DeepProjection: Rapid and structure-specific projections of tissue sheets embedded in 3D microscopy stacks using deep learning

The efficient extraction of local high-resolution content from massive amounts of imaging data remains a serious and unsolved problem in studies of complex biological tissues. Here we present DeepProjection, a trainable projection algorithm based on deep learning. This algorithm rapidly and robustly extracts image content contained in curved manifolds from time-lapse recorded 3D image stacks by binary masking of background content, stack by stack. The masks calculated for a given movie, when predicted, e.g., on fluorescent cell boundaries on one channel, can subsequently be applied to project other fluorescent channels from the same manifold. We apply DeepProjection to follow the dynamic movements of 2D-tissue sheets in embryonic development. We show that we can selectively project the amnioserosa cell sheet during dorsal closure in Drosophila melanogaster embryos and the periderm layer in the elongating zebrafish embryo while masking highly fluorescent out-of-plane artifacts.

Manifold Calculus in System Theory and Control—Fundamentals and First-Order Systems

The aim of the present tutorial paper is to recall notions from manifold calculus and to illustrate how these tools prove useful in describing system-theoretic properties. Special emphasis is put on embedded manifold calculus (which is coordinate-free and relies on the embedding of a manifold into a larger ambient space). In addition, we also consider the control of non-linear systems whose states belong to curved manifolds. As a case study, synchronization of non-linear systems by feedback control on smooth manifolds (including Lie groups) is surveyed. Special emphasis is also put on numerical methods to simulate non-linear control systems on curved manifolds. The present tutorial is meant to cover a portion of the mentioned topics, such as first-order systems, but it does not cover topics such as covariant derivation and second-order dynamical systems, which will be covered in a subsequent tutorial paper.

Geometric Basis of Action Potential of Skeletal Muscle Cells

Although we know something about single cell neuromuscular junction, It is still mysterious how multiple skeletal muscle cells coordinate to complete the intricate spatial curve movement. Here I propose a hypothesis that skeletal muscle cell populations with action potentials are alligned according to a curved manifolds on space(a curved shape on space) and the skeletal muscle also moves according to this corresponding shape(manifolds) when an specific motor nerve impulses are transmitted. the action potential of motor nerve fibers has the characteristics of time curve manifold and this time manifold curve of motor nerve fibers come from visual cortex in which a spatial geometric manifolds are formed within the synaptic connection of neurons. This spatial geometric manifolds of the synaptic connection of neurons orginate from spatial geometric manifolds in outside nature that are transmitted to brain through the cone cells and ganglion cells of the retina.Further,the essence of life is that life is an object that can move autonomously and the essence of life's autonomous movement is the movement of proteins. theoretically, due to the infinite diversity of geometric manifold shapes in nature, the arrangement and combination of 20 amino acids should have infinite diversity, and the geometric manifold formed by protein three-dimensional spatial structure should also have infinite diversity.

Intrinsic Ultracontractivity for Domains in Negatively Curved Manifolds

Let M be a complete, non-compact, connected Riemannian manifold with Ricci curvature bounded from below by a negative constant. A sufficient condition is obtained for open and connected sets D in M for which the corresponding Dirichlet heat semigroup is intrinsically ultracontractive. That condition is formulated in terms of capacitary width. It is shown that both the reciprocal of the bottom of the spectrum of the Dirichlet Laplacian acting in $$L^2(D)$$ L 2 ( D ) , and the supremum of the torsion function for D are comparable with the square of the capacitary width for D if the latter is sufficiently small. The technical key ingredients are the volume doubling property, the Poincaré inequality and the Li-Yau Gaussian estimate for the Dirichlet heat kernel at finite scale.

Torus actions, maximality, and non-negative curvature

Let ℳ 0 n {\mathcal{M}_{0}^{n}} be the class of closed, simply connected, non-negatively curved Riemannian n-manifolds admitting an isometric, effective, isotropy-maximal torus action. We prove that if M ∈ ℳ 0 n {M\in\mathcal{M}_{0}^{n}} , then M is equivariantly diffeomorphic to the free, linear quotient by a torus of a product of spheres of dimensions greater than or equal to 3. As a special case, we then prove the Maximal Symmetry Rank Conjecture for all M ∈ ℳ 0 n {M\in\mathcal{M}_{0}^{n}} . Finally, we show the Maximal Symmetry Rank Conjecture for simply connected, non-negatively curved manifolds holds for dimensions less than or equal to 9 without additional assumptions on the torus action.