Spreading the spirit of EMBO.

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C∗-algebren mit geordneten ideal folge

AbstractLet K be a separable infinite-dimensional Hilbert space and let J be a countable totally ordered set. Every decomposition of J, J = {i ϵ J ¦i < j} ∪ {i ϵ J ¦i ⩾ j} induces a factorisation of the incomplete tensorproduct H of K over J, H = Hj ⊗ Hj. Let K(H) denote the algebra of all compact operators on H. Then AJ is the C∗-algebra of operators on H generated by 1⊗H(Hj). All ideals of AJ are primitive and form a chain. They correspond to certain cuts in J. In particular, AJ has a maximal (minimal) ideal iff J has a largest (smallest) element. Thus AJ is antiliminal iff J has no smallest element, and AJ is postliminal iff J is well ordered. In particular, Az is antiliminal and all proper quotients of Az are isomorphic postliminal C∗-algebras. The example AN has been considered previously by J. Dixmier

A theory that predicts behaviors of disordered cytoskeletal networks.

Morphogenesis in animal tissues is largely driven by actomyosin networks, through tensions generated by an active contractile process. Although the network components and their properties are known, and networks can be reconstituted in vitro, the requirements for contractility are still poorly understood. Here, we describe a theory that predicts whether an isotropic network will contract, expand, or conserve its dimensions. This analytical theory correctly predicts the behavior of simulated networks, consisting of filaments with varying combinations of connectors, and reveals conditions under which networks of rigid filaments are either contractile or expansile. Our results suggest that pulsatility is an intrinsic behavior of contractile networks if the filaments are not stable but turn over. The theory offers a unifying framework to think about mechanisms of contractions or expansion. It provides the foundation for studying a broad range of processes involving cytoskeletal networks and a basis for designing synthetic networks

Mesoderm migration in Drosophila is a multi-step process requiring FGF signaling and integrin activity

Migration is a complex, dynamic process that has largely been studied using qualitative or static approaches. As technology has improved, we can now take quantitative approaches towards understanding cell migration using in vivo imaging and tracking analyses. In this manner, we have established a four-step model of mesoderm migration during Drosophila gastrulation: (I) mesodermal tube formation, (II) collapse of the mesoderm, (III) dorsal migration and spreading and (IV) monolayer formation. Our data provide evidence that these steps are temporally distinct and that each might require different chemical inputs. To support this, we analyzed the role of fibroblast growth factor (FGF) signaling, in particular the function of two Drosophila FGF ligands, Pyramus and Thisbe, during mesoderm migration. We determined that FGF signaling through both ligands controls movements in the radial direction. Thisbe is required for the initial collapse of the mesoderm onto the ectoderm, whereas both Pyramus and Thisbe are required for monolayer formation. In addition, we uncovered that the GTPase Rap1 regulates radial movement of cells and localization of the beta-integrin subunit, Myospheroid, which is also required for monolayer formation. Our analyses suggest that distinct signals influence particular movements, as we found that FGF signaling is involved in controlling collapse and monolayer formation but not dorsal movement, whereas integrins are required to support monolayer formation only and not earlier movements. Our work demonstrates that complex cell migration is not necessarily a fluid process, but suggests instead that different types of movements are directed by distinct inputs in a stepwise manner

Stateless actor-critic for instance segmentation with high-level priors

Instance segmentation is an important computer vision problem which remains challenging despite impressive recent advances due to deep learning-based methods. Given sufficient training data, fully supervised methods can yield excellent performance, but annotation of ground-truth data remains a major bottleneck, especially for biomedical applications where it has to be performed by domain experts. The amount of labels required can be drastically reduced by using rules derived from prior knowledge to guide the segmentation. However, these rules are in general not differentiable and thus cannot be used with existing methods. Here, we relax this requirement by using stateless actor critic reinforcement learning, which enables non-differentiable rewards. We formulate the instance segmentation problem as graph partitioning and the actor critic predicts the edge weights driven by the rewards, which are based on the conformity of segmented instances to high-level priors on object shape, position or size. The experiments on toy and real datasets demonstrate that we can achieve excellent performance without any direct supervision based only on a rich set of priors

L1-determined ideals in group algebras of exponential Lie groups

A locally compact group $G$ is said to be $\ast$-regular if the natural map \Psi:\Prim C^\ast(G)\to\Prim_{\ast} L^1(G) is a homeomorphism with respect to the Jacobson topologies on the primitive ideal spaces \Prim C^\ast(G) and \Prim_{\ast} L^1(G). In 1980 J. Boidol characterized the $\ast$-regular ones among all exponential Lie groups by a purely algebraic condition. In this article we introduce the notion of $L^1$-determined ideals in order to discuss the weaker property of primitive $\ast$-regularity. We give two sufficient criteria for closed ideals $I$ of $C^\ast(G)$ to be $L^1$-determined. Herefrom we deduce a strategy to prove that a given exponential Lie group is primitive $\ast$-regular. The author proved in his thesis that all exponential Lie groups of dimension $\le 7$ have this property. So far no counter-example is known. Here we discuss the example $G=B_5$, the only critical one in dimension $\le 5$

Insulin and TOR signal in parallel through FOXO and S6K to promote epithelial wound healing

The TOR and Insulin/IGF signalling (IIS) network controls growth, metabolism and ageing. Although reducing TOR or insulin signalling can be beneficial for ageing, it can be detrimental for wound healing, but the reasons for this difference are unknown. Here we show that IIS is activated in the cells surrounding an epidermal wound in Drosophila melanogaster larvae, resulting in PI3K activation and redistribution of the transcription factor FOXO. Insulin and TOR signalling are independently necessary for normal wound healing, with FOXO and S6K as their respective effectors. IIS is specifically required in cells surrounding the wound, and the effect is independent of glycogen metabolism. Insulin signalling is needed for the efficient assembly of an actomyosin cable around the wound, and constitutively active myosin II regulatory light chain suppresses the effects of reduced IIS. These findings may have implications for the role of insulin signalling and FOXO activation in diabetic wound healing

Pseudocompact algebras, profinite groups and class formations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/33410/1/0000811.pd