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Mechanical characteristics of single biological cells are used to identify and possibly leverage interesting differences among cells or cell populations. Fluidity---hysteresivity normalized to the extremes of an elastic solid or a viscous liquid---can be extracted from, and compared among, multiple rheological measurements of cells: creep compliance vs. time, complex modulus vs. frequency, and phase lag vs. frequency. With multiple strategies available for acquisition of this nondimensional property, fluidity may serve as a useful and robust parameter for distinguishing cell populations, and for understanding the physical origins of deformability in soft matter. Here, for three disparate eukaryotic cell types deformed in the suspended state via optical stretching, we examine the dependence of fluidity on chemical and environmental influences around a time scale of 1 s. We find that fluidity estimates are consistent in the time and the frequency domains under a structural damping (power-law or fractional derivative)model, but not under an equivalent-complexity lumpedcomponent (spring-dashpot) model; the latter predicts spurious time constants. Although fluidity is suppressed by chemical crosslinking, we find that adenosine triphosphate (ATP) depletion in the cell does not measurably alter the parameter, and thus conclude that active ATP-driven events are not a crucial enabler of fluidity during linear viscoelastic deformation of a suspended cell. Finally, by using the capacity of optical stretching to produce near-instantaneous increases in cell temperature, we establish that fluidity increases with temperature---now measured in a fully suspended, sortable cell without the complicating factor of cell-substratum adhesion.

This paper describes Scratch, a visual, block-based programming language designed to facilitate media manipulation for novice programmers. We report on the Scratch programming experiences of urban youth ages 8-18 at a Computer Clubhouse--an after school center--over an 18-month period. Our analyses of 536 Scratch projects collected during this time documents the learning of key programming concepts even in the absence of instructional interventions or experienced mentors. We discuss the motivations of urban youth who choose to program in Scratch rather than using one of the many other software packages available to them and the implications for introducing programming at after school settings in under served communities.

Investigations of natural variation in cell mechanics within a cell population are essential to understand the stochastic nature of soft-network deformation. Striking commonalities have been found concerning the average values and distribution of rheological parameters of cells: first, attached and suspended cells exhibit power-law rheological behavior; second, cell stiffness is distributed log-normally. A predictive connection between these two near-universal findings has not been reported, to our knowledge. Here we postulate, based on our own and others' experimental reports and leading models of cell rheology, that the exponent that characterizes power-law rheology varies intrinsically among cells as an approximately Gaussian-distributed variable. Besides explaining naturally the log-normal distribution of cell stiffness that is widely observed, this postulate predicts multiple empirically observed relationships from cell deformation studies. Our framework ultimately links inherent noise in postulated relaxation mechanisms of cytoskeletal networks to mechanical variation among cells and cell populations.

Investigations of natural variation among cells within a population are essential for understanding the stochastic nature of tissue cell deformation under applied load. In the existing literature, the population variation of single-cell creep compliance has so far been modeled universally by using a log-normal distribution. Here we use optical stretching, a non-contact and relatively high-throughput technique for probing cell mechanics, to accumulate a sufficient data set that demonstrates robustly that cell compliance varies according to the similar but distinct gamma distribution. Additionally, we re-examine existing simulations that were originally proposed to justify a log-normal fit, and show that in fact these simulation data also correspond to the gamma distribution. Finally, we propose a general stochastic differential equation that analytically predicts a gamma distribution of creep compliance during cell stretching, as well as the Gaussian distribution of cell recovery that we observe experimentally upon removal of applied load. The population variation is well characterized by just a single parameter in each of the creep and recovery regimes. We expect our correction of a phenomenological distribution fit, enabled by an expansive data set for mesenchymal stem cells, to enable the development of more accurate constitutive laws to describe cytoskeletal deformation. These findings thus serve to replace an empirical distribution with a better-fitting model that rests on a more solid experimental and theoretical foundation, and also provides a basis to predict and understand the stochastic nature of the mechanical response of individual cells within populations., Comment: Withdrawn and replaced by arXiv:1104.0702