9 research outputs found

    The width-flux relation of the broad iron line during the state transition of the black hole X-ray binaries

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    The observation of varying broad iron lines during the state transition of the black hole X-ray binaries (BHXBs) have been accumulating.In this work, the relation between the normalized intensity and the width of iron lines is investigated, in order to understand better the variation of iron lines and possibly its connection to state transition. Considering the uncertainties due to ionization and illuminating X-rays, only the effects of geometry and gravity are taken into account. Three scenarios were studied, i.e., the continuous disk model, innermost annulus model, and the cloud model. As shown by our calculations, at given iron width, the line flux of the cloud model is smaller than that of the continuous disk model; while for the innermost annulus model, the width is almost unrelated with the flux. The range of the line strength depends on both the BH spin and the inclination of the disk. We then apply to the observation of MAXI J1631-479 by NuSTAR during its decay from the soft state to the intermediate state. We estimated the relative line strength and width according to the spectral fitting results by Xu et al.(2020), and then compared with our theoretical width-flux relation. It was found that the cloud model was more favored. We further modeled the iron line profiles, and found that the cloud model can explain both the line profile and its variation with reasonable parameters.Comment: 7 figures, 12 pages, accepted for publication in RA

    Automated Facial Recognition for Noonan Syndrome Using Novel Deep Convolutional Neural Network With Additive Angular Margin Loss

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    BackgroundNoonan syndrome (NS), a genetically heterogeneous disorder, presents with hypertelorism, ptosis, dysplastic pulmonary valve stenosis, hypertrophic cardiomyopathy, and small stature. Early detection and assessment of NS are crucial to formulating an individualized treatment protocol. However, the diagnostic rate of pediatricians and pediatric cardiologists is limited. To overcome this challenge, we propose an automated facial recognition model to identify NS using a novel deep convolutional neural network (DCNN) with a loss function called additive angular margin loss (ArcFace).MethodsThe proposed automated facial recognition models were trained on dataset that included 127 NS patients, 163 healthy children, and 130 children with several other dysmorphic syndromes. The photo dataset contained only one frontal face image from each participant. A novel DCNN framework with ArcFace loss function (DCNN-Arcface model) was constructed. Two traditional machine learning models and a DCNN model with cross-entropy loss function (DCNN-CE model) were also constructed. Transfer learning and data augmentation were applied in the training process. The identification performance of facial recognition models was assessed by five-fold cross-validation. Comparison of the DCNN-Arcface model to two traditional machine learning models, the DCNN-CE model, and six physicians were performed.ResultsAt distinguishing NS patients from healthy children, the DCNN-Arcface model achieved an accuracy of 0.9201 ± 0.0138 and an area under the receiver operator characteristic curve (AUC) of 0.9797 ± 0.0055. At distinguishing NS patients from children with several other genetic syndromes, it achieved an accuracy of 0.8171 ± 0.0074 and an AUC of 0.9274 ± 0.0062. In both cases, the DCNN-Arcface model outperformed the two traditional machine learning models, the DCNN-CE model, and six physicians.ConclusionThis study shows that the proposed DCNN-Arcface model is a promising way to screen NS patients and can improve the NS diagnosis rate

    Topology characterization of a benzodiazepine-binding β-rich domain of the GABAA receptor α1 subunit

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    Structural investigation of GABAA receptors has been limited by difficulties imposed by its trans-membrane-complex nature. In the present study, the topology of a membrane-proximal β-rich (MPB) domain in the C139–L269 segment of the receptor α1 subunit was probed by mapping the benzodiazepine (BZ)-binding and epitopic sites, as well as fluorescence resonance energy transfer (FRET) analysis. Ala-scanning and semiconservative substitutions within this segment revealed the contribution of the phenyl rings of Y160 and Y210, the hydroxy group of S186 and the positive charge on R187 to BZ-binding. FRET with the bound BZ ligand indicated the proximity of Y160, S186, R187, and S206 to the BZ-binding site. On the other hand, epitope-mapping using the monoclonal antibodies (mAbs) against the MPB domain established a clustering of T172, R173, E174, Q196, and T197. Based on the lack of FRET between Trp substitutionally placed at R173 or V198 and bound BZ, this epitope-mapped cluster is located on a separate end of the folded protein from the BZ-binding site. Mutations of the five conserved Cys and Trp residues in the MPB domain gave rise to synergistic and rescuing effects on protein secondary structures and unfolding stability that point to a CCWCW-pentad, reminiscent to the CWC-triad “pin” of immunoglobulin (Ig)-like domains, important for the structural maintenance. These findings, together with secondary structure and fold predictions suggest an anti-parallel β-strand topology with resemblance to Ig-like fold, having the BZ-binding and the epitopic residues being clustered at two different ends of the fold

    China's Energy Situation and its Implications in the New Millennium

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    Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

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    In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field