Mutations in Thyroid Hormone Beta Receptor Gene Identified in Children with Clinical Resistance to Thyroid Hormones

Introduction: Patients with resistance to thyroid hormones(RTH) show different clinical features. Several mutations have been identified in them.Objective:To describe patients followed up since 2006 with RTH suspicion evaluated for mutations in thyroid hormone beta receptor(THRß)gene.Methods:Children were followed up in our Endocrinology Department.Patient 1:10-yr-old boy with elevated T3, T4 and free T4, normal TSH in routine thyroid testing requested for overweight. Patient 2:0.7-yr- old boy with Down syndrome and elevated T3, T4 and free T4, normal TSH.Patient 3:Boy with abnormal results on neonatal screening, with elevated T3, T4, free T4 and TSH.Patient 4:4.7?yr-old girl with elevated T3, T4 and free T4, normal TSH in routine thyroid testing requested for low weight.Patient 5: 1-yr- old boy with elevated T3, T4 and free T4, normal TSH in routine thyroid testing requested for low weight.Patient 6:Boy with congenital hypothyroidism diagnosed by screening with elevated T3, T4, free T4 and TSH.Clinical manifestations:Patients 1, 4 and 5 showed palpitations, tachycardia.Familial antecedents: Patient 3 has two brothers with similar RTH profile. Patient 4 had a sister who died at 3 months of age and mother with confirmed RTH. Patient 6 had an aunt with RTH profile.Thyroid ultrasound. All patients had normal gland size except patient 6 who had an hypoplastic gland. Patient 4 showed goiter at follow up.Treatment:Patient 1 received metimazol; patients 1,4 and 5 beta blockers and patient 6 levothyroxine.Molecular biology analysis: genomic DNA was isolated from blood cells and the exons 7-10 of the THRß gene, including the flanking intronic regions were amplified by PCR. DNA sequences from each amplified fragment were performed with the Taq polymerase-based chain terminator method and using the specific forward and reverse THRß primers. Results.Direct sequence analysis revealed a novel missense mutation in exon 10 in patient 3, c.1329G>T transvertion that results in a p.K443N substitution and two known missense mutations: c.1357C>A, p.P453T (Patient 1)in exon 10 and c.949G>A, p.A317T (Patient 4) in exon 9.Conclusion:THRß gene mutations were found in half of the patients with RTH, including a new mutation.Although goiter is a common feature in RTH, only one patient presented it.These findings support the importance of searching THRßgene mutations in suspected individuals to achieve an adequate follow-up and treatment in patients with RHT.Fil: Gonzáles, Viviana. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Balbi, Viviana A.. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Morin, Analía. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Reinoso, Andrea. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Vitale, Laura. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Ricci, Jaime. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Espósito, Mariela. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Martín, Rodrigo. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Tournier, Andrea L.. Provincia de Buenos Aires. Ministerio de Salud. Hospital de Niños "Sor María Ludovica" de La Plata; ArgentinaFil: Adrover, Ezequiela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Molina, Maricel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Targovnik, Hector Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaFil: Rivolta, Carina Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología y Biotecnología; ArgentinaXXVIII Congreso Latinoamericano de Endocrinología PediátricaFlorianópolisBrasilSociedad Latinoamericana de Endocrinología Pediátric

Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks

The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most existing continual learning algorithms for deep artificial and spiking neural networks are unable to adequately auto-regulate the limited resources in the network, which leads to performance drop along with energy consumption rise as the increase of tasks. In this paper, we propose a brain-inspired continual learning algorithm with adaptive reorganization of neural pathways, which employs Self-Organizing Regulation networks to reorganize the single and limited Spiking Neural Network (SOR-SNN) into rich sparse neural pathways to efficiently cope with incremental tasks. The proposed model demonstrates consistent superiority in performance, energy consumption, and memory capacity on diverse continual learning tasks ranging from child-like simple to complex tasks, as well as on generalized CIFAR100 and ImageNet datasets. In particular, the SOR-SNN model excels at learning more complex tasks as well as more tasks, and is able to integrate the past learned knowledge with the information from the current task, showing the backward transfer ability to facilitate the old tasks. Meanwhile, the proposed model exhibits self-repairing ability to irreversible damage and for pruned networks, could automatically allocate new pathway from the retained network to recover memory for forgotten knowledge

Shades of Belonging

Examines data from the 2000 Census and information from surveys and focus groups conducted by the center to look at how Hispanics view their racial identities

Supremum-Norm Convergence for Step-Asynchronous Successive Overrelaxation on M-matrices

Step-asynchronous successive overrelaxation updates the values contained in a single vector using the usual Gau\ss-Seidel-like weighted rule, but arbitrarily mixing old and new values, the only constraint being temporal coherence: you cannot use a value before it has been computed. We show that given a nonnegative real matrix $A$, a $\sigma\geq\rho(A)$ and a vector $\boldsymbol w>0$ such that $A\boldsymbol w\leq\sigma\boldsymbol w$, every iteration of step-asynchronous successive overrelaxation for the problem $(sI- A)\boldsymbol x=\boldsymbol b$, with $s >\sigma$, reduces geometrically the $\boldsymbol w$-norm of the current error by a factor that we can compute explicitly. Then, we show that given a $\sigma>\rho(A)$ it is in principle always possible to compute such a $\boldsymbol w$. This property makes it possible to estimate the supremum norm of the absolute error at each iteration without any additional hypothesis on $A$, even when $A$ is so large that computing the product $A\boldsymbol x$ is feasible, but estimating the supremum norm of $(sI-A)^{-1}$ is not

Non-axisymmetric baby-skyrmion branes

We investigate the existence of non axisymmetric solutions in the 6-dimensional baby-Skyrme brane model. The brane is described by a localized solution to the baby-Skyrme model extending in the extra dimensions. Such non symmetric branes have already been constructed in the original 2+1-dimensional baby-Skyrme model in flat space. We generalize this result to the case of gravitating baby-Skyrme and in the context of extradimensions. These non-trivial deformation from the axisymmetric shape appear for higher values of the topological charge, so we consider the cases of $B=3,4$, where $B$ is the topological charge. We solve the coupled system of the Einstein and baby-Skyrme equations by successive over relaxation method. We argue that the result may be a possible resolution for the fermion mass hierarchy puzzle.Comment: 14 pages, 14 figure

The Hanle Effect in 1D, 2D and 3D

This paper addresses the problem of scattering line polarization and the Hanle effect in one-dimensional (1D), two-dimensional (2D) and three-dimensional (3D) media for the case of a two-level model atom without lower-level polarization and assuming complete frequency redistribution. The theoretical framework chosen for its formulation is the QED theory of Landi Degl'Innocenti (1983), which specifies the excitation state of the atoms in terms of the irreducible tensor components of the atomic density matrix. The self-consistent values of these density-matrix elements is to be determined by solving jointly the kinetic and radiative transfer equations for the Stokes parameters. We show how to achieve this by generalizing to Non-LTE polarization transfer the Jacobi-based ALI method of Olson et al. (1986) and the iterative schemes based on Gauss-Seidel iteration of Trujillo Bueno and Fabiani Bendicho (1995). These methods essentially maintain the simplicity of the Lambda-iteration method, but their convergence rate is extremely high. Finally, some 1D and 2D model calculations are presented that illustrate the effect of horizontal atmospheric inhomogeneities on magnetic and non-magnetic resonance line polarization signals.Comment: 14 pages and 5 figure

Approximate Quantile Computation over Sensor Networks

Sensor networks have been deployed in various environments, from battle field surveillance to weather monitoring. The amount of data generated by the sensors can be large. One way to analyze such large data set is to capture the essential statistics of the data. Thus the quantile computation in the large scale sensor network becomes an important but challenging problem. The data may be widely distributed, e.g., there may be thousands of sensors. In addition, the memory and bandwidth among sensors could be quite limited. Most previous quantile computation methods assume that the data is either stored or streaming in a centralized site, which could not be directly applied in the sensor environment. In this paper, we propose a novel algorithm to compute the quantile for sensor network data, which dynamically adapts to the memory limitations. Moreover, since sensors may update their values at any time, an incremental maintenance algorithm is developed to reduce the number of times that a global recomputation is needed upon updates. The performance and complexity of our algorithms are analyzed both theoretically and empirically on various large data sets, which demonstrate the high promise of our method