The ASTRO-H X-ray Observatory

The joint JAXA/NASA ASTRO-H mission is the sixth in a series of highly successful X-ray missions initiated by the Institute of Space and Astronautical Science (ISAS). ASTRO-H will investigate the physics of the high-energy universe via a suite of four instruments, covering a very wide energy range, from 0.3 keV to 600 keV. These instruments include a high-resolution, high-throughput spectrometer sensitive over 0.3-2 keV with high spectral resolution of Delta E < 7 eV, enabled by a micro-calorimeter array located in the focal plane of thin-foil X-ray optics; hard X-ray imaging spectrometers covering 5-80 keV, located in the focal plane of multilayer-coated, focusing hard X-ray mirrors; a wide-field imaging spectrometer sensitive over 0.4-12 keV, with an X-ray CCD camera in the focal plane of a soft X-ray telescope; and a non-focusing Compton-camera type soft gamma-ray detector, sensitive in the 40-600 keV band. The simultaneous broad bandpass, coupled with high spectral resolution, will enable the pursuit of a wide variety of important science themes.Comment: 22 pages, 17 figures, Proceedings of the SPIE Astronomical Instrumentation "Space Telescopes and Instrumentation 2012: Ultraviolet to Gamma Ray

Hitomi (ASTRO-H) X-ray Astronomy Satellite

The Hitomi (ASTRO-H) mission is the sixth Japanese x-ray astronomy satellite developed by a large international collaboration, including Japan, USA, Canada, and Europe. The mission aimed to provide the highest energy resolution ever achieved at E  >  2  keV, using a microcalorimeter instrument, and to cover a wide energy range spanning four decades in energy from soft x-rays to gamma rays. After a successful launch on February 17, 2016, the spacecraft lost its function on March 26, 2016, but the commissioning phase for about a month provided valuable information on the onboard instruments and the spacecraft system, including astrophysical results obtained from first light observations. The paper describes the Hitomi (ASTRO-H) mission, its capabilities, the initial operation, and the instruments/spacecraft performances confirmed during the commissioning operations for about a month

Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images

We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detection could benefit from convolutional neural networks because their visual recognition ability is comparable to that of humans. We explored image-based seizure detection by applying convolutional neural networks to long-term EEG that included epileptic seizures. After filtering, EEG data were divided into short segments based on a given time window and converted into plot EEG images, each of which was classified by convolutional neural networks as ‘seizure’ or ‘non-seizure’. These resultant labels were then used to design a clinically practical index for seizure detection. The best true positive rate was obtained using a 1-s time window. The median true positive rate of convolutional neural networks labelling by seconds was 74%, which was higher than that of commercially available seizure detection software (20% by BESA and 31% by Persyst). For practical use, the median of detected seizure rate by minutes was 100% by convolutional neural networks, which was higher than the 73.3% by BESA and 81.7% by Persyst. The false alarm of convolutional neural networks' seizure detection was issued at 0.2 per hour, which appears acceptable for clinical practice. Moreover, we demonstrated that seizure detection improved when training was performed using EEG patterns similar to those of testing data, suggesting that adding a variety of seizure patterns to the training dataset will improve our method. Thus, artificial visual recognition by convolutional neural networks allows for seizure detection, which otherwise currently relies on skillful visual inspection by expert epileptologists during clinical diagnosis. Keywords: Convolutional neural networks, Seizure detection, Deep learning, Scalp electroencephalogram, Epileptic seizur

Preparation of Polymeric Adsorbents Bearing Diglycolamic Acid Ligands for Rare Earth Elements

We synthesized polymeric adsorbents modified with diglycolamic acid ligands for the recovery of rare earth elements. Styrene, divinylbenzene, and glycidyl methacrylate were copolymerized by suspension polymerization in the presence of diluent mixtures of heptane and toluene. Varying the composition of the diluent mixtures changed the pore characteristics of the polymeric particles; the highest specific surface area (51.2 m²/g) was obtained with mixtures of equal volumes of heptane and toluene. Polymeric adsorbents were prepared by functionalizing the synthesized polymeric particles with amino groups and then diglycolamic acid ligands. The content of the amino groups was almost constant, whereas that of the diglycolamic acid ligands depended on the specific surface area of the adsorbents. The synthesized polymeric adsorbents selectively adsorbed rare earth elements from a solution containing rare earth and base metal ions. The high adsorption rate of the adsorbents was due to their large specific surface area

Large-scale collection and annotation of full-length enriched cDNAs from a model halophyte, <it>Thellungiella halophila</it>

<p>Abstract</p> <p>Background</p> <p><it>Thellungiella halophila </it>(also known as <it>Thellungiella salsuginea</it>) is a model halophyte with a small plant size, short life cycle, and small genome. It easily undergoes genetic transformation by the floral dipping method used with its close relative, <it>Arabidopsis thaliana</it>. <it>Thellungiella </it>genes exhibit high sequence identity (approximately 90% at the cDNA level) with Arabidopsis genes. Furthermore, <it>Thellungiella </it>not only shows tolerance to extreme salinity stress, but also to chilling, freezing, and ozone stress, supporting the use of <it>Thellungiella </it>as a good genomic resource in studies of abiotic stress tolerance.</p> <p>Results</p> <p>We constructed a full-length enriched <it>Thellungiella </it>(Shan Dong ecotype) cDNA library from various tissues and whole plants subjected to environmental stresses, including high salinity, chilling, freezing, and abscisic acid treatment. We randomly selected about 20 000 clones and sequenced them from both ends to obtain a total of 35 171 sequences. CAP3 software was used to assemble the sequences and cluster them into 9569 nonredundant cDNA groups. We named these cDNAs "RTFL" (RIKEN <it>Thellungiella </it>Full-Length) cDNAs. Information on functional domains and Gene Ontology (GO) terms for the RTFL cDNAs were obtained using InterPro. The 8289 genes assigned to InterPro IDs were classified according to the GO terms using Plant GO Slim. Categorical comparison between the whole Arabidopsis genome and <it>Thellungiella </it>genes showing low identity to Arabidopsis genes revealed that the population of <it>Thellungiella </it>transport genes is approximately 1.5 times the size of the corresponding Arabidopsis genes. This suggests that these genes regulate a unique ion transportation system in <it>Thellungiella</it>.</p> <p>Conclusion</p> <p>As the number of <it>Thellungiella halophila </it>(<it>Thellungiella salsuginea</it>) expressed sequence tags (ESTs) was 9388 in July 2008, the number of ESTs has increased to approximately four times the original value as a result of this effort. Our sequences will thus contribute to correct future annotation of the <it>Thellungiella </it>genome sequence. The full-length enriched cDNA clones will enable the construction of overexpressing mutant plants by introduction of the cDNAs driven by a constitutive promoter, the complementation of <it>Thellungiella </it>mutants, and the determination of promoter regions in the <it>Thellungiella </it>genome.</p