Small Model Networks

2016 ◽  
pp. 186-207
Keyword(s):  
2013 ◽  
Vol 1 (1) ◽  
Author(s):  
Paul Rastall

The paper presents a way of investigating verbal communication and examining assumptions about it independently of particular approaches to linguistic analysis through the development of imaginary language systems using very limited models (small model languages), identifying limitations, and finding ways to extend them. The imaginary systems are compared to real verbal phenomena to highlight communicational principles and show where questions arise. They can be pedagogical tools. A simple model is introduced as an example and ways of extending it are considered along with the questions that are raised in the process.


2018 ◽  
Vol 74 (3) ◽  
pp. 194-204 ◽  
Author(s):  
Iracema Caballero ◽  
Massimo Sammito ◽  
Claudia Millán ◽  
Andrey Lebedev ◽  
Nicolas Soler ◽  
...  

ARCIMBOLDOsolves the phase problem by combining the location of small model fragments usingPhaserwith density modification and autotracing usingSHELXE. Mainly helical structures constitute favourable cases, which can be solved using polyalanine helical fragments as search models. Nevertheless, the solution of coiled-coil structures is often complicated by their anisotropic diffraction and apparent translational noncrystallographic symmetry. Long, straight helices have internal translational symmetry and their alignment in preferential directions gives rise to systematic overlap of Patterson vectors. This situation has to be differentiated from the translational symmetry relating different monomers.ARCIMBOLDO_LITEhas been run on single workstations on a test pool of 150 coiled-coil structures with 15–635 amino acids per asymmetric unit and with diffraction data resolutions of between 0.9 and 3.0 Å. The results have been used to identify and address specific issues when solving this class of structures usingARCIMBOLDO. Features fromPhaserv.2.7 onwards are essential to correct anisotropy and produce translation solutions that will pass the packing filters. As the resolution becomes worse than 2.3 Å, the helix direction may be reversed in the placed fragments. Differentiation between true solutions and pseudo-solutions, in which helix fragments were correctly positioned but in a reverse orientation, was found to be problematic at resolutions worse than 2.3 Å. Therefore, after every new fragment-placement round, complete or sparse combinations of helices in alternative directions are generated and evaluated. The final solution is once again probed by helix reversal, refinement and extension. To conclude, density modification andSHELXEautotracing incorporating helical constraints is also exploited to extend the resolution limit in the case of coiled coils and to enhance the identification of correct solutions. This study resulted in a specialized mode withinARCIMBOLDOfor the solution of coiled-coil structures, which overrides the resolution limit and can be invoked from the command line (keyword coiled_coil) orARCIMBOLDO_LITEtask interface inCCP4i.


1977 ◽  
Vol 9 (2) ◽  
pp. 151-155 ◽  
Author(s):  
Pierre Lutz ◽  
Claude Picot ◽  
Gérard Hild ◽  
Paul Rempp
Keyword(s):  

2011 ◽  
Vol 100 (3) ◽  
pp. 212a ◽  
Author(s):  
Jia Lin Huang ◽  
Karson Schmidt ◽  
Leigh Murray ◽  
Michael E. Noss ◽  
Michelle R. Bunagan

2020 ◽  
Author(s):  
Alexander J.E. Kell ◽  
Sophie L. Bokor ◽  
You-Nah Jeon ◽  
Tahereh Toosi ◽  
Elias B. Issa

The marmoset—a small monkey with a flat cortex—offers powerful techniques for studying neural circuits in a primate. However, it remains unclear whether brain functions typically studied in larger primates can be studied in the marmoset. Here, we asked whether the 300-gram marmosets’ perceptual and cognitive repertoire approaches human levels or is instead closer to rodents’. Using high-level visual object recognition as a testbed, we found that on the same task marmosets substantially outperformed rats and generalized far more robustly across images, all while performing ∼1000 trials/day. We then compared marmosets against the high standard of human behavior. Across the same 400 images, marmosets’ image-by-image recognition behavior was strikingly human-like—essentially as human-like as macaques’. These results demonstrate that marmosets have been substantially underestimated and that high-level abilities have been conserved across simian primates. Consequently, marmosets are a potent small model organism for visual neuroscience, and perhaps beyond.


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