TY - JOUR
AB - Spontaneous emission spectra of two initially excited closely spaced identical atoms are very sensitive to the strength and the direction of the applied magnetic field. We consider the relevant schemes that ensure the determination of the mutual spatial orientation of the atoms and the distance between them by entirely optical means. A corresponding theoretical description is given accounting for the dipole-dipole interaction between the two atoms in the presence of a magnetic field and for polarizations of the quantum field interacting with magnetic sublevels of the two-atom system.
AU - Redchenko, Elena
AU - Makarov, Alexander
AU - Yudson, Vladimir
ID - 307
IS - 4
JF - Physical Review A - Atomic, Molecular, and Optical Physics
TI - Nanoscopy of pairs of atoms by fluorescence in a magnetic field
VL - 97
ER -
TY - JOUR
AB - Migrating cells penetrate tissue barriers during development, inflammatory responses, and tumor metastasis. We study if migration in vivo in such three-dimensionally confined environments requires changes in the mechanical properties of the surrounding cells using embryonic Drosophila melanogaster hemocytes, also called macrophages, as a model. We find that macrophage invasion into the germband through transient separation of the apposing ectoderm and mesoderm requires cell deformations and reductions in apical tension in the ectoderm. Interestingly, the genetic pathway governing these mechanical shifts acts downstream of the only known tumor necrosis factor superfamily member in Drosophila, Eiger, and its receptor, Grindelwald. Eiger-Grindelwald signaling reduces levels of active Myosin in the germband ectodermal cortex through the localization of a Crumbs complex component, Patj (Pals-1-associated tight junction protein). We therefore elucidate a distinct molecular pathway that controls tissue tension and demonstrate the importance of such regulation for invasive migration in vivo.
AU - Ratheesh, Aparna
AU - Biebl, Julia
AU - Smutny, Michael
AU - Veselá, Jana
AU - Papusheva, Ekaterina
AU - Krens, Gabriel
AU - Kaufmann, Walter
AU - György, Attila
AU - Casano, Alessandra M
AU - Siekhaus, Daria E
ID - 308
IS - 3
JF - Developmental Cell
TI - Drosophila TNF modulates tissue tension in the embryo to facilitate macrophage invasive migration
VL - 45
ER -
TY - CONF
AB - We present an efficient algorithm for a problem in the interface between clustering and graph embeddings. An embedding ' : G ! M of a graph G into a 2manifold M maps the vertices in V (G) to distinct points and the edges in E(G) to interior-disjoint Jordan arcs between the corresponding vertices. In applications in clustering, cartography, and visualization, nearby vertices and edges are often bundled to a common node or arc, due to data compression or low resolution. This raises the computational problem of deciding whether a given map ' : G ! M comes from an embedding. A map ' : G ! M is a weak embedding if it can be perturbed into an embedding ψ: G ! M with k' "k < " for every " > 0. A polynomial-time algorithm for recognizing weak embeddings was recently found by Fulek and Kyncl [14], which reduces to solving a system of linear equations over Z2. It runs in O(n2!) O(n4:75) time, where 2:373 is the matrix multiplication exponent and n is the number of vertices and edges of G. We improve the running time to O(n log n). Our algorithm is also conceptually simpler than [14]: We perform a sequence of local operations that gradually "untangles" the image '(G) into an embedding (G), or reports that ' is not a weak embedding. It generalizes a recent technique developed for the case that G is a cycle and the embedding is a simple polygon [1], and combines local constraints on the orientation of subgraphs directly, thereby eliminating the need for solving large systems of linear equations.
AU - Akitaya, Hugo
AU - Fulek, Radoslav
AU - Tóth, Csaba
ID - 309
TI - Recognizing weak embeddings of graphs
ER -
TY - JOUR
AB - Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network and, thus, depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes in the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer population models of interacting neurons that collectively encode stimulus information. The key to disentangling intrinsic from extrinsic correlations is to infer the couplings between neurons separately from the encoding model and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach in retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus.
AU - Ferrari, Ulisse
AU - Deny, Stephane
AU - Chalk, Matthew J
AU - Tkacik, Gasper
AU - Marre, Olivier
AU - Mora, Thierry
ID - 31
IS - 4
JF - Physical Review E
SN - 24700045
TI - Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons
VL - 98
ER -
TY - CONF
AB - A model of computation that is widely used in the formal analysis of reactive systems is symbolic algorithms. In this model the access to the input graph is restricted to consist of symbolic operations, which are expensive in comparison to the standard RAM operations. We give lower bounds on the number of symbolic operations for basic graph problems such as the computation of the strongly connected components and of the approximate diameter as well as for fundamental problems in model checking such as safety, liveness, and coliveness. Our lower bounds are linear in the number of vertices of the graph, even for constant-diameter graphs. For none of these problems lower bounds on the number of symbolic operations were known before. The lower bounds show an interesting separation of these problems from the reachability problem, which can be solved with O(D) symbolic operations, where D is the diameter of the graph. Additionally we present an approximation algorithm for the graph diameter which requires Õ(n/D) symbolic steps to achieve a (1 +ϵ)-approximation for any constant > 0. This compares to O(n/D) symbolic steps for the (naive) exact algorithm and O(D) symbolic steps for a 2-approximation. Finally we also give a refined analysis of the strongly connected components algorithms of [15], showing that it uses an optimal number of symbolic steps that is proportional to the sum of the diameters of the strongly connected components.
AU - Chatterjee, Krishnendu
AU - Dvorák, Wolfgang
AU - Henzinger, Monika
AU - Loitzenbauer, Veronika
ID - 310
TI - Lower bounds for symbolic computation on graphs: Strongly connected components, liveness, safety and diameter
ER -