scholarly journals Stress-induced collective behavior leads to the formation of multicellular structures and the survival of the unicellular alga Chlamydomonas

2021 ◽  
Author(s):  
Felix de Carpentier ◽  
Alexandre Maes ◽  
Christophe H Marchand ◽  
Celine Chung ◽  
Cyrielle Durand ◽  
...  
Keyword(s):  
Collective Behavior ◽  
Large Scale ◽  
Abiotic Stress Response ◽  
Collective Behaviors ◽  
Defense Systems ◽  
Toxic Environment ◽  
Response To Stress ◽  
Unicellular Organisms ◽  
Living Organisms ◽  
Multicellular Organisms

Depending on their nature, living organisms use various strategies to adapt to environmental stress conditions. Multicellular organisms implement a set of reactions involving signaling and cooperation between different types of cells. Unicellular organisms on the other hand must activate defense systems, which involve collective behaviors between individual organisms. In the unicellular model alga Chlamydomonas reinhardtii, the existence and the function of collective behavior mechanisms in response to stress remain largely unknown. Here we report the discovery of a mechanism of abiotic stress response that Chlamydomonas can trigger to form large multicellular structures that can comprise several thousand cells. We show that these aggregates constitute an effective bulwark within which the cells are efficiently protected from the toxic environment. We have generated the first family of mutants that aggregate spontaneously, the socializer mutants (saz), of which we describe here in detail saz1. We took advantage of the saz mutants to implement a large scale multiomics approach that allowed us to show that aggregation is not the result of passive agglutination, but rather genetic reprogramming and substantial modification of the secretome. The reverse genetic analysis we conducted on some of the most promising candidates allowed us to identify the first positive and negative regulators of aggregation and to make hypotheses on how this process is controlled in Chlamydomonas.

scholarly journals Continuous Multiagent Control Using Collective Behavior Entropy for Large-Scale Home Energy Management

2020 ◽  
Vol 34 (01) ◽  
pp. 922-929 ◽  
Author(s):  
Jianwen Sun ◽  
Yan Zheng ◽  
Jianye Hao ◽  
Zhaopeng Meng ◽  
Yang Liu
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Collective Behavior ◽  
Large Scale ◽  
Scale Up ◽  
Group Behavior ◽  
Power Cost ◽  
Collective Behaviors ◽  
Peak Loads ◽  
Home Energy Management

With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent for energy savings and peak loads reduction. Traditional DSM works focusing on optimizing the energy activities for a single household can not scale up to large-scale home energy management problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential way to solve the problem of scalability, where modern homes interact together to reduce energy consumers consumption while striking a balance between energy cost and peak loads reduction. However, it is difficult to solve such an environment with the non-stationarity, and existing MA-DRL approaches cannot effectively give incentives for expected group behavior. In this paper, we propose a collective MA-DRL algorithm with continuous action space to provide fine-grained control on a large scale microgrid. To mitigate the non-stationarity of the microgrid environment, a novel predictive model is proposed to measure the collective market behavior. Besides, a collective behavior entropy is introduced to reduce the high peak loads incurred by the collective behaviors of all householders in the smart grid. Empirical results show that our approach significantly outperforms the state-of-the-art methods regarding power cost reduction and daily peak loads optimization.

Freezing

2017 ◽  
Author(s):  
Andrew Clarke
Keyword(s):  
Thermal Hysteresis ◽  
Higher Plants ◽  
Terrestrial Environment ◽  
Cold Temperatures ◽  
Cellular Dehydration ◽  
A Cell ◽  
Unicellular Organisms ◽  
Cell Cytosol ◽  
Freezing Temperatures ◽  
Multicellular Organisms

Freezing is a widespread ecological challenge, affecting organisms in over half the terrestrial environment as well as both polar seas. With very few exceptions, if a cell freezes internally, it dies. Polar teleost fish in shallow waters avoid freezing by synthesising a range of protein or glycoprotein antifreezes. Terrestrial organisms are faced with a far greater thermal challenge, and exhibit a more complex array of responses. Unicellular organisms survive freezing temperatures by preventing ice nucleating within the cytosol, and tolerating the cellular dehydration and membrane disruption that follows from ice forming in the external environment. Multicellular organisms survive freezing temperatures by manipulating the composition of the extracellular body fluids. Terrestrial organisms may freeze at high subzero temperatures, often promoted by ice nucleating proteins, and small molecular mass cryoprotectants (often sugars and polyols) moderate the osmotic stress on cells. A range of chaperone proteins (dehydrins, LEA proteins) help maintain the integrity of membranes and macromolecules. Thermal hysteresis (antifreeze) proteins prevent damaging recrystallisation of ice. In some cases arthropods and higher plants prevent freezing in their extracellular fluids and survive by supercooling. Vitrification of extracellular water, or of the cell cytosol, may be a more widespread response to very cold temperatures than recognised to date.

scholarly journals Social Collective Attack Model and Procedures for Large-Scale Cyber-Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 991
Author(s):  
Peidong Zhu ◽  
Peng Xun ◽  
Yifan Hu ◽  
Yinqiao Xiong
Keyword(s):  
Smart Grid ◽  
Collective Behavior ◽  
Physical System ◽  
Large Scale ◽  
Public Utility ◽  
Modeling Framework ◽  
Vast Number ◽  
Attack Model ◽  
The Social ◽  
Social Collective

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.

scholarly journals Cell migration guided by long-lived spatial memory

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joseph d’Alessandro ◽  
Alex Barbier--Chebbah ◽  
Victor Cellerin ◽  
Olivier Benichou ◽  
René Marc Mège ◽  
...  
Keyword(s):  
Cell Migration ◽  
Spatial Memory ◽  
Large Scale ◽  
Single Cells ◽  
Environmental Perturbations ◽  
Motile Cells ◽  
Unicellular Organisms ◽  
Biochemical Signals ◽  
The Impact ◽  
Cell Trajectories

AbstractLiving cells actively migrate in their environment to perform key biological functions—from unicellular organisms looking for food to single cells such as fibroblasts, leukocytes or cancer cells that can shape, patrol or invade tissues. Cell migration results from complex intracellular processes that enable cell self-propulsion, and has been shown to also integrate various chemical or physical extracellular signals. While it is established that cells can modify their environment by depositing biochemical signals or mechanically remodelling the extracellular matrix, the impact of such self-induced environmental perturbations on cell trajectories at various scales remains unexplored. Here, we show that cells can retrieve their path: by confining motile cells on 1D and 2D micropatterned surfaces, we demonstrate that they leave long-lived physicochemical footprints along their way, which determine their future path. On this basis, we argue that cell trajectories belong to the general class of self-interacting random walks, and show that self-interactions can rule large scale exploration by inducing long-lived ageing, subdiffusion and anomalous first-passage statistics. Altogether, our joint experimental and theoretical approach points to a generic coupling between motile cells and their environment, which endows cells with a spatial memory of their path and can dramatically change their space exploration.

scholarly journals Amoebic Foraging Model of Metastatic Cancer Cells

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1140
Author(s):  
Daiki Andoh ◽  
Yukio-Pegio Gunji
Keyword(s):  
Bayesian Inference ◽  
Cancer Cells ◽  
Metastatic Cancer ◽  
Gait Patterns ◽  
Lévy Walk ◽  
Levy Walk ◽  
Unicellular Organisms ◽  
Living Organisms ◽  
Walk Algorithm ◽  
Metastatic Cancer Cells

The Lévy walk is a pattern that is often seen in the movement of living organisms; it has both ballistic and random features and is a behavior that has been recognized in various animals and unicellular organisms, such as amoebae, in recent years. We proposed an amoeba locomotion model that implements Bayesian and inverse Bayesian inference as a Lévy walk algorithm that balances exploration and exploitation, and through a comparison with general random walks, we confirmed its effectiveness. While Bayesian inference is expressed only by P(h) = P(h|d), we introduce inverse Bayesian inference expressed as P(d|h) = P(d) in a symmetry fashion. That symmetry contributes to balancing contracting and expanding the probability space. Additionally, the conditions of various environments were set, and experimental results were obtained that corresponded to changes in gait patterns with respect to changes in the conditions of actual metastatic cancer cells.

scholarly journals Genome-wide Analysis of Alternative Pre-mRNA Splicing

2007 ◽  
Vol 283 (3) ◽  
pp. 1229-1233 ◽  
Author(s):  
Claudia Ben-Dov ◽  
Britta Hartmann ◽  
Josefin Lundgren ◽  
Juan Valcárcel
Keyword(s):  
Alternative Splicing ◽  
Large Scale ◽  
Mrna Splicing ◽  
Diagnostic Tools ◽  
Primary Transcript ◽  
Genome Wide ◽  
Human Genes ◽  
Multicellular Organisms ◽  
Eukaryotic Genomes ◽  
Key Questions

Alternative splicing of mRNA precursors allows the synthesis of multiple mRNAs from a single primary transcript, significantly expanding the information content and regulatory possibilities of higher eukaryotic genomes. High-throughput enabling technologies, particularly large-scale sequencing and splicing-sensitive microarrays, are providing unprecedented opportunities to address key questions in this field. The picture emerging from these pioneering studies is that alternative splicing affects most human genes and a significant fraction of the genes in other multicellular organisms, with the potential to greatly influence the evolution of complex genomes. A combinatorial code of regulatory signals and factors can deploy physiologically coherent programs of alternative splicing that are distinct from those regulated at other steps of gene expression. Pre-mRNA splicing and its regulation play important roles in human pathologies, and genome-wide analyses in this area are paving the way for improved diagnostic tools and for the identification of novel and more specific pharmaceutical targets.

scholarly journals Developing a vocabulary and ontology for modeling insect natural history data: example data, use cases, and competency questions

2019 ◽  
Vol 7 ◽  
Author(s):  
Brian Stucky ◽  
James Balhoff ◽  
Narayani Barve ◽  
Vijay Barve ◽  
Laura Brenskelle ◽  
...  
Keyword(s):  
Natural History ◽  
Large Scale ◽  
Use Cases ◽  
Data Systems ◽  
Multiple Sources ◽  
History Data ◽  
Insect Ecology ◽  
Many Sources ◽  
Multicellular Organisms ◽  
Initial Results

Insects are possibly the most taxonomically and ecologically diverse class of multicellular organisms on Earth. Consequently, they provide nearly unlimited opportunities to develop and test ecological and evolutionary hypotheses. Currently, however, large-scale studies of insect ecology, behavior, and trait evolution are impeded by the difficulty in obtaining and analyzing data derived from natural history observations of insects. These data are typically highly heterogeneous and widely scattered among many sources, which makes developing robust information systems to aggregate and disseminate them a significant challenge. As a step towards this goal, we report initial results of a new effort to develop a standardized vocabulary and ontology for insect natural history data. In particular, we describe a new database of representative insect natural history data derived from multiple sources (but focused on data from specimens in biological collections), an analysis of the abstract conceptual areas required for a comprehensive ontology of insect natural history data, and a database of use cases and competency questions to guide the development of data systems for insect natural history data. We also discuss data modeling and technology-related challenges that must be overcome to implement robust integration of insect natural history data.

scholarly journals Shell model description of heavy nuclei and abnormal collective motions

2018 ◽  
Vol 178 ◽  
pp. 02015
Author(s):  
Chong Qi
Keyword(s):  
Shell Model ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Model Description ◽  
Heavy Nuclei ◽  
Collective Models ◽  
Collective Behaviors ◽  
Electromagnetic Transition ◽  
Nuclear Shell ◽  
Model Approach

In this contribution I present systematic calculations on the spectroscopy and electromagnetic transition properties of intermediate-mass and heavy nuclei around 100Sn and 208Pb. We employed the large-scale configuration interaction shell model approach with realistic interactions. Those nuclei are the longest isotopic chains that can be studied by the nuclear shell model. I will show that the yrast spectra of Te isotopes show a vibrational-like equally spaced pattern but the few known E2 transitions show rotational-like behaviour. These kinds of abnormal collective behaviors cannot be reproduced by standard collective models and provide excellent background to study the competition of single-particle and various collective degrees of freedom. Moreover, the calculated B(E2) values for neutron-deficient and heavier Te isotopes show contrasting different behaviours along the yrast line, which may be related to the enhanced neutron-proton correlation when approaching N=50. The deviations between theory and experiment concerning the energies and E2 transition properties of low-lying 0+ and 2+ excited states and isomeric states in those nuclei may provide a constraint on our understanding of nuclear deformation and intruder configuration in that region.

scholarly journals Spatial confinement of active microtubule networks induces large-scale rotational cytoplasmic flow

2017 ◽  
Vol 114 (11) ◽  
pp. 2922-2927 ◽  
Author(s):  
Kazuya Suzuki ◽  
Makito Miyazaki ◽  
Jun Takagi ◽  
Takeshi Itabashi ◽  
Shin’ichi Ishiwata
Keyword(s):  
Fluid Flow ◽  
Cytoplasmic Streaming ◽  
Large Scale ◽  
Random Network ◽  
Hydrodynamic Interactions ◽  
Length Scale ◽  
Vortex Flows ◽  
Directed Flow ◽  
Collective Behaviors ◽  
In Cells

Collective behaviors of motile units through hydrodynamic interactions induce directed fluid flow on a larger length scale than individual units. In cells, active cytoskeletal systems composed of polar filaments and molecular motors drive fluid flow, a process known as cytoplasmic streaming. The motor-driven elongation of microtubule bundles generates turbulent-like flow in purified systems; however, it remains unclear whether and how microtubule bundles induce large-scale directed flow like the cytoplasmic streaming observed in cells. Here, we adopted Xenopus egg extracts as a model system of the cytoplasm and found that microtubule bundle elongation induces directed flow for which the length scale and timescale depend on the existence of geometrical constraints. At the lower activity of dynein, kinesins bundle and slide microtubules, organizing extensile microtubule bundles. In bulk extracts, the extensile bundles connected with each other and formed a random network, and vortex flows with a length scale comparable to the bundle length continually emerged and persisted for 1 min at multiple places. When the extracts were encapsulated in droplets, the extensile bundles pushed the droplet boundary. This pushing force initiated symmetry breaking of the randomly oriented bundle network, leading to bundles aligning into a rotating vortex structure. This vortex induced rotational cytoplasmic flows on the length scale and timescale that were 10- to 100-fold longer than the vortex flows emerging in bulk extracts. Our results suggest that microtubule systems use not only hydrodynamic interactions but also mechanical interactions to induce large-scale temporally stable cytoplasmic flow.

scholarly journals A Multi-Scale Modelling of Aggregation of TiO2 Nanoparticle Suspensions in Water

2021 ◽  
Author(s):  
Giulia Mancardi ◽  
Matteo Alberghini ◽  
Neus Aguilera-Porta ◽  
Monica Calatayud ◽  
Pietro Asinari ◽  
...  
Keyword(s):  
Titanium Dioxide ◽  
Density Functional ◽  
Large Scale ◽  
Molecular Descriptors ◽  
Titanium Dioxide Nanoparticles ◽  
Accurate Determination ◽  
Multi Scale ◽  
Spherical Bead ◽  
Living Organisms ◽  
Dynamics Simulations

Titanium dioxide nanoparticles have risen concerns about their possible toxicity and the European Food Safety Authority recently banned the use of TiO2 nano-additive in food products. Following the intent of relating nanomaterials atomic structure with their toxicity without having to conduct large scale experiments on living organisms, we investigate the aggregation of titanium dioxide nanoparticles using a multi-scale technique: starting from ab initio Density Functional Theory to get an accurate determination of the energetics and electronic structure, we switch to classical Molecular Dynamics simulations to calculate the Potential of Mean Force for the connection of two identical nanoparticles in water; the fitting of the latter by a set of mathematical equations is the key for the upscale. Lastly, we perform Brownian Dynamics simulations where each nanoparticle is a spherical bead. This coarsening strategy allows studying the aggregation of a few thousand nanoparticles. Applying this novel procedure, we find three new molecular descriptors, namely, the aggregation free energy and two numerical parameters used to correct the observed deviation from the aggregation kinetic described by the Smoluchowski theory. Molecular descriptors can be fed into QSAR models to predict the toxicity of a material knowing its physicochemical properties, without having to conduct large scale experiments on living organisms.

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