When The Sunbeams Are Kissing The Roses : I\u27ll go back to my sweet Irish Rose

https://digitalcommons.library.umaine.edu/mmb-vp/2733/thumbnail.jp

Viscoelastic response of fibroblasts to tension transmitted through adherens junctions

Cytoplasmic deformation was monitored by observing the displacements of 200-nm green fluorescent beads microinjected into the cytoplasm of Swiss 3T3 fibroblasts. We noted a novel protrusion of nonruffling cell margins that was accompanied by axial flow of beads and cytoplasmic vesicles as far as 50 microm behind the protruding plasma membrane. Fluorescent analog cytochemistry and immunofluorescence localization of F-actin, alpha-actinin, N-cadherin, and beta-catenin showed that the protruding margins of deforming cells were mechanically coupled to neighboring cells by adherens junctions. Observations suggested that protrusion resulted from passive linear deformation in response to tensile stress exerted by centripetal contraction of the neighboring cell. The time dependence of cytoplasmic strain calculated from the displacements of beads and vesicles was fit quantitatively by a Kelvin-Voight model for a viscoelastic solid with a mean limiting strain of 0.58 and a mean strain rate of 4.3 x 10(-3) s(-1). In rare instances, the deforming cell and its neighbor spontaneously became uncoupled, and recoil of the protruding margin was observed. The time dependence of strain during recoil also fit a Kelvin-Voight model with similar parameters, suggesting that the kinetics of deformation primarily reflect the mechanical properties of the deformed cell rather than the contractile properties of its neighbor. The existence of mechanical coupling between adjacent fibroblasts through adherens junctions and the viscoelastic responses of cells to tension transmitted directly from cell to cell are factors that must be taken into account to fully understand the role of fibroblasts in such biological processes as wound closure and extracellular matrix remodeling during tissue development

Beeping a Maximal Independent Set

We consider the problem of computing a maximal independent set (MIS) in an extremely harsh broadcast model that relies only on carrier sensing. The model consists of an anonymous broadcast network in which nodes have no knowledge about the topology of the network or even an upper bound on its size. Furthermore, it is assumed that an adversary chooses at which time slot each node wakes up. At each time slot a node can either beep, that is, emit a signal, or be silent. At a particular time slot, beeping nodes receive no feedback, while silent nodes can only differentiate between none of its neighbors beeping, or at least one of its neighbors beeping. We start by proving a lower bound that shows that in this model, it is not possible to locally converge to an MIS in sub-polynomial time. We then study four different relaxations of the model which allow us to circumvent the lower bound and find an MIS in polylogarithmic time. First, we show that if a polynomial upper bound on the network size is known, it is possible to find an MIS in O(log^3 n) time. Second, if we assume sleeping nodes are awoken by neighboring beeps, then we can also find an MIS in O(log^3 n) time. Third, if in addition to this wakeup assumption we allow sender-side collision detection, that is, beeping nodes can distinguish whether at least one neighboring node is beeping concurrently or not, we can find an MIS in O(log^2 n) time. Finally, if instead we endow nodes with synchronous clocks, it is also possible to find an MIS in O(log^2 n) time.Comment: arXiv admin note: substantial text overlap with arXiv:1108.192

The Potential of Restarts for ProbSAT

This work analyses the potential of restarts for probSAT, a quite successful algorithm for k-SAT, by estimating its runtime distributions on random 3-SAT instances that are close to the phase transition. We estimate an optimal restart time from empirical data, reaching a potential speedup factor of 1.39. Calculating restart times from fitted probability distributions reduces this factor to a maximum of 1.30. A spin-off result is that the Weibull distribution approximates the runtime distribution for over 93% of the used instances well. A machine learning pipeline is presented to compute a restart time for a fixed-cutoff strategy to exploit this potential. The main components of the pipeline are a random forest for determining the distribution type and a neural network for the distribution's parameters. ProbSAT performs statistically significantly better than Luby's restart strategy and the policy without restarts when using the presented approach. The structure is particularly advantageous on hard problems.Comment: Eurocast 201

Inclusion in Catholic Schools: An Introduction to the Special Issue

Introduction to the special issu

Computing in Additive Networks with Bounded-Information Codes

This paper studies the theory of the additive wireless network model, in which the received signal is abstracted as an addition of the transmitted signals. Our central observation is that the crucial challenge for computing in this model is not high contention, as assumed previously, but rather guaranteeing a bounded amount of \emph{information} in each neighborhood per round, a property that we show is achievable using a new random coding technique. Technically, we provide efficient algorithms for fundamental distributed tasks in additive networks, such as solving various symmetry breaking problems, approximating network parameters, and solving an \emph{asymmetry revealing} problem such as computing a maximal input. The key method used is a novel random coding technique that allows a node to successfully decode the received information, as long as it does not contain too many distinct values. We then design our algorithms to produce a limited amount of information in each neighborhood in order to leverage our enriched toolbox for computing in additive networks

Preschool sleep and depression interact to predict gray matter volume trajectories across late childhood to adolescence

There is a close relationship between sleep and depression, and certain maladaptive outcomes of sleep problems may only be apparent in individuals with heightened levels of depression. In a sample enriched for preschool depression, we examined how sleep and depression in early childhood interact to predict later trajectories of gray matter volume. Participants (N = 161) were recruited and assessed during preschool (ages 3-6 years) and were later assessed with five waves of structural brain imaging, spanning from late childhood to adolescence. Sleep and depression were assessed using a semi-structured parent interview when the children were preschool-aged, and total gray matter volume was calculated at each scan wave. Although sleep disturbances alone did not predict gray matter volume/trajectories, preschool sleep and depression symptoms interacted to predict later total gray matter volume and the trajectory of decline in total gray matter volume. Sleep disturbances in the form of longer sleep onset latencies, increased irregularity in the child\u27s sleep schedule, and higher levels of daytime sleepiness in early childhood were all found to interact with early childhood depression severity to predict later trajectories of cortical gray matter volume. Findings provide evidence of the interactive effects of preschool sleep and depression symptoms on later neurodevelopment