Single-Cell Measurements of Two-Dimensional Binding Affinity Across Cell Contacts

Increasing the efficiency of passive fuel cells is a significant hurdle in commercializing small fuel cells. By understanding the interactions within a single cell, possibilities for further performance increases in fuel cell structures overall are uncovered. To investigate the multiphase flows and the interactions between the layers on the anode side of a direct methanol fuel cell (DMFC), a single cell was studied using a two-dimensional model. This multiphase model focuses on the flow mechanism of a single CO2 gas bubble. The model describes the mass transfer in a single cell by using the physical properties of a single bubble and by tracing its movement. The simulation results indicate that the thickness of a gas diffusion layer (GDL) has an effect on the CO2 bubble size at a low power output level. When the power output is increased, the porosity and the GDL’s contact angle with CO2 play a significant role in determining the size of the CO2 bubbles. The final bubble size and the time it takes for the bubbles to penetrate the layers of the DMFC are controlled by the physical properties of the GDL and by the power output. The model suggests that, to achieve optimal performance, the GDL in passive DMFCs should be thick enough to allow bubbles grow to their maximum size. The thickness of the GDL can be calculated by estimating the maximum size of the bubble.

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In vivo distribution of proteins within a single cell.

Clinical Chemistry ◽

10.1093/clinchem/28.4.1011 ◽

1982 ◽

Vol 28 (4) ◽

pp. 1011-1014 ◽

Cited By ~ 2

Author(s):

C F Austerberry ◽

P L Paine

Keyword(s):

Xenopus Laevis ◽

Single Cell ◽

Gel Electrophoresis ◽

Living Cell ◽

Experimental System ◽

Two Dimensional ◽

Two Dimensional Gel Electrophoresis ◽

In Vivo Distribution ◽

Nucleocytoplasmic Distribution

Abstract Using the oocyte of Xenopus laevis, we present an experimental system, involving two-dimensional gel electrophoresis, for measuring unambiguously the nucleocytoplasmic distribution of proteins within a living cell.

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IN SILICO STUDY OF APIGENIN AND APIGETRIN AS INHIBITOR OF 3-HYDROXY-3-METHYL-GLUTAYL-COENZYME A REDUCTASE

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10i11.20493 ◽

2017 ◽

Vol 10 (11) ◽

pp. 284

Author(s):

Dwintha Lestari ◽

Elin Yulinah Sukandar ◽

Irda Fidrianny

Keyword(s):

Binding Site ◽

Binding Affinity ◽

In Silico ◽

Coenzyme A ◽

Inhibition Constant ◽

Two Dimensional ◽

Binding Interaction ◽

Docking Result ◽

Binding Process ◽

In Silico Study

Objective: The objectives of this research were to investigate in silico interaction between apigenin and apigetrin with 3-hydroxy-3-methyl-glutayl-coenzyme A (HMG Co-A) reductase, to find the most favorable binding site as well as to predict the binding mode.Materials and Methods: Docking calculation was performed by branded Sony Vaio PC Linux Ubuntu 14.04 LTS. The binding process based on the best docking result with HMG Co-A reductase was presented in two-dimensional diagram. Statin, atorvastatin, and R-mevalonate were used as standard.Results: Binding affinity and inhibition constant of R-mevalonate were Ei=−4.2 kcal/mol, Ki=836.78 μM; apigenin Ei=−7.0 kcal/mol, Ki=7.43 μM; apigetrin Ei=−5.9 kcal/mol, Ki=47.53 μM; simvastatin Ei=−8.2 kcal/mol; Ki=0.98 μM; atorvastatin Ei=−8.4 kcal/mol; Ki=0.7 μM. Apigenin had better binding interaction than apigetrin.Conclusion: Apigenin could be developed as anticholesterol.

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Determining Force Dependence of Two-Dimensional Receptor-Ligand Binding Affinity by Centrifugation

Biophysical Journal ◽

10.1016/s0006-3495(98)77807-5 ◽

1998 ◽

Vol 74 (1) ◽

pp. 492-513 ◽

Cited By ~ 108

Author(s):

James W. Piper ◽

Robert A. Swerlick ◽

Cheng Zhu

Keyword(s):

Ligand Binding ◽

Binding Affinity ◽

Two Dimensional ◽

Receptor Ligand

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Three-dimensional and multicellular steady and unsteady convection in fluid-saturated porous media at high Rayleigh numbers

Journal of Fluid Mechanics ◽

10.1017/s0022112079000926 ◽

1979 ◽

Vol 94 (1) ◽

pp. 25-38 ◽

Cited By ~ 71

Author(s):

Gerald Schubert ◽

Joe M. Straus

Keyword(s):

Single Cell ◽

Cross Section ◽

Three Dimensional ◽

Two Dimensional ◽

Arbitrary Length ◽

Dimensional Flow ◽

A Value ◽

Square Cylinders ◽

Two Dimensional Flow ◽

Rayleigh Numbers

In an effort to determine the characteristics of the various types of convection that can occur in a fluid-saturated porous medium heated from below, a Galerkin approach is used to investigate three-dimensional convection in a cube and two-dimensional convection in a square cross-section. Strictly two-dimensional, single-cell flow in a square cross-section is steady for Rayleigh numbers R between 4π2 and a critical value which lies between 300 and 320; it is unsteady at higher values of R. Double-cell, two-dimensional flow in a square cross-section becomes unsteady when R exceeds a value between 650 and 700, and triple-cell motion is unsteady for R larger than a value between 800 and 1000. Considerable caution must be exercised in attributing physical reality to these flows. Strictly two-dimensional, steady, multicellular convection may not be realizable in a three-dimensional geometry because of instability to perturbations in the orthogonal dimension. For example, even though single-cell, two-dimensional convection in a square cross-section is steady at R = 200, it cannot exist in either an infinitely long square cylinder or in a cube. It could exist, however, in a cylinder whose length is smaller than 0.38 times the dimension of its square cross-section. Three-dimensional convection in a cube becomes unsteady when R exceeds a value between 300 and 320, similar to the unicellular two-dimensional flow in a square cross-section. Nusselt numbers Nu, generally accurate to 1%, are given for the strictly two-dimensional flows up to R = 1000 and for three-dimensional convection in cubes up to R = 500. Single-cell, two-dimensional, steady convection in a square cross-section transports the most heat for R < 97; this mode of convection is also stable in square cylinders of arbitrary length including the cube for R < 97. Steady three-dimensional convection in cubes transports more heat for 97 [lsim ] R [lsim ] 300 than do any of the realizable two-dimensional modes. At R [gsim ] 300 the unsteady modes of convection in both square cylinders and cubes involve wide variations in Nu.

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Two-dimensional square-lattice photonic bandgap single-cell laser

10.1117/12.463871 ◽

2002 ◽

Author(s):

Han-Youl Ryu ◽

Se-Heon Kim ◽

Hong-Gyu Park ◽

Yong-Hee Lee

Keyword(s):

Single Cell ◽

Photonic Bandgap ◽

Square Lattice ◽

Two Dimensional

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DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

10.1101/2021.08.10.455795 ◽

2021 ◽

Author(s):

Owen M. O'Connor ◽

Razan N. Alnahhas ◽

Jean-Baptiste Lugagne ◽

Mary Dunlop

Keyword(s):

Single Cell ◽

Temporal Dynamics ◽

Single Cells ◽

Time Lapse ◽

Error Rates ◽

Two Dimensions ◽

Age And Growth ◽

Two Dimensional ◽

Spatial Effects ◽

Deep Convolutional Neural Networks

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells that must be tracked. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats and arbitrary image sizes as inputs. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

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