage is a key necessity for survival. Cell-substrate adhesion activates diverse signaling pathways, without which cells undergo anoikis – a form of programmed cell death1. Acquisition of anoikis resistance is a pivotal step in cancer disease progression, as metastasizing cancer cells often lose firm attachment to surrounding tissue2–5. In these poorly attached states, cells often adopt rounded morphologies and form small hemispherical plasma membrane protrusions called blebs6–13. Bleb function has long been investigated in the context of amoeboid migration but is far less deeply examined in other scenarios14–19. Here we show by quantitative subcellular 3D imaging and manipulation of cell morphological states that blebbing triggers the formation of membrane-proximal signaling hubs that initiate signaling cascades leading to anoikis resistance. Specifically, in melanoma cells we discovered that blebbing generates plasma membrane contours that recruit curvature sensing septin proteins, which scaffold constitutively active mutant NRAS and effectors, driving the upregulation of ERK and PI3K signaling. Inhibition of blebs or septins has little effect on the survival of well-adhered cells, but in detached cells causes NRAS mislocalization, reduced MAPK and PI3K signaling, and ultimately, death. These data unveil an unanticipated morphological requirement for mutant NRAS to operate as an effective oncoprotein, suggesting novel clinical targets for the treatment of NRAS-driven melanoma. Furthermore, they define an unforeseen role for blebs as potent signaling organelles capable of integrating myriad cellular information flows into concerted signaling responses, in this case granting robust anoikis resistance.Abstract Figure

show abstract

“…CMC (Joshi et al 2000;Watters et al 2000;Joshi et al 2001;Fregeau et al 2002Fregeau et al , 2003Chatterjee et al 2010;Morscher et al 2013;Pattabiraman et al 2013;Rodriguez et al 2021, this work), cmctoolkit (Rui et al 2021a,b), fewbody (Fregeau & Rasio 2007;Antognini et al 2014;Amaro-Seoane & Chen 2016), COSMIC (Breivik et al 2020b,a), matplotlib (Hunter 2007), SciPy (Virtanen et al 2020), NumPy (Harris et al 2020), pandas (McKinney 2010Reback et al 2021).…”

Section: Softwarementioning

confidence: 88%

“…Each snapshot is saved in its respective file as a pandas-readable HDF5 table with keys corresponding to the simulation time when the snapshot was written. These can be directly imported into Python using the read hdf command in pandas (McKinney 2010;Reback et al 2021).…”

Section: Input/outputmentioning

confidence: 99%

Modeling Dense Star Clusters in the Milky Way and Beyond with the Cluster Monte Carlo Code

Rodriguez,

Weatherford,

Coughlin

et al. 2021

Preprint

View full text Add to dashboard Cite

We describe the public release of the Cluster Monte Carlo Code (CMC) a parallel, star-by-star N -body code for modeling dense star clusters. CMC treats collisional stellar dynamics using Hénon's method, where the cumulative effect of many two-body encounters is statistically reproduced as a single effective encounter between nearest-neighbor particles on a relaxation timescale. The star-by-star approach allows for the inclusion of additional physics, including strong gravitational three-and four-body encounters, two-body tidal and gravitational-wave captures, mass loss in arbitrary galactic tidal fields, and stellar evolution for both single and binary stars. The public release of CMC is pinned directly to the COSMIC population synthesis code, allowing dynamical star cluster simulations and population synthesis studies to be performed using identical assumptions about the stellar physics and initial conditions. As a demonstration, we present two examples of star cluster modeling: first, we perform the largest (N = 10 8 ) star-by-star N -body simulation of a Plummer sphere evolving to core collapse, reproducing the expected self-similar density profile over more than 15 orders of magnitude; second, we generate realistic models for typical globular clusters, and we show that their dynamical evolution can produce significant numbers of black hole mergers with masses greater than those produced from isolated binary evolution (such as GW190521, a recently reported merger with component masses in the pulsational pair-instability mass gap).

show abstract

pandas-dev/pandas: Pandas 1.2.4

Cited by 28 publications

References 0 publications

An integrated pipeline for mammalian genetic screening

An integrated pipeline for mammalian genetic screening

Blebs Promote Cell Survival by Assembling Oncogenic Signaling Hubs

Modeling Dense Star Clusters in the Milky Way and Beyond with the Cluster Monte Carlo Code

Contact Info

Product

Resources

About