Journal articles: 'Collective motion'

1

Vicsek, Tamás, and Anna Zafeiris. "Collective motion." Physics Reports 517, no. 3-4 (August 2012): 71–140. http://dx.doi.org/10.1016/j.physrep.2012.03.004.

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Rosensteel, G., and J. Troupe. "Nonlinear collective motion." Journal of Physics G: Nuclear and Particle Physics 25, no. 3 (January 1, 1999): 549–56. http://dx.doi.org/10.1088/0954-3899/25/3/007.

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Horiuchi, Noriaki. "Quantum collective motion." Nature Photonics 7, no. 6 (May 30, 2013): 422–23. http://dx.doi.org/10.1038/nphoton.2013.142.

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Charlesworth, Henry J., and Matthew S. Turner. "Intrinsically motivated collective motion." Proceedings of the National Academy of Sciences 116, no. 31 (July 17, 2019): 15362–67. http://dx.doi.org/10.1073/pnas.1822069116.

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Collective motion is found in various animal systems, active suspensions, and robotic or virtual agents. This is often understood by using high-level models that directly encode selected empirical features, such as coalignment and cohesion. Can these features be shown to emerge from an underlying, low-level principle? We find that they emerge naturally under future state maximization (FSM). Here, agents perceive a visual representation of the world around them, such as might be recorded on a simple retina, and then move to maximize the number of different visual environments that they expect to be able to access in the future. Such a control principle may confer evolutionary fitness in an uncertain world by enabling agents to deal with a wide variety of future scenarios. The collective dynamics that spontaneously emerge under FSM resemble animal systems in several qualitative aspects, including cohesion, coalignment, and collision suppression, none of which are explicitly encoded in the model. A multilayered neural network trained on simulated trajectories is shown to represent a heuristic mimicking FSM. Similar levels of reasoning would seem to be accessible under animal cognition, demonstrating a possible route to the emergence of collective motion in social animals directly from the control principle underlying FSM. Such models may also be good candidates for encoding into possible future realizations of artificial “intelligent” matter, able to sense light, process information, and move.

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Felderhof, B. U. "Collective motion in ferrofluids." Journal of Physics: Conference Series 392 (December 11, 2012): 012001. http://dx.doi.org/10.1088/1742-6596/392/1/012001.

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Troupe, J., and G. Rosensteel. "Algebraic Nonlinear Collective Motion." Annals of Physics 270, no. 1 (November 1998): 126–54. http://dx.doi.org/10.1006/aphy.1998.5858.

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7

Dönau, Friedrich. "Dynamics of collective motion." Nuclear Physics A 520 (December 1990): c437—c449. http://dx.doi.org/10.1016/0375-9474(90)91166-o.

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Bertsch, G. F. "Large amplitude collective motion." Nuclear Physics A 574, no. 1-2 (July 1994): 169–83. http://dx.doi.org/10.1016/0375-9474(94)90044-2.

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Nabeel, Arshed, and Danny Raj Masila. "Disentangling intrinsic motion from neighborhood effects in heterogeneous collective motion." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 6 (June 2022): 063119. http://dx.doi.org/10.1063/5.0093682.

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Most real-world collectives, including animal groups, pedestrian crowds, active particles, and living cells, are heterogeneous. The differences among individuals in their intrinsic properties have emergent effects at the group level. It is often of interest to infer how the intrinsic properties differ among the individuals based on their observed movement patterns. However, the true individual properties may be masked by the nonlinear interactions in the collective. We investigate the inference problem in the context of a bidisperse collective with two types of agents, where the goal is to observe the motion of the collective and classify the agents according to their types. Since collective effects, such as jamming and clustering, affect individual motion, the information in an agent’s own movement is insufficient for accurate classification. A simple observer algorithm, based only on individual velocities, cannot accurately estimate the level of heterogeneity of the system and often misclassifies agents. We propose a novel approach to the classification problem, where collective effects on an agent’s motion are explicitly accounted for. We use insights about the phenomenology of collective motion to quantify the effect of the neighborhood on an agent’s motion using a neighborhood parameter. Such an approach can distinguish between agents of two types, even when their observed motion is identical. This approach estimates the level of heterogeneity much more accurately and achieves significant improvements in classification. Our results demonstrate that explicitly accounting for neighborhood effects is often necessary to correctly infer intrinsic properties of individuals.

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Okolowicz, J., J. M. Irvine, and J. Nemeth. "Nuclear temperatures and collective motion." Journal of Physics G: Nuclear Physics 11, no. 6 (June 1985): 721–34. http://dx.doi.org/10.1088/0305-4616/11/6/009.

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Rabani, Amit, Gil Ariel, and Avraham Be'er. "Collective Motion of Spherical Bacteria." PLoS ONE 8, no. 12 (December 20, 2013): e83760. http://dx.doi.org/10.1371/journal.pone.0083760.

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Mischiati, Matteo, and P. S. Krishnaprasad. "Geometric decompositions of collective motion." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2200 (April 2017): 20160571. http://dx.doi.org/10.1098/rspa.2016.0571.

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Collective motion in nature is a captivating phenomenon. Revealing the underlying mechanisms, which are of biological and theoretical interest, will require empirical data, modelling and analysis techniques. Here, we contribute a geometric viewpoint, yielding a novel method of analysing movement. Snapshots of collective motion are portrayed as tangent vectors on configuration space, with length determined by the total kinetic energy. Using the geometry of fibre bundles and connections, this portrait is split into orthogonal components each tangential to a lower dimensional manifold derived from configuration space. The resulting decomposition, when interleaved with classical shape space construction, is categorized into a family of kinematic modes—including rigid translations, rigid rotations, inertia tensor transformations, expansions and compressions. Snapshots of empirical data from natural collectives can be allocated to these modes and weighted by fractions of total kinetic energy. Such quantitative measures can provide insight into the variation of the driving goals of a collective, as illustrated by applying these methods to a publicly available dataset of pigeon flocking. The geometric framework may also be profitably employed in the control of artificial systems of interacting agents such as robots.

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Rodríguez, Tomás R., and J. Luis Egido. "Collective motion in complex nuclei." Journal of Physics: Conference Series 312, no. 9 (September 23, 2011): 092009. http://dx.doi.org/10.1088/1742-6596/312/9/092009.

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Olemskoi, A. I., and O. V. Yushchenko. "Collective Motion of Active Particles." Russian Physics Journal 47, no. 4 (April 2004): 453–60. http://dx.doi.org/10.1023/b:rupj.0000042776.25572.7f.

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15

Deutsch, Andreas, Guy Theraulaz, and Tamas Vicsek. "Collective motion in biological systems." Interface Focus 2, no. 6 (October 10, 2012): 689–92. http://dx.doi.org/10.1098/rsfs.2012.0048.

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Balbutsev, E. B. "Collective motion from various aspects." Physics of Particles and Nuclei 39, no. 6 (November 2008): 912–49. http://dx.doi.org/10.1134/s1063779608060038.

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17

Strömbom, Daniel. "Collective motion from local attraction." Journal of Theoretical Biology 283, no. 1 (August 2011): 145–51. http://dx.doi.org/10.1016/j.jtbi.2011.05.019.

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Rupprecht, Nathaniel, and Dervis Can Vural. "Collective motion of predictive swarms." PLOS ONE 12, no. 10 (October 24, 2017): e0186785. http://dx.doi.org/10.1371/journal.pone.0186785.

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19

Justh, Eric W., and P. S. Krishnaprasad. "Optimality, reduction and collective motion." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2177 (May 2015): 20140606. http://dx.doi.org/10.1098/rspa.2014.0606.

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The planar self-steering particle model of agents in a collective gives rise to dynamics on the N -fold direct product of SE (2), the rigid motion group in the plane. Assuming a connected, undirected graph of interaction between agents, we pose a family of symmetric optimal control problems with a coupling parameter capturing the strength of interactions. The Hamiltonian system associated with the necessary conditions for optimality is reducible to a Lie–Poisson dynamical system possessing interesting structure. In particular, the strong coupling limit reveals additional (hidden) symmetry, beyond the manifest one used in reduction: this enables explicit integration of the dynamics, and demonstrates the presence of a ‘master clock’ that governs all agents to steer identically. For finite coupling strength, we show that special solutions exist with steering controls proportional across the collective. These results suggest that optimality principles may provide a framework for understanding imitative behaviours observed in certain animal aggregations.

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20

Warren, William H. "Collective Motion in Human Crowds." Current Directions in Psychological Science 27, no. 4 (July 11, 2018): 232–40. http://dx.doi.org/10.1177/0963721417746743.

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The balletic motion of bird flocks, fish schools, and human crowds is believed to emerge from local interactions between individuals in a process of self-organization. The key to explaining such collective behavior thus lies in understanding these local interactions. After decades of theoretical modeling, experiments using virtual crowds and analysis of real crowd data are enabling us to decipher the “rules of engagement” governing these interactions. On the basis of such results, my students and I built a dynamical model of how a pedestrian aligns his or her motion with that of a neighbor and how these binary interactions are combined within a neighborhood of interaction. Computer simulations of the model generate coherent motion at the global level and reproduce individual trajectories at the local level. This approach has yielded the first experiment-driven, bottom-up model of collective motion, providing a basis for understanding more complex patterns of crowd behavior in both everyday and emergency situations.

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21

Bonche, P., E. Chabanat, B. Q. Chen, J. Dobaczewski, H. Flocard, B. Gall, P. H. Heenen, J. Meyer, N. Tajima, and M. S. Weiss. "Microscopic approach to collective motion." Nuclear Physics A 574, no. 1-2 (July 1994): 185–205. http://dx.doi.org/10.1016/0375-9474(94)90045-0.

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22

Brink, D. M. "Collective motion in excited nuclei." Nuclear Physics A 574, no. 1-2 (July 1994): 207–15. http://dx.doi.org/10.1016/0375-9474(94)90046-9.

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23

Smith, Colin A., David Ban, Supriya Pratihar, Karin Giller, Maria Paulat, Stefan Becker, Christian Griesinger, Donghan Lee, and Bert L. de Groot. "Allosteric switch regulates protein–protein binding through collective motion." Proceedings of the National Academy of Sciences 113, no. 12 (March 9, 2016): 3269–74. http://dx.doi.org/10.1073/pnas.1519609113.

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Many biological processes depend on allosteric communication between different parts of a protein, but the role of internal protein motion in propagating signals through the structure remains largely unknown. Through an experimental and computational analysis of the ground state dynamics in ubiquitin, we identify a collective global motion that is specifically linked to a conformational switch distant from the binding interface. This allosteric coupling is also present in crystal structures and is found to facilitate multispecificity, particularly binding to the ubiquitin-specific protease (USP) family of deubiquitinases. The collective motion that enables this allosteric communication does not affect binding through localized changes but, instead, depends on expansion and contraction of the entire protein domain. The characterization of these collective motions represents a promising avenue for finding and manipulating allosteric networks.

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24

Ariel, Gil, and Amir Ayali. "Locust Collective Motion and Its Modeling." PLOS Computational Biology 11, no. 12 (December 10, 2015): e1004522. http://dx.doi.org/10.1371/journal.pcbi.1004522.

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25

Szabó, A., R. Ünnep, E. Méhes, W. O. Twal, W. S. Argraves, Y. Cao, and A. Czirók. "Collective cell motion in endothelial monolayers." Physical Biology 7, no. 4 (November 12, 2010): 046007. http://dx.doi.org/10.1088/1478-3975/7/4/046007.

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26

Sakata, Fumihiko, Toshio Marumori, Yukio Hashimoto, and Shi-Wei Yan. "Nonlinear Dynamics of Nuclear Collective Motion." Progress of Theoretical Physics Supplement 141 (2001): 1–111. http://dx.doi.org/10.1143/ptps.141.1.

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27

Bertsch, G. F., and H. Feldmeier. "Variational approach to anharmonic collective motion." Physical Review C 56, no. 2 (August 1, 1997): 839–46. http://dx.doi.org/10.1103/physrevc.56.839.

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28

Butail, Sachit, and Maurizio Porfiri. "Detecting switching leadership in collective motion." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 1 (January 2019): 011102. http://dx.doi.org/10.1063/1.5079869.

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29

Algar, Shannon D., Thomas Lymburn, Thomas Stemler, Michael Small, and Thomas Jüngling. "Learned emergence in selfish collective motion." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 12 (December 2019): 123101. http://dx.doi.org/10.1063/1.5120776.

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30

Chomaz, Ph, and C. Simenel. "Coupled collective motion in nuclear reactions." Nuclear Physics A 731 (February 2004): 188–201. http://dx.doi.org/10.1016/j.nuclphysa.2003.11.031.

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31

Sakata, F., K. Iwasawa, T. Marumori, and J. Terasaki. "Nonlinear dynamics and nuclear collective motion." Physics Reports 264, no. 1-5 (January 1996): 339–55. http://dx.doi.org/10.1016/0370-1573(95)00047-x.

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32

Hofmann, H., R. Samhammer, and S. Yamaji. "Nuclear collective motion: Markovian or not?" Physics Letters B 229, no. 4 (October 1989): 309–15. http://dx.doi.org/10.1016/0370-2693(89)90409-7.

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33

Leonard, Naomi, Luca Scardovi, and Rodolphe Sepulchre. "Stabilization of Three-Dimensional Collective Motion." Communications in Information and Systems 8, no. 4 (2008): 473–500. http://dx.doi.org/10.4310/cis.2008.v8.n4.a6.

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34

Gov, N. S. "Traction forces during collective cell motion." HFSP Journal 3, no. 4 (August 2009): 223–27. http://dx.doi.org/10.2976/1.3185785.

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35

Herbert-Read, J. E., M. Romenskyy, and D. J. T. Sumpter. "A Turing test for collective motion." Biology Letters 11, no. 12 (December 2015): 20150674. http://dx.doi.org/10.1098/rsbl.2015.0674.

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A widespread problem in biological research is assessing whether a model adequately describes some real-world data. But even if a model captures the large-scale statistical properties of the data, should we be satisfied with it? We developed a method, inspired by Alan Turing, to assess the effectiveness of model fitting. We first built a self-propelled particle model whose properties (order and cohesion) statistically matched those of real fish schools. We then asked members of the public to play an online game (a modified Turing test) in which they attempted to distinguish between the movements of real fish schools or those generated by the model. Even though the statistical properties of the real data and the model were consistent with each other, the public could still distinguish between the two, highlighting the need for model refinement. Our results demonstrate that we can use ‘citizen science’ to cross-validate and improve model fitting not only in the field of collective behaviour, but also across a broad range of biological systems.

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36

Ogawa, Hiroaki, Michisuke Kobayashi, Tomohiro Iseki, and Masaru Aniya. "Collective Motion in Liquid Silver Chalcogenides." Journal of the Physical Society of Japan 74, no. 8 (August 2005): 2265–69. http://dx.doi.org/10.1143/jpsj.74.2265.

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37

Theillard, Maxime, Roberto Alonso-Matilla, and David Saintillan. "Geometric control of active collective motion." Soft Matter 13, no. 2 (2017): 363–75. http://dx.doi.org/10.1039/c6sm01955b.

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38

Wei-Min, Zhang. "Microscopic study of fermionic collective motion." Nuclear Physics A 467, no. 3 (June 1987): 422–36. http://dx.doi.org/10.1016/0375-9474(87)90538-0.

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39

Tveter, T. S., J. J. Gaardhøje, A. Maj, T. Ramsøy, A. Atac, J. Bacelar, A. Bracco, et al. "Collective motion in hot superheavy nuclei." Nuclear Physics A 599, no. 1-2 (March 1996): 123–28. http://dx.doi.org/10.1016/0375-9474(96)00054-1.

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40

Liu, Bo, Tianguang Chu, and Long Wang. "Collective motion in non-reciprocal swarms." Journal of Control Theory and Applications 7, no. 2 (February 2009): 105–11. http://dx.doi.org/10.1007/s11768-009-7211-6.

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41

Jeżabek, M. "On collective motion of chiral bags." Physics Letters B 174, no. 4 (July 1986): 429–33. http://dx.doi.org/10.1016/0370-2693(86)91031-2.

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42

Seligman, T. H., J. J. M. Verbaarschot, and H. A. Weidenmüller. "Chaotic motion and collective nuclear rotation." Physics Letters B 167, no. 4 (February 1986): 365–69. http://dx.doi.org/10.1016/0370-2693(86)91281-5.

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43

Rowe, D. J. "Algebraic models of nuclear collective motion." Nuclear Physics A 574, no. 1-2 (July 1994): 253–68. http://dx.doi.org/10.1016/0375-9474(94)90049-3.

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44

Naotaka, Yoshinaga. "Fundamental pairs in nuclear collective motion." Nuclear Physics A 570, no. 1-2 (March 1994): 421–28. http://dx.doi.org/10.1016/0375-9474(94)90309-3.

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45

Sakata, F., T. Marumori, and M. Ogura. "Quantum Theory of Dynamical Collective Subspace for Large-Amplitude Collective Motion." Progress of Theoretical Physics 76, no. 2 (August 1, 1986): 400–413. http://dx.doi.org/10.1143/ptp.76.400.

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46

Cambui, Dorílson S. "Collective behavior states in animal groups." Modern Physics Letters B 31, no. 06 (February 28, 2017): 1750054. http://dx.doi.org/10.1142/s0217984917500543.

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In this work, we study some states of collective behavior observed in groups of animals. For this end we consider an agent-based model with biologically motivated behavioral rules where the speed is treated as an independent stochastic variable, and the motion direction is adjusted in accord with alignment and attractive interactions. Four types of collective behavior have been observed: disordered motion, collective rotation, coherent collective motion, and formation flight. We investigate the case when transitions between collective states depend on both the speed and the attraction between individuals. Our results show that, to any size of the attraction, small speeds are associated to the coherent collective motion, while collective rotation is more and more pronounced for high speed since the attraction radius is large enough.

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47

Antoniou, Dimitri, and Steven D. Schwartz. "Low-Frequency Collective Motions in Proteins." Journal of Theoretical and Computational Chemistry 02, no. 02 (June 2003): 163–69. http://dx.doi.org/10.1142/s0219633603000458.

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There are several kinds of low-frequency collective motions in proteins, which are believed to have a significant effect on their properties. We propose that a new kind of global collective motion in proteins are density fluctuations, which are slowly-varying, long-lived, propagating disturbances. These can be studied using the linear response formalism, which is a dynamical approximation that uses the full anharmonic interatomic potential. We have performed a molecular dynamics simulation of a realistic protein and have found results that are consistent with the theoretical predictions of linear response theory.

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48

Garnier, Josselin, George Papanicolaou, and Tzu-Wei Yang. "Mean field model for collective motion bistability." Discrete & Continuous Dynamical Systems - B 24, no. 2 (2019): 851–79. http://dx.doi.org/10.3934/dcdsb.2018210.

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49

Afroze, Farhana, Daisuke Inoue, Tamanna Ishrat Farhana, Tetsuya Hiraiwa, Ryo Akiyama, Arif Md Rashedul Kabir, Kazuki Sada, and Akira Kakugo. "Monopolar flocking of microtubules in collective motion." Biochemical and Biophysical Research Communications 563 (July 2021): 73–78. http://dx.doi.org/10.1016/j.bbrc.2021.05.037.

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50

Simons, Julie, and Alexandra Rosenberger. "Flagellar Cooperativity and Collective Motion in Sperm." Fluids 6, no. 10 (October 8, 2021): 353. http://dx.doi.org/10.3390/fluids6100353.

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Sperm have thin structures known as flagella whose motion must be regulated in order to reach the egg for fertilization. Large numbers of sperm are typically needed in this process and some species have sperm that exhibit collective or aggregate motion when swimming in groups. The purpose of this study is to model planar motion of flagella in groups to explore how collective motion may arise in three-dimensional fluid environments. We use the method of regularized Stokeslets and a three-dimensional preferred curvature model to simulate groups of undulating flagella, where flagellar waveforms are modulated via hydrodynamic coupling with other flagella and surfaces. We find that collective motion of free-swimming flagella is an unstable phenomenon in long-term simulations unless there is an external mechanism to keep flagella near each other. However, there is evidence that collective swimming can result in significant gains in velocity and efficiency. With the addition of an ability for sperm to attach and swim together as a group, velocities and efficiencies can be increased even further, which may indicate why some species have evolved mechanisms that enable collective swimming and cooperative behavior in sperm.

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Journal articles on the topic 'Collective motion'

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