Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration

We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approach consists of two sequential networks with a specifically designed loss function to address the challenges in 3D first trimester ultrasound. The first part learns the affine transformation and the second part learns the voxelwise nonrigid deformation between the target image and the atlas. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challenges present in 3D first trimester ultrasound. The method was tested on a dataset of human embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed alignment of the brain in some cases and gave insight in open challenges for the proposed method. We conclude that our method is a promising approach towards fully automated spatial alignment and segmentation of embryonic brains in 3D ultrasound

Stein structures: existence and flexibility

This survey on the topology of Stein manifolds is an extract from our recent joint book. It is compiled from two short lecture series given by the first author in 2012 at the Institute for Advanced Study, Princeton, and the Alfred Renyi Institute of Mathematics, Budapest.Comment: 29 pages, 11 figure

Caltech Faint Galaxy Redshift Survey X: A Redshift Survey in the Region of the Hubble Deep Field North

A redshift survey has been carried out in the region of the Hubble Deep Field North using the Low Resolution Imaging Spectrograph at the Keck Observatory. The resulting redshift catalog, which contains 671 entries, is a compendium of our own data together with published LRIS/Keck data. It is more than 92% complete for objects, irrespective of morphology, to $R = 24$ mag in the HDF itself and to $R = 23$ mag in the Flanking Fields within a diameter of 8 arcmin centered on the HDF, an unusually high completion for a magnitude limited survey performed with a large telescope. A median redshift $z = 1.0$ is reached at $R \sim 23.8$. Strong peaks in the redshift distribution, which arise when a group or poor cluster of galaxies intersect the area surveyed, can be identified to $z \sim 1.2$ in this dataset. More than 68% of the galaxies are members of these redshift peaks. In a few cases, closely spaced peaks in $z$ can be resolved into separate groups of galaxies that can be distinguished in both velocity and location on the sky. The radial separation of these peaks in the pencil-beam survey is consistent with a characteristic length scale for the their separation of $\approx$70 Mpc in our adopted cosmology ($h = 0.6, \Omega_M = 0.3$, $\Lambda = 0$). Strong galaxy clustering is in evidence at all epochs back to $z \le 1.1$. (abstract abridged)Comment: Accepted to the ApJ. This version contains all the figures and tables. 2 minor typos in table 2b correcte

RGD-containing Peptides Inhibit Fibrinogen Binding to Platelet αIIbβ3 by Inducing an Allosteric Change in the Amino-terminal Portion of αIIb

To determine the molecular basis for the insensitivity of rat alpha(IIb)beta(3) to inhibition by RGD-containing peptides, hybrids of human and rat alpha(IIb)beta(3) and chimeras of alpha(IIb)beta(3) in which alpha(IIb) was composed of portions of human and rat alpha(IIb) were expressed in Chinese hamster ovary cells and B lymphocytes, and the ability of the tetrapeptide RGDS to inhibit fibrinogen binding to the various forms of alpha(IIb)beta(3) was measured. These measurements indicated that sequences regulating the sensitivity of alpha(IIb)beta(3) to RGDS are located in the seven amino-terminal repeats of alpha(IIb). Moreover, replacing the first three or four (but not the first two) repeats of rat alpha(IIb) with the corresponding human sequences enhanced sensitivity to RGDS, whereas replacing the first two or three repeats of human alpha(IIb) with the corresponding rat sequences had little or no effect. Nevertheless, RGDS bound to Chinese hamster ovary cells expressing alpha(IIb)beta(3) regardless whether the alpha(IIb) in the heterodimers was human, rat, or a rat-human chimera. These results indicate that the sequences determining the sensitivity of alpha(IIb)beta(3) to RGD-containing peptides are located in the third and fourth amino-terminal repeats of alpha(IIb). Because RGDS binds to both human and rat alpha(IIb)beta(3), the results suggest that differences in RGDS sensitivity result from differences in the allosteric changes induced in these repeats following RGDS binding

On the cohomology of pseudoeffective line bundles

The goal of this survey is to present various results concerning the cohomology of pseudoeffective line bundles on compact K{\"a}hler manifolds, and related properties of their multiplier ideal sheaves. In case the curvature is strictly positive, the prototype is the well known Nadel vanishing theorem, which is itself a generalized analytic version of the fundamental Kawamata-Viehweg vanishing theorem of algebraic geometry. We are interested here in the case where the curvature is merely semipositive in the sense of currents, and the base manifold is not necessarily projective. In this situation, one can still obtain interesting information on cohomology, e.g. a Hard Lefschetz theorem with pseudoeffective coefficients, in the form of a surjectivity statement for the Lefschetz map. More recently, Junyan Cao, in his PhD thesis defended in Grenoble, obtained a general K{\"a}hler vanishing theorem that depends on the concept of numerical dimension of a given pseudoeffective line bundle. The proof of these results depends in a crucial way on a general approximation result for closed (1,1)-currents, based on the use of Bergman kernels, and the related intersection theory of currents. Another important ingredient is the recent proof by Guan and Zhou of the strong openness conjecture. As an application, we discuss a structure theorem for compact K{\"a}hler threefolds without nontrivial subvarieties, following a joint work with F.Campana and M.Verbitsky. We hope that these notes will serve as a useful guide to the more detailed and more technical papers in the literature; in some cases, we provide here substantially simplified proofs and unifying viewpoints.Comment: 39 pages. This survey is a written account of a lecture given at the Abel Symposium, Trondheim, July 201

Learning Biomarker Models for Progression Estimation of Alzheimer’s Disease

Being able to estimate a patient’s progress in the course of Alzheimer’s disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and—employing cognitive scores and image-based biomarkers—real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression