Ataxia telangiectasia

Review on Ataxia telangiectasia, with data on clinics, and the genes involved

ATM (ataxia telangiectasia mutated)

Review on ATM (ataxia telangiectasia mutated), with data on DNA, on the protein encoded, and where the gene is implicated

Nijmegen breakage syndrome

Review on Nijmegen breakage syndrome, with data on clinics, and the genes involved

NBS1 (Nijmegen breakage syndrome 1)

Review on NBS1 (Nijmegen breakage syndrome 1), with data on DNA, on the protein encoded, and where the gene is implicated

Unsupervised Learning of Category-Specific Symmetric 3D Keypoints from Point Sets

Automatic discovery of category-specific 3D keypoints from a collection of objects of a category is a challenging problem. The difficulty is added when objects are represented by 3D point clouds, with variations in shape and semantic parts and unknown coordinate frames. We define keypoints to be category-specific, if they meaningfully represent objects’ shape and their correspondences can be simply established order-wise across all objects. This paper aims at learning such 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category. In order to do so, we model shapes defined by the keypoints, within a category, using the symmetric linear basis shapes without assuming the plane of symmetry to be known. The usage of symmetry prior leads us to learn stable keypoints suitable for higher misalignments. To the best of our knowledge, this is the first work on learning such keypoints directly from 3D point clouds for a general category. Using objects from four benchmark datasets, we demonstrate the quality of our learned keypoints by quantitative and qualitative evaluations. Our experiments also show that the keypoints discovered by our method are geometrically and semantically consistent

Allocation of nutrients during the reproductive cycle of Ophidiaster ophidianus (Echinodermata: Asteroidea)

Copyright © 2011 Taylor & Francis.The reproductive cycle of Ophidiaster ophidianus (strictly protected status) from Sa˜o Miguel Island, in the Azorean Archipelago was studied. The reproductive strategy; the energy allocation of each sex during the reproductive cycle and the nutritional condition of the population were analyzed. Gonadal index (GI) showed a clear seasonal pattern with spawning between August and October but histological examination revealed that gamete release can occur throughout the entire year. The pyloric caeca index (PCI) showed little annual variation but with an inverse relationship with the GI. Allocation of energy to the gonads and to the pyloric caeca reflected the seasonal reproductive strategy of this species. Individuals were able to simultaneously develop gonads, pyloric caeca, and quickly regenerate lost arms. There was a major expenditure of energy by females compared to males but, sexual size dimorphism was not observed. The reproductive pattern observed in O. ophidianus combining rich food availability and seawater temperatures characteristic of a temperate zone may be the key to the success of this species in the Azorean oceanic Island.Portuguese Foundation for Science and Technology (FCT)

Calibration of the charge and energy loss per unit length of the MicroBooNE liquid argon time projection chamber using muons and protons

We describe a method used to calibrate the position- and time-dependent response of the MicroBooNE liquid argon time projection chamber anode wires to ionization particle energy loss. The method makes use of crossing cosmic-ray muons to partially correct anode wire signals for multiple effects as a function of time and position, including cross-connected TPC wires, space charge effects, electron attachment to impurities, diffusion, and recombination. The overall energy scale is then determined using fully-contained beam-induced muons originating and stopping in the active region of the detector. Using this method, we obtain an absolute energy scale uncertainty of 2% in data. We use stopping protons to further refine the relation between the measured charge and the energy loss for highly-ionizing particles. This data-driven detector calibration improves both the measurement of total deposited energy and particle identification based on energy loss per unit length as a function of residual range. As an example, the proton selection efficiency is increased by 2% after detector calibration

Reconstruction and measurement of (100) MeV energy electromagnetic activity from π0 arrow γγ decays in the MicroBooNE LArTPC

We present results on the reconstruction of electromagnetic (EM) activity from photons produced in charged current νμ interactions with final state π0s. We employ a fully-automated reconstruction chain capable of identifying EM showers of (100) MeV energy, relying on a combination of traditional reconstruction techniques together with novel machine-learning approaches. These studies demonstrate good energy resolution, and good agreement between data and simulation, relying on the reconstructed invariant π0 mass and other photon distributions for validation. The reconstruction techniques developed are applied to a selection of νμ + Ar → μ + π0 + X candidate events to demonstrate the potential for calorimetric separation of photons from electrons and reconstruction of π0 kinematics

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies