Rheology of fractal networks

We model the cytoskeleton as a fractal network by identifying each segment with a simple Kelvin-Voigt element, with a well defined equilibrium length. The final structure retains the elastic characteristics of a solid or a gel, which may support stress, without relaxing. By considering a very simple regular self-similar structure of segments in series and in parallel, in 1, 2 or 3 dimensions, we are able to express the viscoelasticity of the network as an effective generalised Kelvin-Voigt model with a power law spectrum of retardation times, $\cal L\sim\tau^{\alpha}$. We relate the parameter $\alpha$ with the fractal dimension of the gel. In some regimes ($0<\alpha<1$), we recover the weak power law behaviours of the elastic and viscous moduli with the angular frequencies, $G'\sim G''\sim w^\alpha$, that occur in a variety of soft materials, including living cells. In other regimes, we find different power laws for $G'$ and $G''$.Comment: 5 pages, 3 figure

Rheo-NMR velocimetry characterisation of PBLG/m-cresol

NMR spectroscopy was used to characterise the velocity profile in nematic solutions of poly(gamma-benzyl-L-glutamate) (PBLG) in m-cresol during the application of a simple shear-flow at constant shear-rate values. In this study, the rheo-NMR technique was used that allows an insight over the fluid dynamics during the application of a shear flow in the presence of an external magnetic field, in a perpendicular direction to the shear gradient. Using this Magnetic Resonance Imaging (MRI) based technique, the velocity profile developed inside the system is accessed.info:eu-repo/semantics/publishedVersio

Reversible Photorheology in Solutions of Cetyltrimethylammonium Bromide, Salicylic Acid, and trans-2,4,4 '-Trihydroxychalcone

We show photorheology in aqueous solutions of weakly entangled wormlike micelles prepared with cetyltrimethylammonium bromide (CTAB), salicylic acid (HSal), and dilute amounts of the photochromic multistate compound trans-2,4,4'-trihydroxychalcone (Ct). Different chemical species of Ct are associated with different colorations and propensities to reside within or outside CTAB micelles. A light-induced transfer between the intra- and intermicellar space is used to alter the mean length of wormlike micelles and hence the rheological properties of the fluid, studied in steady-state shear Bow and in dynamic rheological measurements. Light-induced changes of fluid rheology are reversible by a the relaxation process. at relaxation rates which depend on pH and which are consistent with photochromic reversion rates measured by UV-vis absorption spectroscopy. Parameterizing viscoelostic rheological states by their effective relaxation time tau(c) and corresponding response modulus G(c), we find the light and dark states of the system to fall onto a characteristic state curve defined by comparable experiments conducted without photosensitive components. These reference experiments were prepared with the same concentration of CTAB, but different concentrations of HSal or sodium salicylote (NaSal), and tested at different temperatures

Real-time rheology of actively growing bacteria

The population growth of a Staphylococcus aureus culture, an active colloidal system of spherical cells, was followed by rheological measurements, under steady-state and oscillatory shear flows. We observed a rich viscoelastic behavior as a consequence of the bacteria activity, namely, of their multiplication and density-dependent aggregation properties. In the early stages of growth (lag and exponential phases), the viscosity increases by about a factor of 20, presenting several drops and full recoveries. This allows us to evoke the existence of a percolation phenomenon. Remarkably, as the bacteria reach their late phase of development, in which the population stabilizes, the viscosity returns close to its initial value. Most probably, this is caused by a change in the bacteria physiological activity and in particular, by the decrease of their adhesion properties. The viscous and elastic moduli exhibit power-law behaviors compatible with the "soft glassy materials" model, whose exponents are dependent on the bacteria growth stage. DOI: 10.1103/PhysRevE.87.030701

Rheology of living cells

The mechanical behavior of living cells, during planktonic growth, has been thoroughly explored combining common biological techniques with rheology and rheo-imaging measurements. Under a shear flow, bacterial cultures of Staphylococcus aureus revealed a complex and rich rheological behavior not usually accessed in biological studies. In particular, in stationary shear flow, the viscosity increased during the exponential phase and returned close to its initial value at the late phase of growth, accompanied by the stabilization of the bacterial population. In oscillatory flow, the elastic and viscous moduli exhibited power-law behaviors whose exponents are dependent on the bacteria growth stage, and can be associated to a Soft Glassy Material behavior. These behaviors were framed in a microscopic model that suggests the formation of a dynamic web-like structure, where specific aggregation phenomena may occur, depending on growth stage and cell density. Furthermore, systematic measurements combining optical density and dry weight techniques presented new evidences, which confirmed that the observed cell aggregation patterns developed during growth, under shear, can not only be cell density dependent.info:eu-repo/semantics/publishedVersio

Rotational tumbling of escherichia coli aggregates under shear

Growing living cultures of Escherichia coli bacteria are investigated using real-time in situ rheology and rheoimaging measurements. In the early stages of growth (lag phase) and when subjected to a constant stationary shear, the viscosity slowly increases with the cell's population. As the bacteria reach the exponential phase of growth, the viscosity increases rapidly, with sudden and temporary abrupt decreases and recoveries. At a certain stage, corresponding grossly to the late phase of growth, when the population stabilizes, the viscosity also keeps its maximum constant value, with drops and recoveries, for a long period of time. This complex rheological behavior, which is observed to be shear strain dependent, is a consequence of two coupled effects: the cell density continuous increase and its changing interacting properties. Particular attention is given to the late phase of growth of E. coli populations under shear. Rheoimaging measurements reveal, near the static plate, a rotational motion of E. coli aggregates, collectively tumbling and flowing in the shear direction. This behavior is interpreted in the light of a simple theoretical approach based on simple rigid body mechanics.info:eu-repo/semantics/publishedVersio

BIOFRAG: A new database for analysing BIOdiversity responses to forest FRAGmentation

Habitat fragmentation studies are producing inconsistent and complex results across which it is nearly impossible to synthesise. Consistent analytical techniques can be applied to primary datasets, if stored in a flexible database that allows simple data retrieval for subsequent analyses. Method: We developed a relational database linking data collected in the field to taxonomic nomenclature, spatial and temporal plot attributes and further environmental variables (e.g. information on biogeographic region. Typical field assessments include measures of biological variables (e.g. presence, abundance, ground cover) of one species or a set of species linked to a set of plots in fragments of a forested landscape. Conclusion: The database currently holds records of 5792 unique species sampled in 52 landscapes in six of eight biogeographic regions: mammals 173, birds 1101, herpetofauna 284, insects 2317, other arthropods: 48, plants 1804, snails 65. Most species are found in one or two landscapes, but some are found in four. Using the huge amount of primary data on biodiversity response to fragmentation becomes increasingly important as anthropogenic pressures from high population growth and land demands are increasing. This database can be queried to extract data for subsequent analyses of the biological response to forest fragmentation with new metrics that can integrate across the components of fragmented landscapes. Meta-analyses of findings based on consistent methods and metrics will be able to generalise over studies allowing inter-comparisons for unified answers. The database can thus help researchers in providing findings for analyses of trade-offs between land use benefits and impacts on biodiversity and to track performance of management for biodiversity conservation in human-modified landscapes.Fil: Pfeifer, Marion. Imperial College London; Reino UnidoFil: Lefebvre, Veronique. Imperial College London; Reino UnidoFil: Gardner, Toby A.. Stockholm Environment Institute; SueciaFil: Arroyo Rodríguez, Víctor. Universidad Nacional Autónoma de México; MéxicoFil: Baeten, Lander. University of Ghent; BélgicaFil: Banks Leite, Cristina. Imperial College London; Reino UnidoFil: Barlow, Jos. Lancaster University; Reino UnidoFil: Betts, Matthew G.. State University of Oregon; Estados UnidosFil: Brunet, Joerg. Swedish University of Agricultural Sciences; SueciaFil: Cerezo Blandón, Alexis Mauricio. Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; ArgentinaFil: Cisneros, Laura M.. University of Connecticut; Estados UnidosFil: Collard, Stuart. Nature Conservation Society of South Australia; AustraliaFil: D´Cruze, Neil. The World Society for the Protection of Animals; Reino UnidoFil: Da Silva Motta, Catarina. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Duguay, Stephanie. Carleton University; CanadáFil: Eggermont, Hilde. University of Ghent; BélgicaFil: Eigenbrod, Félix. University of Southampton; Reino UnidoFil: Hadley, Adam S.. State University of Oregon; Estados UnidosFil: Hanson, Thor R.. No especifíca;Fil: Hawes, Joseph E.. University of East Anglia; Reino UnidoFil: Heartsill Scalley, Tamara. United State Department of Agriculture. Forestry Service; Puerto RicoFil: Klingbeil, Brian T.. University of Connecticut; Estados UnidosFil: Kolb, Annette. Universitat Bremen; AlemaniaFil: Kormann, Urs. Universität Göttingen; AlemaniaFil: Kumar, Sunil. State University of Colorado - Fort Collins; Estados UnidosFil: Lachat, Thibault. Swiss Federal Institute for Forest; SuizaFil: Lakeman Fraser, Poppy. Imperial College London; Reino UnidoFil: Lantschner, María Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Norte. Estación Experimental Agropecuaria San Carlos de Bariloche; ArgentinaFil: Laurance, William F.. James Cook University; AustraliaFil: Leal, Inara R.. Universidade Federal de Pernambuco; BrasilFil: Lens, Luc. University of Ghent; BélgicaFil: Marsh, Charles J.. University of Leeds; Reino UnidoFil: Medina Rangel, Guido F.. Universidad Nacional de Colombia; ColombiaFil: Melles, Stephanie. University of Toronto; CanadáFil: Mezger, Dirk. Field Museum of Natural History; Estados UnidosFil: Oldekop, Johan A.. University of Sheffield; Reino UnidoFil: Overal , Williams L.. Museu Paraense Emílio Goeldi. Departamento de Entomologia; BrasilFil: Owen, Charlotte. Imperial College London; Reino UnidoFil: Peres, Carlos A.. University of East Anglia; Reino UnidoFil: Phalan, Ben. University of Southampton; Reino UnidoFil: Pidgeon, Anna Michle. University of Wisconsin; Estados UnidosFil: Pilia, Oriana. Imperial College London; Reino UnidoFil: Possingham, Hugh P.. Imperial College London; Reino Unido. The University Of Queensland; AustraliaFil: Possingham, Max L.. No especifíca;Fil: Raheem, Dinarzarde C.. Royal Belgian Institute of Natural Sciences; Bélgica. Natural History Museum; Reino UnidoFil: Ribeiro, Danilo B.. Universidade Federal do Mato Grosso do Sul; BrasilFil: Ribeiro Neto, Jose D.. Universidade Federal de Pernambuco; BrasilFil: Robinson, Douglas W.. State University of Oregon; Estados UnidosFil: Robinson, Richard. Manjimup Research Centre; AustraliaFil: Rytwinski, Trina. Carleton University; CanadáFil: Scherber, Christoph. Universität Göttingen; AlemaniaFil: Slade, Eleanor M.. University of Oxford; Reino UnidoFil: Somarriba, Eduardo. Centro Agronómico Tropical de Investigación y Enseñanza; Costa RicaFil: Stouffer, Philip C.. State University of Louisiana; Estados UnidosFil: Struebig, Matthew J.. University of Kent; Reino UnidoFil: Tylianakis, Jason M.. University College London; Estados Unidos. Imperial College London; Reino UnidoFil: Teja, Tscharntke. Universität Göttingen; AlemaniaFil: Tyre, Andrew J.. Universidad de Nebraska - Lincoln; Estados UnidosFil: Urbina Cardona, Jose N.. Pontificia Universidad Javeriana; ColombiaFil: Vasconcelos, Heraldo L.. Universidade Federal de Uberlandia; BrasilFil: Wearn, Oliver. Imperial College London; Reino Unido. The Zoological Society of London; Reino UnidoFil: Wells, Konstans. University of Adelaide; AustraliaFil: Willig, Michael R.. University of Connecticut; Estados UnidosFil: Wood, Eric. University of Wisconsin; Estados UnidosFil: Young, Richard P.. Durrell Wildlife Conservation Trust; Reino UnidoFil: Bradley, Andrew V.. Imperial College London; Reino UnidoFil: Ewers, Robert M.. Imperial College London; Reino Unid

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio