Weak Force Stalls Protrusion at the Leading Edge of the Lamellipodium

AbstractProtrusion, the first step of cell migration, is driven by actin polymerization coupled to adhesion at the cell’s leading edge. Polymerization and adhesive forces have been estimated, but the net protrusion force has not been measured accurately. We arrest the leading edge of a moving fish keratocyte with a hydrodynamic load generated by a fluid flow from a micropipette. The flow arrests protrusion locally as the cell approaches the pipette, causing an arc-shaped indentation and upward folding of the leading edge. The effect of the flow is reversible upon pipette removal and dependent on the flow direction, suggesting that it is a direct effect of the external force rather than a regulated cellular response. Modeling of the fluid flow gives a surprisingly low value for the arresting force of just a few piconewtons per micrometer. Enhanced phase contrast, fluorescence, and interference reflection microscopy suggest that the flow does not abolish actin polymerization and does not disrupt the adhesions formed before the arrest but rather interferes with weak nascent adhesions at the very front of the cell. We conclude that a weak external force is sufficient to reorient the growing actin network at the leading edge and to stall the protrusion

Requirements for Power Hardware-in-the-Loop Emulation of Distribution Grid Challenges

The ongoing transition of low voltage (LV) power grids towards active systems requires novel evaluation and testing concepts, in particular for realistic testing of devices. Power Hardware-in-the-Loop (PHIL) evaluations are a promising approach for this purpose. This paper presents preliminary investigations addressing the systematic design of PHIL applications and their applicable stability mechanisms and gives a detailed review of the related work. A requirement analysis for emulation of grid situations demanding system services is given and the realization of a PHIL setup is demonstrated in a residential scenario, comprising a hybrid electrical energy storage system (HESS)

Neural Mention Detection

Mention detection is an important preprocessing step for annotation and interpretation in applications such as NER and coreference resolution, but few stand-alone neural models have been proposed able to handle the full range of mentions. In this work, we propose and compare three neural network-based approaches to mention detection. The first approach is based on the mention detection part of a state of the art coreference resolution system; the second uses ELMO embeddings together with a bidirectional LSTM and a biaffine classifier; the third approach uses the recently introduced BERT model. Our best model (using a biaffine classifier) achieves gains of up to 1.8 percentage points on mention recall when compared with a strong baseline in a HIGH RECALL coreference annotation setting. The same model achieves improvements of up to 5.3 and 6.2 p.p. when compared with the best-reported mention detection F1 on the CONLL and CRAC coreference data sets respectively in a HIGH F1 annotation setting. We then evaluate our models for coreference resolution by using mentions predicted by our best model in start-of-the-art coreference systems. The enhanced model achieved absolute improvements of up to 1.7 and 0.7 p.p. when compared with our strong baseline systems (pipeline system and end-to-end system) respectively. For nested NER, the evaluation of our model on the GENIA corpora shows that our model matches or outperforms state-of-the-art models despite not being specifically designed for this task

Named Entity Recognition as Dependency Parsing

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing, concerned with identifying spans of text expressing references to entities. NER research is often focused on flat entities only (flat NER), ignoring the fact that entity references can be nested, as in [Bank of [China]] (Finkel and Manning, 2009). In this paper, we use ideas from graph-based dependency parsing to provide our model a global view on the input via a biaffine model (Dozat and Manning, 2017). The biaffine model scores pairs of start and end tokens in a sentence which we use to explore all spans, so that the model is able to predict named entities accurately. We show that the model works well for both nested and flat NER through evaluation on 8 corpora and achieving SoTA performance on all of them, with accuracy gains of up to 2.2 percentage points

Microscopic Calculation of in-Medium Proton-Proton Cross Sections

We derive in-medium PROTON-PROTON cross sections in a microscopic model based upon the Bonn nucleon-nucleon potential and the Dirac-Brueckner approach for nuclear matter. We demonstrate the difference between proton-proton and neutron-proton cross sections and point out the need to distinguish carefully between the two cases. We also find substantial differences between our in-medium cross sections and phenomenological parametrizations that are commonly used in heavy-ion reactions.Comment: 9 pages of RevTex and 4 figures (postscript in separate uuencoded file), UI-NTH-930

Cooperation and Self-Regulation in a Model of Agents Playing Different Games

A simple model for cooperation between "selfish" agents, which play an extended version of the Prisoner's Dilemma(PD) game, in which they use arbitrary payoffs, is presented and studied. A continuous variable, representing the probability of cooperation, $p_k(t) \in$ [0,1], is assigned to each agent $k$ at time $t$. At each time step $t$ a pair of agents, chosen at random, interact by playing the game. The players update their $p_k(t)$ using a criteria based on the comparison of their utilities with the simplest estimate for expected income. The agents have no memory and use strategies not based on direct reciprocity nor 'tags'. Depending on the payoff matrix, the systems self-organizes - after a transient - into stationary states characterized by their average probability of cooperation $\bar{p}_{eq}$ and average equilibrium per-capita-income $\bar{p}_{eq},\bar{U}_\infty$. It turns out that the model exhibit some results that contradict the intuition. In particular, some games which - {\it a priory}- seems to favor defection most, may produce a relatively high degree of cooperation. Conversely, other games, which one would bet that lead to maximum cooperation, indeed are not the optimal for producing cooperation.Comment: 11 pages, 3 figures, keybords: Complex adaptive systems, Agent-based models, Social system

Momentum-Dependent Mean Field Based Upon the Dirac-Brueckner Approach for Nuclear Matter

A momentum-dependent mean field potential, suitable for application in the transport-model description of nucleus-nucleus collisions, is derived in a microscopic way. The derivation is based upon the Bonn meson-exchange model for the nucleon-nucleon interaction and the Dirac-Brueckner approach for nuclear matter. The properties of the microscopic mean field are examined and compared with phenomenological parametrizations which are commonly used in transport-model calculations.Comment: 15 pages text (RevTex) and 4 figures (postscript in a separate uuencoded file), UI-NTH-930