Congenital adrenal hyperplasia: Diagnostic advances

Summary: Congenital adrenal hyperplasia is a group of autosomal recessive disorders resulting from the deficiency of one of the five enzymes required for the synthesis of cortisol in the adrenal cortex. The most frequent is steroid 21-hydroxylase deficiency, accounting for more than 90% of cases. Much has been learned about the genetics of the various clinical forms of 21-hydroxylase deficiency, and correlations between the genotype and the phenotype have been studied extensively. Gene-specific diagnosis is now feasible and neonatal screening and prenatal treatment have been widely implemented. This discussion will be limited to the most common form of congenital adrenal hyperplasia, with focus on the diagnostic advances in this diseas

Treatment with human growth hormone in patients with Prader-Labhart-Willi syndrome reduces body fat and increases muscle mass and physical performance

Twelve children with documented Prader-Labhart-Willi syndrome were treated with human growth hormone (24 U/m2/week) during 1 year. The children were divided into three groups: group 1: overweight and prepubertal (n = 6, age 3.8-7.0 years); group 2: underweight and prepubertal (n = 3, age 0.6-4.1 years); group 3: pubertal (n = 3, age 9.2-14.6 years). In group 1, height increased from -1.7 SD to -0.6 SD, while weight decreased from 1.1 SD to 0.4 SD, with a dramatic drop in weight for height from 3.8 SD to 1.2 SD. Hand length increased from -1.5 SD to -0.4 SD and foot length from -2.5 SD to -1.4 SD. Body fat, measured by dual X-ray energy absorptiometry, dropped by a third, whereas muscle mass increased by a fourth. Physical capability (Wingate test) improved considerably. The children were reported to be much more active and capable. In group 2, similar changes were seen, but weight for height increased, probably because muscle mass increase exceeded fat mass decrease. Changes in group 3 were similar as in group 1, even though far less distinct. Conclusion: Growth hormone treatment in Prader-Labhart-Willi syndrome led to dramatic changes: distinct increase in growth velocity, height and muscle mass, as well as an improvement in physical performance. Fat mass and weight for height decreased in the initially overweight children, and weight for height increased in underweight childre

Coherent states on spheres

We describe a family of coherent states and an associated resolution of the identity for a quantum particle whose classical configuration space is the d-dimensional sphere S^d. The coherent states are labeled by points in the associated phase space T*(S^d). These coherent states are NOT of Perelomov type but rather are constructed as the eigenvectors of suitably defined annihilation operators. We describe as well the Segal-Bargmann representation for the system, the associated unitary Segal-Bargmann transform, and a natural inversion formula. Although many of these results are in principle special cases of the results of B. Hall and M. Stenzel, we give here a substantially different description based on ideas of T. Thiemann and of K. Kowalski and J. Rembielinski. All of these results can be generalized to a system whose configuration space is an arbitrary compact symmetric space. We focus on the sphere case in order to be able to carry out the calculations in a self-contained and explicit way.Comment: Revised version. Submitted to J. Mathematical Physic

Unsupervised classemes

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33885-4_41Proceedings of Information Fusion in Computer Vision for Concept Recognition at the ECCV 2012In this paper we present a new model of semantic features that, unlike previously presented methods, does not rely on the presence of a labeled training data base, as the creation of the feature extraction function is done in an unsupervised manner. We test these features on an unsupervised classification (clustering) task, and show that they outperform primitive (low-level) features, and that have performance comparable to that of supervised semantic features, which are much more expensive to determine relying on the presence of a labeled training set to train the feature extraction function

Iron oxide-based nanostructured ceramics with tailored magnetic and mechanical properties: Development of mechanically robust, bulk superparamagnetic materials

Nanostructured iron-oxide based materials with tailored mechanical and magnetic behavior are produced in bulk form. By applying ultra-fast heating routines via spark plasma sintering (SPS) to supercrystalline pellets, materials with an enhanced combination of elastic modulus, hardness and saturation magnetization are achieved. Supercrystallinity-namely the arrangement of the constituent nanoparticles into periodic structures-is achieved through self-assembly of the organically-functionalized iron oxide nanoparticles. The optimization of the following SPS regime allows the control of organics' removal, necking, iron oxide phase transformations and nano-grain size retention, and thus the fine-tuning of both mechanical properties and magnetic response, up until the production of bulk mm-size superparamagnetic materials.Deusche Forschungsgemeinschaft (DFG

Carotid Baroreflex Activation: Past, Present, and Future

Electrical activation of the carotid baroreceptor system is an attractive therapy for the treatment of resistant hypertension. In the past, several attempts were made to directly activate the baroreceptor system in humans, but the method had to be restricted to a few selected patients. Adverse effects, the need for better electrical devices and better surgical techniques, and the lack of knowledge about long-term effects has greatly hampered developments in this area for many years. Recently, a new and promising device was evaluated in a multicenter feasibility trial, which showed a clinically and statistically significant reduction in office systolic blood pressure (>20 mm Hg). This reduction could be sustained for at least 2 years with an acceptable safety profile. In the future, this new device may stimulate further application of electrical activation of the carotid baroreflex in treatment-resistant hypertension

Adaptively Transforming Graph Matching

Recently, many graph matching methods that incorporate pairwise constraint and that can be formulated as a quadratic assignment problem (QAP) have been proposed. Although these methods demonstrate promising results for the graph matching problem, they have high complexity in space or time. In this paper, we introduce an adaptively transforming graph matching (ATGM) method from the perspective of functional representation. More precisely, under a transformation formulation, we aim to match two graphs by minimizing the discrepancy between the original graph and the transformed graph. With a linear representation map of the transformation, the pairwise edge attributes of graphs are explicitly represented by unary node attributes, which enables us to reduce the space and time complexity significantly. Due to an efficient Frank-Wolfe method-based optimization strategy, we can handle graphs with hundreds and thousands of nodes within an acceptable amount of time. Meanwhile, because transformation map can preserve graph structures, a domain adaptation-based strategy is proposed to remove the outliers. The experimental results demonstrate that our proposed method outperforms the state-of-the-art graph matching algorithms

Simultaneous Optimization of Both Node and Edge Conservation in Network Alignment via WAVE

Network alignment can be used to transfer functional knowledge between conserved regions of different networks. Typically, existing methods use a node cost function (NCF) to compute similarity between nodes in different networks and an alignment strategy (AS) to find high-scoring alignments with respect to the total NCF over all aligned nodes (or node conservation). But, they then evaluate quality of their alignments via some other measure that is different than the node conservation measure used to guide the alignment construction process. Typically, one measures the amount of conserved edges, but only after alignments are produced. Hence, a recent attempt aimed to directly maximize the amount of conserved edges while constructing alignments, which improved alignment accuracy. Here, we aim to directly maximize both node and edge conservation during alignment construction to further improve alignment accuracy. For this, we design a novel measure of edge conservation that (unlike existing measures that treat each conserved edge the same) weighs each conserved edge so that edges with highly NCF-similar end nodes are favored. As a result, we introduce a novel AS, Weighted Alignment VotEr (WAVE), which can optimize any measures of node and edge conservation, and which can be used with any NCF or combination of multiple NCFs. Using WAVE on top of established state-of-the-art NCFs leads to superior alignments compared to the existing methods that optimize only node conservation or only edge conservation or that treat each conserved edge the same. And while we evaluate WAVE in the computational biology domain, it is easily applicable in any domain.Comment: 12 pages, 4 figure

Diario oficial del Ministerio de Marina: Año LI Número 49 - 1958 febrero 28

Trabajo presentado a la 13th Asian Conference on Computer Vision (ACCV), celebrada en Taipei (Taiwan) del 20 al 24 de noviembre de 2016.In recent years, there has been a growing interest on tackling the Non-Rigid Structure from Motion problem (NRSfM), where the shape of a deformable object and the pose of a moving camera are simultaneously estimated from a monocular video sequence. Existing solutions are limited to single objects and continuous, smoothly changing sequences. In this paper we extend NRSfM to a multi-instance domain, in which the images do not need to have temporal consistency, allowing for instance, to jointly reconstruct the face of multiple persons from an unordered list of images. For this purpose, we present a new formulation of the problem based on a dual low-rank shape representation, that simultaneously captures the between- and within-individual deformations. The parameters of this model are learned using a variant of the probabilistic linear discriminant analysis that requires consecutive batches of expectation and maximization steps. The resulting approach estimates 3D deformable shape and pose of multiple instances from only 2D point observations on a collection images, without requiring pre-trained 3D data, and is shown to be robust to noisy measurements and missing points. We provide quantitative and qualitative evaluation on both synthetic and real data, and show consistent benefits compared to current state of the art.This work has been partially supported by the Spanish Ministry of Science and Innovation under project RobInstruct TIN2014-58178-R; by the ERA-net CHISTERA projects VISEN PCIN-2013-047 and I-DRESS PCIN-2015-147.Peer Reviewe