C O N T E N T S:

- Osmosis is the diffusion of water molecules from a place with a high water potential Finding the Water Potential of Potato Cells :: Biology Lab Report that had little net movement of water, this water potential is -900kPa (ref- teacher support: coursework guidance OCR- graph).(More…)

- GCSE Science/Osmosis in potato slices coursework Osmosis is a type of diffusion involving water molecules and a semi-permeable membrane.(More…)
- This lesson can be used as a reinforcement/inquiry activity during the cell transport unit, as a follow up to information on passive transport (diffusion, osmosis).(More…)

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description: Passive transport – Wikipedia

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**[1] Gcse Science Coursework Osmosis spud-ng.com BBC Bitesize GCSE Biology Movement across cell membranes Revise how substances can move into and out of cells through diffusion, osmosis and active transport. [1] Cell Biology Bulldogbiology.comExplain the role of cell membranes as a highly selective barrier (diffusion, osmosis, facilitated diffusion, active transport). [1] Biology 1 The purpose of this lab is to observe the acts of passive transport: diffusion and osmosis in a model membrane Sample Lab Report Diffusion and Osmosis Learning Ace Sample Lab Report Diffusion and Osmosis. [1] Diffusion results because of AP Biology Osmosis and Diffusion Lab Report Scribd Patrick McCrystal Diffusion Lab Report Diffusion and Osmosis : Migrant Molecules This lab experiment exemplified two. different types of passive transport: diffusion osmosis lab example 2 Biology Junction Lab 1: Osmosis & Diffusion. [1]**

*Osmosis is the diffusion of water molecules from a place with a high water potential Finding the Water Potential of Potato Cells :: Biology Lab Report that had little net movement of water, this water potential is -900kPa (ref- teacher support: coursework guidance OCR- graph).*In osmosis water moves through a selectively osmosis lab example 2 Biology Junction Lab 1: Osmosis & Diffusion. [1] Obtain five, Lab 1 Osmosis & Diffusion Biology Junction Lab 1 Osmosis & Diffusion : Diffusion or osmosis occurs until dynamic equilibrium has been reached. [1] Diffusion lab with sandwich bag The Biology Corner Define osmosis Use arrows to illustrate how diffusion occurred in this lab. 6. [1] Soil and Water Conservation Society Soil and Water Conservation Society (SWCS) is a nonprofit scientific and educational organization founded in 1943 that serves as an advocate for conservation Pearson The Biology Place Prentice Hall Bridge pageLabBench Activity Diffusion and Osmosis. by Theresa Knapp Holtzclaw. [1] KEY TOPICS Diffusion and osmosis are two topics you’ll need to know in and out to be prepared for the AP Biology exam. [1] With our service, you can find the most popular phrases for keyword “Equilibrium Biology Diffusion”. [1] The higher the solute in solvent, then there PDF Osmosis and Diffusion Lab using Potato Cores Wikispaces Biology:( Osmosis and Diffusion Lab using Potato Cores (Class:( 3B Mr.( Boyer(Name:( Simon Han(Abstract:) In this experiment,! we learnt about Osmosis and Diffusion Biology Osmosis Lab Report Essay Sample blablawriting.com Is this the perfect essay for you? Save time and order Biology Osmosis Lab Report essay editing for only $12.9 per page. [1] Water Austin Stults September 29, 2011 Ms. Kellogg/AP Biology Osmosis / Diffusion and Water Potential Lab PART 1: Diffusion Introduction: The movement of particles into and Osmosis Lab Report Jane’s AP Bio Webpage Google Sites Osmosis Lab Report. which was osmosis (the diffusion of water). [1]

GCSE Biology Osmosis Coursework Potato and Osmosis Investigation Introduction into Osmosis Osmosis is a unique type of diffusion. [1]

**POSSIBLY USEFUL **

**[1] Introduction: Kinetic energy, a source of energy stored in cells, causes molecules to bump into each other and move in new directions.New GCSE BBC Bitesize Diffusion and Osmosis YouTube 19.05.2012 Video embedded This video describes how diffusion works Osmosis and osmotic pressure Chem1Semipermeable membranes and osmotic flow. [1] Osmosis Coursework Help toponlinepaperessay.life Osmosis Coursework Help osmosis coursework help Osmosis is a type of diffusion involving water molecules and a semi-permeable membrane. [1] Osmosis definition, the tendency of a fluid, usually water, to pass through a semipermeable membrane into a solution where the solvent concentration is higher, thus.Osmosis is the diffusion of water across a selectively permeable membrane (Bell et al 2004). [1] Osmosis is a specific type of diffusion; it is the passage of water from a region of high water concentration through a semi-permeable membrane to a region of low water concentration. [1] Virtual Diffusion Lab Diffusion Osmosis Lab Report Google Docs This lab, title Diffusion and Osmosis, was centered around the diffusion across a cellular membrane and how exactly materials move and diffuse in concentrations. [1] He then describes the diffusion demonstration and how Diffusion & Osmosis Lab Review Osmosis Lab Report Essay 491 Words StudyMode Osmosis Lab Report Hypothesis: Osmosis will occur when there is an uneven distribution of solute in a solvent. [1] Lab 4: Diffusion and Osmosis Pierce College Putman’s Biol 160 Lab 4: Diffusion and Osmosis 4.1 Lab 4: Diffusion and Osmosis Introduction behavior in your lab report ! Osmosis 1. [1] Lab 1 Osmosis & Diffusion Sample 1 Sample 2 Sample 3 Sample 4 AP Lab 1: Osmosis and Diffusion Lab Report Google Sites Allysha’s e-Portfolio. [1] Top grades and quality guaranteed! Diffusion Osmosis Lab Report Google Docs Diffusion Osmosis Lab Report Share. [1] Concept 1: Diffusion ; Concept 2: Osmosis ; Closer Look: Osmosis ; Diffusion and Osmosis. by Theresa Knapp Holtzclaw teacherqualitygrant.pds-hrd.wikispaces.net Lab Report on Osmosis and Diffusion. [1] Sample Lab Report Diffusion and Osmosis. 55 Lab Topic 3 Diffusion and Osmosis Laboratory Objectives After completing this lab topic, you should be able to: 1. [1] Topics and Samples Online Osmosis Lab Report Abstract: Osmosis and diffusion are the very essential at both the organ and cellular levels. [1] Purpose: The purpose of this lab is to AP Lab 1: Osmosis and Diffusion Lab Report Allysha’s e Allysha’s e-Portfolio. [1] Osmosis obviously occurred because there was a change in mass for both the dialysis tubing filled with the unknown solution and the beaker DOCX teacherqualitygrant.pds-hrd.wikispaces.net Lab Report on Osmosis and Diffusion. [1]**

*GCSE Science/Osmosis in potato slices coursework Osmosis is a type of diffusion involving water molecules and a semi-permeable membrane.*Osmosis is a specialized case of diffusion that involves the passive transport of water. [1] To characterize the in vivo function of the NPC as a barrier to passive diffusion of macromolecules, we enhanced our FRAP ( Reits and Neefjes, 2001 ) assay of transport rates of GFP fusion proteins ( Timney et al., 2006 ; Lord et al., 2015 ), making it substantially more automated and quantitative by integrating several cell biological measurements in a unified pipeline ( Fig. 1 B ). [2]

Thermodynamics and kinetics analysis of simulated passive diffusion. (A) Coarse-grained model of the passive diffusion through the NPC, with the NPC modeled as a cylindrical scaffold, with flexible FG repeats anchored to its walls. (B) Simulated and experimental exponential time constants of transport 95% confidence intervals, plotted as a function of molecular mass, with solid lines showing fits to power functions. [2] [xyz-ihs snippet=”Amazon-Affiliate-Native-Ads”] Video 2 shows sample simulation trajectory of passive diffusion of macromolecules of different molecular masses/radii through the NPC, using the model described in the text and in Fig. 4 and Fig. S2. [2] Our experimental measurements of passive diffusion in mutant strains are remarkably consistent with our simulation results, showing that the deletion of FG domains may increase the overall leakiness of a given strain, but it affects substrates of different sizes in a similar way, thereby maintaining the relative size selectivity of the NPC ( Fig. 6 B ). [2] Therefore, passive diffusion is expected to be sensitive to substrate geometry and plummet at a well-defined size threshold as the substrate reaches the characteristic pore size, which is inconsistent with our data ( Fig. 3, E and F ). [2] Our results clearly support the model of a soft barrier to passive diffusion ( Fig. 1 A ) and refute the prevailing paradigm of a rather firm barrier at any well-defined size threshold, including the oft-cited 30-60-kD threshold ( Keminer and Peters, 1999 ; Mohr et al., 2009 ; Ma et al., 2012 ; Christie et al., 2016 ; Knockenhauer and Schwartz, 2016 ; Musser and Grwald, 2016 ; Schmidt and Glich, 2016 ). [2] The prevailing functional model of passive transport, which we term the “rigid barrier” model, is that of a barrier with a firm size threshold of 40 kD for passive diffusion ( Christie et al., 2016 ; Knockenhauer and Schwartz, 2016 ; Musser and Grwald, 2016 ; Schmidt and Glich, 2016 ). [2]

The labeled GFP-xPrA substrates are appropriate model substrates for passive diffusion in vivo because PrA and GFP are both understood to be functionally inert in yeast, lack nuclear localization and export signals, and do not interact with native yeast proteins ( Gavin et al., 2002 ; Keilhauer et al., 2015 ). [2] Such proteins are retained in just one compartment by either binding to even larger molecular complexes, for which passive diffusion is expected to be negligible even if we assume a soft barrier ( Wr et al., 2015 ) or are actively removed from the other compartment by facilitated diffusion ( Kirli et al., 2015 ). [2] These variables have different effects on small and large macromolecules ( Lowe et al., 2015 ) and are therefore likely to introduce a significant bias for characterizing the size threshold for passive diffusion in vitro ( Keminer and Peters, 1999 ; Mohr et al., 2009 ). [2] Competition with transport receptors or the functional arrangement of different types of FG repeats may contribute to modulating this core function ( Vovk et al., 2016 ; Zahn et al., 2016 ) or contribute to other functions such as facilitated diffusion, but are secondary to this entropic effect in forming the passive barrier itself. [2] The model described in our paper represents a significantly different approach for understanding diffusion in surfaces with complex geometries compared with many other earlier models that typically use random-walk motion and escape times of molecules to determine geometric effects on diffusion ( Yoshigaki, 2007 ; Jiang and Powers, 2008 ; Callan-Jones et al., 2011 ; Holcman and Schuss, 2011 ; Singh et al., 2012 ; Kusters and Storm, 2014 ; Kusters et al., 2014 ). [3] We report numerical simulations of the mathematical model that incorporate the cylindrical nature of tubules and the effect of the cylindrical geometry on diffusion of molecules along the tubule surface. [3] In this paper, we examine how the geometry of a surface plays a role in diffusion of molecules and, consequently, how concentration gradients of diffusing species develop. To address this, we developed a numerical implementation of the Laplace-Beltrami mathematical model to understand diffusion in geometrically complex surfaces using membrane tubules as a biologically relevant example. [3] To address this question, we developed a new finite element approach to model diffusion on curved membrane surfaces based on solutions to Fick?s law of diffusion and used this to study the effects of geometry on the entry of surface-bound particles into tubules by diffusion. [3]

Classical studies on diffusion in cell membranes assume the surface to be planar and model diffusion in two dimensions ( Saffman et al., 1975 ). [3] Understanding how geometry influences diffusion of molecules on membrane surfaces has important cell biological implications. [3] When molecules move ( diffuse ) via special transport proteins found within the cell membrane, it is called facilitated diffusion, otherwise it is only simple diffusion. [4] This effect could potentially explain recent observations that diffusion of membrane proteins is slower in tubules than the surrounding “flat” membranes in vitro ( Domanov et al., 2011 ). [3] Experimentally determined diffusion constants measured for a protein KvAP have been shown to be directly proportional to the radius of the tubule in which the protein diffuses ( Domanov et al., 2011 ; Aimon et al., 2014 ). [3] To assess the validity of our models, we compared our simulations with a recent experimental work by Aimon et al. (2014), who measured diffusion of proteins in membrane tethers connected to a giant unilamellar vesicle using a fluorescence recovery after photobleaching (FRAP) assay. [3] Diffusion of proteins in membranes is also sensitive to a number of factors, including the size of the protein, its confinement to domains, the viscosity of the environment, and crowding ( Petrov and Schwille, 2008 ; Eggeling et al., 2009 ; Kusumi et al., 2010 ; Harb et al., 2012 ; Edwald et al., 2014 ). [3] Earlier experiments have shown that the protein and lipid composition of membrane tubes themselves can be curvature dependent, which could potentially introduce additional constraints on diffusion ( Callan-Jones et al., 2011 ; Aimon et al., 2014 ). [3] Diffusion in membranes has been characterized by a wide variety of theoretical techniques, including some that describe a tubular geometry ( Saffman et al., 1975 ; Berk and Hochmuth, 1992 ; Yoshigaki, 2007 ; Kusters et al., 2013 ). [3] Even just using first principles and varying only relative geometry, we can make several interesting predictions about material flow and concentration gradients arising from diffusion in tubules with relevance to biological processes occurring in membranes. [3] The symmetric tubule diffusion using defined virtual coordinates reduces to the form u t ? k x 0 for specific forms of A ( x ) determined by the tube?s geometry. [3] We show by adapting standard diffusion paradigms that geometry has a nontrivial influence on diffusion and thereby the concentration of molecules that diffuse into tubules. [3] We expect the geometry of the tubule to control the diffusion of molecules along its surface in two ways. [3] We simulated diffusion into the tubes for 80 s and then calculated the average concentration of diffusing species across the tubule surface over time. [3] FIGURE 3: Variation of concentration along the tubule surface under Neumann boundary condition simulations. (A) Evolution of concentration gradients as a function of time for a Neumann simulation of molecules actively flowing into a tube of length 1 ?m and radius 0.1 ?m with a diffusion coefficient of 0.1 ?m 2 /s. [3]

We investigated how diffusion coefficients impact the evolution of concentration gradients across the surface of a tubule of constant length 1 ?m and radius 0.1 ?m for a simulation time of 5 s. [3] We varied diffusion coefficients from 0.01 to 0.5 ?m 2 /s on a tubule of length 1 ?m and radius 0.1 ?m for a total simulation time of 5 s. [3] Next we studied the effect of tubule radius (Supplemental Figure 7A) by simulating molecules diffusing into tubules of a constant length 1 ?m and diffusion coefficient of 0.1 ?m 2 /s for a period of 5 s. [3] We first characterized how a typical Neumann boundary condition evolves by using as an example a molecule with a diffusion coefficient 0.1 ?m 2 /s diffusing onto a tubule of radius 0.1 ?m and length 1 ?m. [3]

While the shortest tubule ( h 0.1 ?m) equilibrated rapidly, as expected, even lengths of 0.5 ?m posed a significant barrier to the diffusion of molecules onto the tubule (Supplemental Figure 4B). [3] We next simulated diffusion in tubules with a constant length of 1 ?m but radii ranging from 0.01 to 0.2 ?m (Supplemental Figure 4D). [3] In the case of long thin tubules, our simulated diffusion profile resembles but is not identical to the experimentally observed diffusion profiles ( Aimon et al., 2014 ), as demonstrated in Figure 5. [3] FIGURE 5: Comparison of simulated diffusion with previously published experimental data ( Aimon et al., 2014 ). [3]

At the other end of the size spectrum, the rather gradual decrease in the energy barrier with molecular mass also explains why specialized NTRs are used even for some small macromolecules that are transported at high quantities, such as the 25-kD Ran ( Smith et al., 1998 ; Ribbeck and Glich, 2001 ), because the energy penalty for passive diffusion scales with the total volume of passive diffusers according to our model. [2] We conclude that the barrier to passive diffusion lacks any fixed molecular mass threshold and can be predicted remarkably well based on molecular mass within the size range from 27 to 230 kD, at least to first approximation. [2]

We will discuss two examples of passive transport in this tutorial: diffusion and osmosis. [1] The processes of diffusion and osmosis account for much of the passive movement … SmartCockpit Airline training guides, Aviation SMARTCOCKPIT ; Our #1 goal, since 2000, is to offer the most extensive online aviation resource to worldwide professional pilots. [1] Throughout this lab, we wanted to study these effects on diffusion and osmosis. [1] Today you’ll finally be conducting the diffusion and osmosis lab you’ve been building for the last few classes. [1] Today we’ll either continue to work on creating the lab, or another activity involving diffusion and osmosis. [1] You and a partner were tasked to create a lab that demonstrated the process of diffusion and the process of osmosis. [1] Our process used the aspect of osmosis and diffusion and provided us with a great lab. [1] Osmosis – The diffusion of a solvent (water) across a selectively permeable membrane. [5] Introduction Osmosis is the diffusion of water across a selectively permeable membrane in living organisms from a hypotonic to a hypertonic solution. [1]

The goal of this tutorial is for you to be able to describe the movement of molecules in the processes of diffusion and osmosis. [1] The maps also revealed “hot spots” within the cell, where molecules exhibited rapid diffusion (short correlation time). [6] From left to right: At time t 0 s, the cytoplasmic pool of reporter macromolecules is photobleached with a spot-bleaching laser (cyan crosshair), and the nuclear fluorescence in a single cell is plotted over time, as the nuclear pool of reporters equilibrates with the cytoplasm through passive diffusion. [2] The diffusion of cell-specific metabolites is measured at ultra-long diffusion times in the rodent and primate brain in vivo to observe how cell long-range morphology constrains metabolite diffusion. [7] The driving idea is to iteratively change the morphometric statistics values, generate many synthetic cells accordingly, and simulate particle diffusion in these cells to compute the corresponding ADC, until the difference between simulated and measured ADC satisfies some convergence criteria ( Fig. 1 ). [7] To fit experimental ADC as a function of t d, it is necessary to iteratively change the set of parameters describing the morphometric statistics used for cell graph generation and then simulate particles diffusion and compute corresponding ADC, until the difference between simulated and measured ADC satisfies some convergence criteria. [7] Molecular diffusion of many particles in each cell is then simulated according to a Monte Carlo algorithm. [7] Diffusion-weighted (DW)-MRI and -MRS, which allow the investigation of the diffusion process of endogenous molecules in biological tissues at these scales ( 1 ), have made it clear that cell architecture has a critical influence on molecular displacement ( 2 ? ? – 5 ). [7] In living systems, diffusion is responsible for the movement of a large number of substances, such as gases and small uncharged molecules, into and out of cells. [1] Molecules first undergo rapid diffusion within the three-dimensional volume of the cell body. [6] Nuclear fluorescence is plotted after normalizing each exponential fit between 2 (at t 0) and 1 (at t ?; top). (D) Diffusion curves from all such measurements of WT cells for the different-sized reporters, normalized as in C. (E) Population distributions of permeability coefficients for the different reporters as a function of their size. [2] The autocorrelation time, ?, depends upon the time it takes a molecule to traverse an optical volume, which is a convolution of the rate of diffusion, the imaging point spread function, evanescent field depth, and camera pixel size. [6] For this analysis, we kept the concentration of molecules available at the rim for diffusion as a constant, implying that the total number of molecules available differs as a function of radius. [3] Diffusion is defined as the net movement of molecules from an area of greater concentration to an area of lesser concentration. [1] This animation depicts the process of facilitated diffusion, in which a carrier protein helps molecules move across a membrane in response to a concentration gradient. [1] Large pores, consisting of several protein subunits, that allow the bulk flow of water, ions and larger molecules down their chemical concentration gradients (facilitated diffusion). [8] Generation of virtual slices also allows the quantitative analysis of synthetic cells reconstructed from diffusion not only based on the model parameters, but also using tools available for conventional histology, such as Sholl analysis ( 25 ) (at least for Sholl parameters that do not depend on the number of processes or can be normalized by it, because our modeling approach does not allow extracting the process number statistics). [7] In this paper, we have developed a mathematical framework to describe how geometry influences diffusion using a tubular surface as a model system. [3] While the total areas of the tubular and the flat surface are equal, the local area for the diffusion of molecules is reduced in a tubular geometry due to radial curvature, thus prolonging the gradient. [3] To understand the importance of tubular geometry on diffusion from a more general perspective, it is useful to compare diffusion on a tubule to diffusion on a flat surface. [3] We solved the Laplace-Beltrami equations to understand how various geometric parameters affect diffusion and thereby the concentration gradient of a diffusing species along the surface of a tubule for a prescribed boundary condition. [3] We first characterized diffusion onto a tubule as a function of time under the Dirichlet boundary condition. [3] Comparison of the diffusion as a function of time between two distinct geometries could be extremely arbitrary. [3] The locally averaged autocorrelation time, ?, of the intensity fluctuations measured at each pixel location was calculated, and the value was converted to a pseudo-color map that reports the average rate of diffusion of all objects moving at a given pixel location. [6] Here we decided to elaborate on our recent finding that metabolite diffusion measured at long diffusion times t d (up to ?1 s) is fairly stable in the monkey ( 20 ) and in the human brain ( 21 ), suggesting that metabolites are not significantly confined in subcellular regions or organelles but are instead diffusing in the long fibers typical of neuron and astrocyte morphology. [7] The investigated volume of interest within the brain (green box) and a typical DW-MRS spectrum at t d 2 s (here without diffusion weighting), as used to measure ADC time dependence for each metabolite ( Inset plots), are shown for each species. [7] For the first time, to our knowledge, the diffusion of cell-specific metabolites is investigated at ultra-long diffusion times (up to 2 s) in the healthy rodent and primate brain by DW-MRS in vivo, to specifically probe the intracellular compartment at increasing spatial scales as the diffusion time is increased. [7]

The increase in the value of ? ^ for each substrate could be a result of an increasing diffusion time between consecutive substrate encounters with the NPC, a decreased translocation probability per each such encounter (the transport efficiency), or a combination of both. [2] These results indicate that the slow diffusion of much larger substrates across the NPC ( Wang and Brattain, 2007 ; Popken et al., 2015 ) cannot be interpreted as residual leakage beyond a well-defined cutoff ( Kirli et al., 2015 ) but rather as an intrinsic property of the NPC filtering mechanism. [2] Various other substrate features have been previously proposed to affect passive diffusion ( Naim et al., 2009 ; Colwell et al., 2010 ; Tagliazucchi et al., 2013 ; Goryaynov and Yang, 2014 ). [2] It has also been suggested that the high net charge of FG nups may contribute to interaction with the mostly acidic transport receptors ( Tagliazucchi et al., 2013 ), perhaps helping to inhibit passive diffusion through competition. [2] Fig. S3 shows the general effect on the transport machinery in FG deletion strains, providing controls for our measurements of passive diffusion. [2] We collected single-cell FRAP measurements ( Fig. 6 A ) over the entire set of six labeled substrates (covering the molecular mass range 27-67 for passive diffusion) and for each of these strains. [2] Except in relatively extreme cases (such as the incorporation of many surface hydrophobic residues into diffusion substrates ), our data suggest that molecular volume is the most prominent determinant of passive diffusion. [2] Diffusion on the surfaces of molecular glasses is observed to be greatly enhanced compared with the bulk diffusion with lower activation energies. [1] To quantitatively evaluate the impact of cell structure on measured molecular diffusion, mainly two modeling strategies have been developed. [7] The consequence of such nonplanar geometry on diffusion is poorly understood but is highly relevant in the case of cell membranes, which often adopt complex geometries. [3] When cell membrane channels are helping diffusion of glucose across membrane. [1] Question : 1) Diffusion of a substance across a cell membrane. [9] The empirically tested theory of diffusion through porous aqueous membranes predicts that the permeability coefficient p of porous membranes depends on the particle probability to enter the pore ( Eq. 3 ) multiplied by the particle friction with the pore walls ( Eq. 4 ; Renkin, 1954 ): F 1 1 ? r r 0 2, (3) F 2 1 ? 2.1 r r 0 + 2.1 r r 0 3 ? 0.95 r r 0 5, (4) and p F 1 F 2, where r is the particle radius and r 0 is the pore radius. [2] Diffusion of particles in curved surfaces is inherently complex compared with diffusion in a flat membrane, owing to the nonplanarity of the surface. [3] Our studies provide a framework for modeling diffusion in curved surfaces and suggest that biological regulation can emerge purely from membrane geometry. [3] We also compared diffusion in tubular structures with that in a comparable flat surface and showed that the tubular geometry slows down diffusion. [3] Because geometry is tightly regulated in a cell, it is possible for the cells to modulate geometry to steer diffusion and concentration gradients. [3] Ans. (i) When CO 2 gets accumulated in high concentration inside the cell, it moves out of the cell by diffusion process and when concentration of carbon dioxide is lower than the surrounding atmosphere, it moves inside the cells by diffusion process. [1] To map the average rate of diffusion of all fluorescent objects at a particular location in the cell, we performed temporal autocorrelation analysis on the video data. [6] When a molecule binds at the plasma membrane it will explore a region given by Equation 1, and when it unbinds (with rate constant, g ) and undergoes diffusion in the cytosol, it will move a similar distance as shown in Equation 2, before rebinding (with rate constant, f ). [6] This assumption, though valid in studying many phenomena, may not be correct in studying diffusion in membrane deformations ( Sbalzarini et al., 2005 ; Leitenberger et al., 2008 ; Adler et al., 2010 ). [3] FG domains obstruct the passive diffusion of large macromolecules ( Paine and Feldherr, 1972 ; Paine et al., 1975 ; Keminer and Peters, 1999 ; Ribbeck and Glich, 2001 ; Mohr et al., 2009 ; Ma et al., 2012 ), restricting access to the nucleus and contributing to the retention of nuclear and cytoplasmic factors in their appropriate subcellular compartments. [2] Using thousands of independent time-resolved fluorescence microscopy measurements in vivo, we show that the NPC lacks such a firm size threshold; instead, it forms a soft barrier to passive diffusion that intensifies gradually with increasing molecular mass in both the wild-type and mutant strains with various subsets of phenylalanine-glycine (FG) domains and different levels of baseline passive permeability. [2] Passive macromolecular diffusion through nuclear pore complexes (NPCs) is thought to decrease dramatically beyond a 30-60-kD size threshold. [2] We found that FG domains with exceptionally high net charge and low hydropathy near the cytoplasmic end of the central channel contribute more strongly to obstruction of passive diffusion than to facilitated transport, revealing a compartmentalized functional arrangement within the NPC. [2] Macromolecules traverse the NPC through either facilitated or passive diffusion. [2] Passive diffusion, in contrast, is the equilibration of ions, small molecules, and small- to medium-sized macromolecules between the cytoplasm and the nucleoplasm, independent of NTRs and GTP hydrolysis. [2] Fig. S2 shows the thermodynamics and kinetics of simulated passive diffusion in relation to Fig. 4. [2] This discrepancy may be a result of loss of docking sites for Kap123, consistent with decreased rim intensity for Kap123 in that strain (Fig. S3) and with recent kinetic models that suggested that facilitated diffusion is rate-limited by the availability of docking sites rather than by transport efficiency ( Kim and Elbaum, 2013b ). [2] Our model does not account for the size of the diffusing species, which could also be critical for understanding diffusion in a real membrane ( Guigas and Weiss, 2006 ). [3] These results provide a framework for modeling diffusion in complex surfaces and suggest new numerical models for how biological functions could emerge as a consequence of the nature of diffusion in tubular geometries. [3] We want to emphasize that all diffusion conditions described in this paper refer solely to surface diffusion along tubules and not diffusion in the lumen of the tubule. [3] We show that variations in tubule radius and length can distinctly alter diffusion gradients in tubules over biologically relevant timescales. [3] This gives rise to a strong dependence of diffusion on the tubule geometry. [3] This influence of geometry on diffusion has been demonstrated for tubular geometries. [3]

Paul Andersen starts with a brief description of diffusion and osmosis. [1] We found this to be a truly difficult task and I get that, what we need to work through the most is how will you know that diffusion and/or osmosis has taken place? We have to find ways to demonstrate that these two processes have actually taken place. [1]

The predicted slowing down of diffusion due to curvature effects was shown to influence receptor egress from the dendritic spine ( Kusters et al., 2013 ). [3] Facilitated diffusion also depends on unique properties of the interactions between NTRs and FG Nups, which were suggested to exchange rapidly ( Hough et al., 2015 ; Milles et al., 2015 ) through unique interaction mechanisms ( Raveh et al., 2016 ). [2]

He then describes the diffusion demonstration and how molecules move over time. [1] There is a real need to develop models that accurately measure the diffusion of molecules in complex geometries. [3] Until now, we have compared the diffusion of molecules along a tubular surface of various dimensions. [3] One such prediction is that diffusion of a particle in a tubular structure appears to be slower than its diffusion in a flat surface, simply as a consequence of geometric effects. [3] For such particles, the diffusion times through an aqueous pore would increase sharply beyond a well-defined size threshold ( Renkin, 1954 ), which is inconsistent with the data ( Fig. 3 F ; complete analysis in Materials and methods). [2] By investigating the relationship between surface diffusion and relaxation times of ultrathin films, we find that the fast surface diffusion remains invariant of the films? relaxation dynamics even when the activation energy of the film becomes lower than the activation energy for the surface diffusion, indicating a complete decoupling of the relaxation dynamics and surface diffusion. [1] The methodology used is exactly the same as described in our recent work in the primate brain ( 20 ), i.e., spectra were acquired with a diffusion contrast ? b 3,000 s/mm 2 using a STEAM (stimulated echo acquisition mode) sequence modified for diffusion weighting, and t d was changed by changing the mixing time. [7] Diffusion constants of 1-mg/ml samples of the recombinantly purified fusion proteins were measured using a DLS instrument (Wyatt Technology) using the manufacturer?s Dynamics software. [2] By analogy with our biophysical understandings of how DNA-interacting proteins find specific binding sites on genomic DNA via a combination of three- and one-dimensional diffusion ( 36 ), we hypothesized that M10 might use a similar mechanism, but with increased dimensionality, to rapidly navigate the cytosol and plasma membrane and finally travel to the tip of the filopodium. [6] Filtration, diffusion, and molecular sieving through porous cellulose membranes. [2] An example of diffusion in biological system is diffusion of oxygen and carbon dioxide across the alveolar-capillary membrane in mammalian lungs. [4] We think this assumption of clear metabolic compartmentation is valid at the time scale of diffusion measurement: for example, the glutamate-glutamine cycle, which is a major metabolic pathway between neurons and astrocytes, is estimated in the range 0.2-0.5 mol/g per minute based on 13 C MRS studies ( 35 ), so that less than 0.1% of the glutamate pool (?10 mol/g) is transferred from neurons to astrocytes every second. [7] Visualizing the actual process of diffusion can be difficult at times. [1]

Our simulations suggest that the kinetics of passive diffusion may be modeled as an elementary chemical reaction with a single reaction step and a single transition state. [2] Many of these methods use random-walk simulations, which apply stochastic models to describe diffusion. [3] Note that the model only considers the effects of geometric factors on diffusion. [3] We have developed a generalizable model of diffusion in tubular geometries from fundamental diffusion equations and have simulated diffusion for various biologically relevant boundary conditions and parameters. [3] We did not introduce additional long axons that could be relevant for neuronal Glu and NAA. The rationale is that we wanted to assess the ability of our strategy to differentiate between astrocytic and neuronal metabolites based on their diffusion properties only, without differences imposed by different models. [7] The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting. [1] Li JR, et al. ( 2014 ) Numerical study of a macroscopic finite pulse model of the diffusion MRI signal. [7] Our model represents a simple but fundamental formulation of a diffusion equation in a static tube. [3]

True or false? Facilitated diffusion does not require the cell to use energy. [1] Extracted cell morphologies were qualitatively and quantitatively consistent with histological data, which strongly supports the idea that modeling metabolite diffusion based solely on the long-range structural properties used here is essentially valid. [7] The second approach consists in simplifying cell architectures to basic geometries (such as spheres, cylinders, or pores) for which analytical solutions describing diffusion generally exist ( 10 ). [7]

This spread of particles through random motion from an area of high concentration to an area of lower concentration is known as diffusion. [1] The entire simulation pipeline was implemented on graphics processing units (GPUs), taking advantage of the parallelizable nature of particles diffusion. [7] Monte Carlo simulations have been proposed in the past to fit simulated ( 31 ) or experimental data ( 32 ), but consisted in diffusion in determinist axonal geometries. [7] Using this equivalent geometric description, we simulated conditions to compare diffusion in curved tubes with that on comparable planar disks. [3] Characterizing the microstructure of an organ noninvasively using molecular diffusion measurements represents a major challenge in medical imaging and life science. [7] Results of Sholl analysis performed on mouse hippocampal astrocytes using conventional histology, and on synthetic astrocytes generated from the Ins and tCho diffusion compartments in the mouse brain, are reported in Table 2 and SI Appendix, Fig. S9. [7] We then derived a Laplace-Beltrami operator for the diffusion equation and solved it using finite element methods (FEM) for a symmetric condition (Supplemental Text S1). [3]

In the analysis, we assumed that the diffusion of GFP-xPrA within the nucleus and cytoplasm is rapid, compared with its exchange across the NE, and that the rates of mass transport across the envelope follow Fick?s first law. [2] Facilitated diffusion is the rapid translocation of cargo-carrying nuclear transport receptors (NTRs) through interactions with FG domains, and it is generally but not necessarily coupled to energy input from the nuclear GTPase Ran ( Quimby and Dasso, 2003 ). [2] Unlike active transport, diffusion does not involve chemical energy. [4]

They then exhibit periods of slower two-dimensional diffusion in the plane of the plasma membrane. [6] Name the process in which diffusion takes place through a selective permeable membrane. [1] Dialysis – The diffusion of a solute (salt, glucose, etc.) across a selectively permeable membrane. [5]

If passive diffusion depends on overall excluded volume, it is also expected to decay more gradually compared with a geometric barrier. [2] Facilitated diffusion in contrast is an assisted diffusion in a way that it requires a carrier molecule. [4] Can you remember walking into the front door of your home and smelling a pleasant aroma coming from the kitchen? It was diffusion of molecules from the kitchen to the front door of the house that allowed you to detect the odors. [1]

The predicted slowdown also confirms other theoretical works by groups who have investigated diffusion in curved surfaces. [3] For diffusion along the curved surface, the local area available for diffusion is different relative to a flat surface. [3] Therefore, to understand how diffusion scales with different geometric parameters on a tubular surface, we described classical motion of diffusion set on a surface. [3]

This strategy is the basis for recent quantitative DW-MRI strategies (AxCaliber, ActivAx, NODDI, etc.) ( 11 ? – 13 ) to estimate relevant structural quantities based on water diffusion in the brain, essentially axonal diameter, density, and angular dispersion. [7] While this quantification of slowdown is arbitrary, it enables us to study the relative effects of flat versus tubular geometries on diffusion. [3] Natively unfolded nucleoporins gate protein diffusion across the nuclear pore complex. [2] Yeh CH, et al. ( 2013 ) Diffusion microscopist simulator: A general Monte Carlo simulation system for diffusion magnetic resonance imaging. [7] For practicality, we first narrowed our investigation to just the GFP-2PrA and GFP-4PrA substrates, which bracket the often-stated ?40-kD diffusion limit. [2] An example would include the diffusion of nutrients from our blood into surrounding tissues. [1] We found that each of these variables plays a distinct role in regulating diffusion, depending on the boundary conditions. [3] We then examined how concentration evolves as a function of time for the same tubule dimensions and diffusion coefficients simulated for the Dirichlet boundary condition. [3] We next investigated how diffusion coefficient, tubule radius, and tubule length alter the equilibration time. [3] To study how tubule geometry influences the way molecules are sorted into tubules, we varied the length and radius of the tubule and the diffusion coefficient of the diffusing species to determine the corresponding concentration gradients. [3] Of the three parameters we examined (tubule height, tubule radius, and diffusion coefficient), within the limits of biological molecules, we found that tubule height was a key parameter affecting the equilibration of material across the surface of tubules. [3] Next we simulated diffusion on tubules of variable lengths, assuming a constant radius of 0.1 ?m and a diffusion coefficient of 0.1 ?m 2 /s (Supplemental Figure 4A). [3] We examined three key parameters that can potentially influence diffusion–the tube radius, the length of the tubule, and the diffusion coefficient of the diffusing species–using a range of biologically relevant values of these parameters. [3] As before, we varied the three parameters of interest (radius, length, and diffusion coefficient), but in this case, we carried out simulations for both flat and curved surfaces. [3]

Diffusion coefficients ranging from 0.01 to 0.5 ?m 2 /s were chosen to mimic those previously reported for membrane proteins ( Daumas et al., 2003 ; Kenworthy et al., 2004 ). [3] Both experiments were performed on tubes with a length of 6 ?m but slightly different radii of 30 and 20 nm and contained two different proteins whose diffusion coefficients were quantified as 0.73 and 0.38 ?m 2 /s. [3] Diffusing species with a diffusion coefficient of 0.5 ?m 2 /s have a half-time of ?0.5 s, whereas a 10-fold slower diffusion coefficient of 0.05 ?m 2 /s yields a half-time of ?4.5 s. [3]

Most models and experiments assume that the changes in diffusion with curvature are due to the altered diffusion coefficients of molecules ( Berk and Hochmuth, 1992 ; Domanov et al., 2011 ; Zhu et al., 2012 ; Aimon et al., 2014 ). [3] Diffusion from the rim into the tubule occurs with a defined diffusion coefficient k, which falls within a range of values previously reported for membrane-associated proteins ( Daumas et al., 2003 ; Kenworthy et al., 2004 ). [3] To delineate these factors from differences arising purely from geometry, we have included a program (Supplemental Text S6) that can determine the “expected diffusion coefficient” given t 1/2 (as measured in FRAP experiments) for concentration equilibration for a given tubule geometry. [3] Not surprisingly, as shown in Supplemental Figures 5 and 6, increasing the diffusion coefficient increased the absolute concentration of material present within the tubule. [3]

The amplitude and temporal spread of the concentration gradient is systematically dependent on the curvature of the tubule and the diffusion coefficient of the molecule. [3] We acknowledge that this description is still incomplete, as other factors could potentially impact how concentration gradients evolve for a given tubule geometry and diffusion coefficient. [3] Even the measurement of diffusion coefficients in nonplanar geometries could be inherently difficult due to the complexity of geometry ( Sbalzarini et al., 2005 ; Daniels and Turner, 2007 ; Domanov et al., 2011 ; Renner et al., 2011 ). [3]

A well-established determinant of passive diffusion rates is molecular mass, which was observed to affect passive permeability in seminal studies by Paine et al. in the 1970s ( Paine and Feldherr, 1972 ; Paine et al., 1975 ) using state-of-the-art methods for that time, suggesting a molecular mass size threshold of 30-60 kD for permeation of the nucleus. [2] Our results indicate that passive diffusion rates depend most strongly on the substrate molecular mass, that is, its volume ( Fischer et al., 2004 ; Fig. 3, A and B ), and not so much on molecular shape asymmetry ( Fig. 3 E ). [2] In these studies, passive diffusion rates have been quantified only for a small number of relevant substrate sizes ( Mincer and Simon, 2011 ; Moussavi-Baygi et al., 2011 ) or they have focused on the system thermodynamics rather than kinetics ( Ghavami et al., 2016 ), prohibiting direct comparisons with our kinetic experimental and computational data. [2] The overall molecular mass of a substrate determines its passive diffusion rates, regardless of its particular shape geometry. [2] As barrier-forming FG domains are deleted from the NPC, we may expect either that passive permeability would increase more significantly for larger substrates than for smaller substrates or, alternatively, that the deletion of these domains may increase the diffusion rates of both small and large substrates uniformly. [2] 4- to 5-MD mRNP complexes translocate through the NPC in merely 200 ms ( Grwald and Singer, 2010 ; Lowe et al., 2015 ), and the facilitated diffusion rates of ?10-MD complexes of quantum dots and NTRs crossed the NPC within a few seconds when bound to 20 NTR molecules and in less than a second when bound to 40 NTR molecules, respectively ( Lowe et al., 2010 ). [2]

The diffusion coefficient was then determined using a theoretical solution for diffusion in the special case of a long, thin cylinder connected to a sphere that acts as a reservoir for the diffusing molecules ( Berk and Hochmuth, 1992 ). [3] Pharmacological studies of diffusion fMRI using anaesthetic agents and various challenges, by Yoshifumi Abe, Tomokazu Tsurugizawa and Denis Le Bihan, reveal that water diffusion (apparent diffusion coefficient) quantitatively reflects the underlying neural activity and is associated with local cell swelling. [1] This work suggests that the time dependence of metabolite diffusion coefficient allows distinguishing and quantitatively characterizing brain cell morphologies noninvasively. [7] This analysis shows that the local diffusion coefficient is highest around the cell center and lower at the cell periphery. [6]

Comparison of diffusion coefficients obtained from the simulation with those obtained from other empirical models ( Berk and Hochmuth, 1992 ) that incorporate factors such as hydrodynamic effects ( Daniels and Turner, 2007 ) will decouple these two factors. [3] To compare these experimental photobleaching data with our diffusion model, we simulated diffusion on these exact geometries using the experimentally determined diffusion coefficients. [3] Each bead was also assigned a random rotational diffusion coefficient using the Einstein-Stokes equation. [2]

We have also included, as a part of the code, a diffusion coefficient mapper for FRAP experiments of tubules that, given the height, radius, and t 1/2 will give out the diffusion coefficient. [3] The open circles denote the original FRAP data from the paper, and the lines denote the recovery curve from the simulation, adapting the geometry and diffusion coefficients measured in the paper. [3] The fit to a power function is indicated as in A. (C, top) The ratio between S max, the sedimentation coefficient of a perfect sphere, and S, the measured sedimentation coefficient, plotted against the SAXS measured shape asymmetry. (bottom) The ratio between the measured sedimentation coefficient and the measured diffusion coefficient 95% confidence intervals, plotted against molecular mass. [2]

**RANKED SELECTED SOURCES **(17 source documents arranged by frequency of occurrence in the above report)

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3. (54) Simple rules for passive diffusion through the nuclear pore complex | JCB

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7. (8) The Diffusion Limit of Transport Equations in Biology – Springer

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10. (5) VCell- Modeling & Analysis Software Virtual Cell Modeling & Analysis Software

11. (5) Diffusion – Biology-Online Dictionary

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13. (2) S101 Diffusion O2 Sensor | Qubit Biology Inc.

14. (2) Diffusion, Dialysis and Osmosis Tutorial

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16. (1) Primary and Secondary Active Transport – WikiLectures

17. (1) 1) Diffusion Of A Substance Across A Cell Membrane. | Chegg.com