Non-LTE Calculations of the Fe I 6173 {\AA} Line in a Flaring Atmosphere

The Fe I 6173 {\AA} line is widely used in the measurements of vector magnetic fields by instruments including the Helioseismic and Magnetic Imager (HMI). We perform non-local thermodynamic equilibrium calculations of this line based on radiative hydrodynamic simulations in a flaring atmosphere. We employ both a quiet-Sun atmosphere and a penumbral atmosphere as the initial one in our simulations. We find that, in the quiet-Sun atmosphere, the line center is obviously enhanced during an intermediate flare. The enhanced emission is contributed from both radiative backwarming in the photosphere and particle beam heating in the lower chromosphere. A blue asymmetry of the line profile also appears due to an upward mass motion in the lower chromosphere. If we take a penumbral atmosphere as the initial atmosphere, the line has a more significant response to the flare heating, showing a central emission and an obvious asymmetry. The low spectral resolution of HMI would indicate some loss of information but the enhancement and line asymmetry are still kept. By calculating polarized line profiles, we find that the Stokes I and V profiles can be altered as a result of flare heating. Thus the distortion of this line has a crucial influence on the magnetic field measured from this line, and one should be cautious in interpreting the magnetic transients observed frequently in solar flares.Comment: 12 pages, 5 figures, accepted by ApJ

KIR2DL2/2DL3-E(35) alleles are functionally stronger than -Q(35) alleles.

KIR2DL2 and KIR2DL3 segregate as alleles of a single locus in the centromeric motif of the killer cell immunoglobulin-like receptor (KIR) gene family. Although KIR2DL2/L3 polymorphism is known to be associated with many human diseases and is an important factor for donor selection in allogeneic hematopoietic stem cell transplantation, the molecular determinant of functional diversity among various alleles is unclear. In this study we found that KIR2DL2/L3 with glutamic acid at position 35 (E(35)) are functionally stronger than those with glutamine at the same position (Q(35)). Cytotoxicity assay showed that NK cells from HLA-C1 positive donors with KIR2DL2/L3-E(35) could kill more target cells lacking their ligands than NK cells with the weaker -Q(35) alleles, indicating better licensing of KIR2DL2/L3(+) NK cells with the stronger alleles. Molecular modeling analysis reveals that the glutamic acid, which is negatively charged, interacts with positively charged histidine located at position 55, thereby stabilizing KIR2DL2/L3 dimer and reducing entropy loss when KIR2DL2/3 binds to HLA-C ligand. The results of this study will be important for future studies of KIR2DL2/L3-associated diseases as well as for donor selection in allogeneic stem cell transplantation

Electron transfer mechanisms, characteristics and applications of biological cathode microbial fuel cells – A mini review

AbstractSince the microbial fuel cells (MFCs) research in the laboratory has reached an unprecedented success, it has raised a research upsurge internationally in recent years. However, compared with laboratory studies, the widespread applications of the conventional MFCs were restrained by the limitations of high cost and low efficiency. This stimulates researchers to overcome the obstacles. In this condition, bio-cathodes attracted their great interests. This paper is a brief review about the experimental progress of bio-cathodes in microbial fuel cells with an emphasis on the classification according to the final electron acceptors and the comparison with the traditional abiotic cathode MFCs. Bio-cathodes are feasible in removing nutrient in wastewater treatment and being used as biosensors in bioremediation. Presently, tremendous efforts are being made in investigating appropriate electrodes and dominant strains to achieve the effective practical applications

Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0.947 $\pm$ 0.044.Comment: ISBI 2019 (Oral