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Based on the data from the observation of the supernova remnant(SNR) G327.1-1.1 by Advanced Satellite for Cosmology and Astrophysics (ASCA) and ROSAT , we find that G327.1-1.1 is a composite remnant with both a nonthermal emission component and a diffuse thermal emission component. The nonthermal component is well fitted by a power-law model with photon index Γ ∼ 2.2. This component is attributed to the emission from the synchrotron nebula powered by an undiscovered central pulsar.The thermal component has a temperature of about 0.4 keV. We attribute it to the emission from the shock-heat swept-up ISM. Its age, explosion energy and density of ambient medium are derived from the observed thermal component. Some charactistics about the synchrotron nebula are also derived. We search for the pulsed signal, but has not found it. The soft X-ray(0.4 -2 keV) and hard X-ray(2 -10 keV) images are different, but they both elongate in the SE-NW direction. And this X-ray SE-NW elongation is in positional coincidence with the radio ridge in MOST 843MHz radio map. We present a possibility that the X-ray nonthermal emission mainly come from the trail produced by a quickly moving undiscoverd pulsar, and the long radio ridge is formed when the pulsar is moving out of the boundary of the plerionic structure.

Epidemiological, cross-sectional, and prospective studies have suggested that insomnia, Alzheimer’s disease (AD) and depression are mutually interacting conditions and frequently co-occur. The monoamine and amino acid neurotransmitter systems in central nervous system were involved in the examination of neurobiological processes of this symptom complex. However, few studies have reported systematic and contrastive discussion of different neurotransmitters (NTs) changing in these neurological diseases. Thus, it is necessary to establish a reliable analytical method to monitoring NTs and their metabolite levels in rat brain tissues for elucidating the differences in pathophysiology of these neurological diseases. A rapid, sensitive and reliable LC-MS/MS method was established for simultaneous determination of the NTs and their metabolites, including tryptophan (Trp), tyrosine (Tyr), serotonin (5-HT), 5-hydroxyindolacetic acid (5-HIAA), dopamine (DA), acetylcholine (ACh), norepinephrine (NE), glutamic acid (Glu), and γ-aminobutyric acid (GABA) in rat brain tissues. The mobile phase consisting of methanol and 0.01% formic acid in water was performed on an Inertsil EP C18 column, and the developed method was validated well. Results demonstrated that there were significant differences for 5-HT, DA, NE, Trp, Tyr and ACh between model and control group in all three models, and a Bayes linear discriminant function was established to distinguish these three kinds of nervous system diseases by DA, Tyr and ACh for their significant differences among control and three model groups. It could be an excellent strategy to provide perceptions into the similarity and differentia of mechanisms from the point of NTs’ changing in brain directly and a new method to distinguish insomnia, depression and AD from view of essence.

The ASCA SIS and ROSAT PSPC spectral data of the supernova remnant 3C 397 are analyzed with a two-component nonequilibrium ionization model. In addition, the ASCA SIS0 and SIS1 spectra are Ðtted simultaneously in an equilibrium case. The resulting values of the hydrogen column density yield a distance of D8 kpc to 3C 397. It is found that the hard X-ray emission, containing S and Fe Ka lines, arises primarily from the hot component, while most of the soft emission, composed mainly of Mg, Si, and Fe L lines and continuum, is produced by the cool component. The emission measures suggest that the remnant evolves in a cloudy medium and imply that the supernova progenitor might not be a massive early-type star. The cool component is approaching ionization equilibrium. The ages estimated from the ionization parameters and dynamics are all much greater than the previous determination. We restore the X-ray maps using the ASCA SIS data and compare them with the ROSAT HRI and the NRAO VLA Sky Survey 20 cm maps. The morphology with two bright concentrations suggests a bipolar remnant encountering a denser medium in the west.

By contrasting positive-negative counterparts, graph contrastive learning has become a prominent technique for unsupervised graph representation learning. However, existing methods fail to consider the class information and will introduce false-negative samples in the random negative sampling, causing poor performance. To this end, we propose a graph debiased contrastive learning framework, which can jointly perform representation learning and clustering. Specifically, representations can be optimized by aligning with clustered class information, and simultaneously, the optimized representations can promote clustering, leading to more powerful representations and clustering results. More importantly, we randomly select negative samples from the clusters which are different from the positive sample's cluster. In this way, as the supervisory signals, the clustering results can be utilized to effectively decrease the false-negative samples. Extensive experiments on five datasets demonstrate that our method achieves new state-of-the-art results on graph clustering and classification tasks.

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