TY - JOUR
AB - Differential cell adhesion and cortex tension are thought to drive cell sorting by controlling cell-cell contact formation. Here, we show that cell adhesion and cortex tension have different mechanical functions in controlling progenitor cell-cell contact formation and sorting during zebrafish gastrulation. Cortex tension controls cell-cell contact expansion by modulating interfacial tension at the contact. By contrast, adhesion has little direct function in contact expansion, but instead is needed to mechanically couple the cortices of adhering cells at their contacts, allowing cortex tension to control contact expansion. The coupling function of adhesion is mediated by E-cadherin and limited by the mechanical anchoring of E-cadherin to the cortex. Thus, cell adhesion provides the mechanical scaffold for cell cortex tension to drive cell sorting during gastrulation.
AU - Maître, Jean-Léon
AU - Berthoumieux, Hélène
AU - Krens, Gabriel
AU - Salbreux, Guillaume
AU - Julicher, Frank
AU - Paluch, Ewa
AU - Heisenberg, Carl-Philipp J
ID - 2951
IS - 6104
JF - Science
TI - Adhesion functions in cell sorting by mechanically coupling the cortices of adhering cells
VL - 338
ER -
TY - JOUR
AB - Body axis elongation represents a common and fundamental morphogenetic process in development. A key mechanism triggering body axis elongation without additional growth is convergent extension (CE), whereby a tissue undergoes simultaneous narrowing and extension. Both collective cell migration and cell intercalation are thought to drive CE and are used to different degrees in various species as they elongate their body axis. Here, we provide an overview of CE as a general strategy for body axis elongation and discuss conserved and divergent mechanisms underlying CE among different species.
AU - Tada, Masazumi
AU - Heisenberg, Carl-Philipp J
ID - 2952
IS - 21
JF - Development
TI - Convergent extension Using collective cell migration and cell intercalation to shape embryos
VL - 139
ER -
TY - JOUR
AU - Heisenberg, Carl-Philipp J
AU - Fässler, Reinhard
ID - 2953
IS - 5
JF - Current Opinion in Cell Biology
TI - Cell-cell adhesion and extracellular matrix diversity counts
VL - 24
ER -
TY - JOUR
AB - Spontaneous postsynaptic currents (PSCs) provide key information about the mechanisms of synaptic transmission and the activity modes of neuronal networks. However, detecting spontaneous PSCs in vitro and in vivo has been challenging, because of the small amplitude, the variable kinetics, and the undefined time of generation of these events. Here, we describe a, to our knowledge, new method for detecting spontaneous synaptic events by deconvolution, using a template that approximates the average time course of spontaneous PSCs. A recorded PSC trace is deconvolved from the template, resulting in a series of delta-like functions. The maxima of these delta-like events are reliably detected, revealing the precise onset times of the spontaneous PSCs. Among all detection methods, the deconvolution-based method has a unique temporal resolution, allowing the detection of individual events in high-frequency bursts. Furthermore, the deconvolution-based method has a high amplitude resolution, because deconvolution can substantially increase the signal/noise ratio. When tested against previously published methods using experimental data, the deconvolution-based method was superior for spontaneous PSCs recorded in vivo. Using the high-resolution deconvolution-based detection algorithm, we show that the frequency of spontaneous excitatory postsynaptic currents in dentate gyrus granule cells is 4.5 times higher in vivo than in vitro.
AU - Pernia-Andrade, Alejandro
AU - Goswami, Sarit
AU - Stickler, Yvonne
AU - Fröbe, Ulrich
AU - Schlögl, Alois
AU - Jonas, Peter M
ID - 2954
IS - 7
JF - Biophysical Journal
TI - A deconvolution based method with high sensitivity and temporal resolution for detection of spontaneous synaptic currents in vitro and in vivo
VL - 103
ER -
TY - CONF
AB - We consider two-player stochastic games played on finite graphs with reachability objectives where the first player tries to ensure a target state to be visited almost-surely (i.e., with probability 1), or positively (i.e., with positive probability), no matter the strategy of the second player. We classify such games according to the information and the power of randomization available to the players. On the basis of information, the game can be one-sided with either (a) player 1, or (b) player 2 having partial observation (and the other player has perfect observation), or two-sided with (c) both players having partial observation. On the basis of randomization, the players (a) may not be allowed to use randomization (pure strategies), or (b) may choose a probability distribution over actions but the actual random choice is external and not visible to the player (actions invisible), or (c) may use full randomization. Our main results for pure strategies are as follows. (1) For one-sided games with player 1 having partial observation we show that (in contrast to full randomized strategies) belief-based (subset-construction based) strategies are not sufficient, and we present an exponential upper bound on memory both for almostsure and positive winning strategies; we show that the problem of deciding the existence of almost-sure and positive winning strategies for player 1 is EXPTIME-complete. (2) For one-sided games with player 2 having partial observation we show that non-elementary memory is both necessary and sufficient for both almost-sure and positive winning strategies. (3) We show that for the general (two-sided) case finite-memory strategies are sufficient for both positive and almost-sure winning, and at least non-elementary memory is required. We establish the equivalence of the almost-sure winning problems for pure strategies and for randomized strategies with actions invisible. Our equivalence result exhibits serious flaws in previous results of the literature: we show a non-elementary memory lower bound for almost-sure winning whereas an exponential upper bound was previously claimed.
AU - Chatterjee, Krishnendu
AU - Doyen, Laurent
ID - 2955
T2 - Proceedings of the 2012 27th Annual ACM/IEEE Symposium on Logic in Computer Science
TI - Partial-observation stochastic games: How to win when belief fails
ER -
TY - CONF
AB - Two-player games on graphs are central in many problems in formal verification and program analysis such as synthesis and verification of open systems. In this work we consider solving recursive game graphs (or pushdown game graphs) that can model the control flow of sequential programs with recursion. While pushdown games have been studied before with qualitative objectives, such as reachability and parity objectives, in this work we study for the first time such games with the most well-studied quantitative objective, namely, mean payoff objectives. In pushdown games two types of strategies are relevant: (1) global strategies, that depend on the entire global history; and (2) modular strategies, that have only local memory and thus do not depend on the context of invocation, but only on the history of the current invocation of the module. Our main results are as follows: (1) One-player pushdown games with mean-payoff objectives under global strategies are decidable in polynomial time. (2) Two-player pushdown games with mean-payoff objectives under global strategies are undecidable. (3) One-player pushdown games with mean-payoff objectives under modular strategies are NP-hard. (4) Two-player pushdown games with mean-payoff objectives under modular strategies can be solved in NP (i.e., both one-player and two-player pushdown games with mean-payoff objectives under modular strategies are NP-complete). We also establish the optimal strategy complexity showing that global strategies for mean-payoff objectives require infinite memory even in one-player pushdown games; and memoryless modular strategies are sufficient in two-player pushdown games. Finally we also show that all the problems have the same computational complexity if the stack boundedness condition is added, where along with the mean-payoff objective the player must also ensure that the stack height is bounded.
AU - Chatterjee, Krishnendu
AU - Velner, Yaron
ID - 2956
T2 - Proceedings of the 2012 27th Annual ACM/IEEE Symposium on Logic in Computer Science
TI - Mean payoff pushdown games
ER -
TY - CONF
AB - We consider probabilistic automata on infinite words with acceptance defined by parity conditions. We consider three qualitative decision problems: (i) the positive decision problem asks whether there is a word that is accepted with positive probability; (ii) the almost decision problem asks whether there is a word that is accepted with probability 1; and (iii) the limit decision problem asks whether words are accepted with probability arbitrarily close to 1. We unify and generalize several decidability results for probabilistic automata over infinite words, and identify a robust (closed under union and intersection) subclass of probabilistic automata for which all the qualitative decision problems are decidable for parity conditions. We also show that if the input words are restricted to lasso shape (regular) words, then the positive and almost problems are decidable for all probabilistic automata with parity conditions. For most decidable problems we show an optimal PSPACE-complete complexity bound.
AU - Chatterjee, Krishnendu
AU - Tracol, Mathieu
ID - 2957
T2 - Proceedings of the 2012 27th Annual ACM/IEEE Symposium on Logic in Computer Science
TI - Decidable problems for probabilistic automata on infinite words
ER -
TY - JOUR
AB - The activity of hippocampal pyramidal cells reflects both the current position of the animal and information related to its current behavior. Here we investigated whether single hippocampal neurons can encode several independent features defining trials during a memory task. We also tested whether task-related information is represented by partial remapping of the place cell population or, instead, via firing rate modulation of spatially stable place cells. To address these two questions, the activity of hippocampal neurons was recorded in rats performing a conditional discrimination task on a modified T-maze in which the identity of a food reward guided behavior. When the rat was on the central arm of the maze, the firing rate of pyramidal cells changed depending on two independent factors: (1) the identity of the food reward given to the animal and (2) the previous location of the animal on the maze. Importantly, some pyramidal cells encoded information relative to both factors. This trial-type specific and retrospective coding did not interfere with the spatial representation of the maze: hippocampal cells had stable place fields and their theta-phase precession profiles were unaltered during the task, indicating that trial-related information was encoded via rate remapping. During error trials, encoding of both trial-related information and spatial location was impaired. Finally, we found that pyramidal cells also encode trial-related information via rate remapping during the continuous version of the rewarded alternation task without delays. These results suggest that hippocampal neurons can encode several task-related cognitive aspects via rate remapping.
AU - Allen, Kevin
AU - Rawlins, J Nick
AU - Bannerman, David
AU - Csicsvari, Jozsef L
ID - 2958
IS - 42
JF - Journal of Neuroscience
TI - Hippocampal place cells can encode multiple trial-dependent features through rate remapping
VL - 32
ER -
TY - JOUR
AB - We study maximum likelihood estimation in Gaussian graphical models from a geometric point of view. An algebraic elimination criterion allows us to find exact lower bounds on the number of observations needed to ensure that the maximum likelihood estimator (MLE) exists with probability one. This is applied to bipartite graphs, grids and colored graphs. We also study the ML degree, and we present the first instance of a graph for which the MLE exists with probability one, even when the number of observations equals the treewidth.
AU - Uhler, Caroline
ID - 2959
IS - 1
JF - Annals of Statistics
TI - Geometry of maximum likelihood estimation in Gaussian graphical models
VL - 40
ER -
TY - JOUR
AB - The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine learning literature. We consider different types of boosting and compare them to partial least squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the re-introduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θanc = 4 Ne u), and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L2-loss performs best. Applying that method to the ibex data, we estimate θanc ≈ 1.288, and find that most of the variation across loci of the ancestral mutation rate u is between 7.7×10−4 and 3.5×10−3 per locus per generation. The proportion of males with access to matings is estimated to ω ≈ 0.21, which is in good agreement with recent independent estimates.
AU - Aeschbacher, Simon
AU - Beaumont, Mark
AU - Futschik, Andreas
ID - 2962
IS - 3
JF - Genetics
TI - A novel approach for choosing summary statistics in approximate Bayesian computation
VL - 192
ER -