Emergence of Global Preferential Attachment From Local Interaction

Global degree/strength based preferential attachment is widely used as an evolution mechanism of networks. But it is hard to believe that any individual can get global information and shape the network architecture based on it. In this paper, it is found that the global preferential attachment emerges from the local interaction models, including distance-dependent preferential attachment (DDPA) evolving model of weighted networks(M. Li et al, New Journal of Physics 8 (2006) 72), acquaintance network model(J. Davidsen et al, Phys. Rev. Lett. 88 (2002) 128701) and connecting nearest-neighbor(CNN) model(A. Vazquez, Phys. Rev. E 67 (2003) 056104). For DDPA model and CNN model, the attachment rate depends linearly on the degree or strength, while for acquaintance network model, the dependence follows a sublinear power law. It implies that for the evolution of social networks, local contact could be more fundamental than the presumed global preferential attachment. This is onsistent with the result observed in the evolution of empirical email networks.Comment: 9 pages, 5 figure

Network of Econophysicists: a weighted network to investigate the development of Econophysics

The development of Econophysics is studied from the perspective of scientific communication networks. Papers in Econophysics published from 1992 to 2003 are collected. Then a weighted and directed network of scientific communication, including collaboration, citation and personal discussion, is constructed. Its static geometrical properties, including degree distribution, weight distribution, weight per degree, and betweenness centrality, give a nice overall description of the research works. The way we introduced here to measure the weight of connections can be used as a general one to construct weighted network.Comment: 6 pages, 7 figure

Resin and Magnetic Nanoparticle-Based Free Radical Probes for Glycan Capture, Isolation, and Structural Characterization

By combining the merits of solid supports and free radical activated glycan sequencing (FRAGS) reagents, we develop a multifunctional solid-supported free radical probe (SS-FRAGS) that enables glycan enrichment and characterization. SS-FRAGS comprises a solid support, free radical precursor, disulfide bond, pyridyl, and hydrazine moieties. Thio-activated resin and magnetic nanoparticles (MNPs) are chosen as the solid support to selectively capture free glycans via the hydrazine moiety, allowing for their enrichment and isolation. The disulfide bond acts as a temporary covalent linkage between the solid support and the captured glycan, allowing the release of glycans via the cleavage of the disulfide bond by dithiothreitol. The basic pyridyl functional group provides a site for the formation of a fixed charge, enabling detection by mass spectrometry and avoiding glycan rearrangement during collisional activation. The free radical precursor generates a nascent free radical upon collisional activation and thus simultaneously induces systematic and predictable fragmentation for glycan structure elucidation. A radical-driven glycan deconstruction diagram (R-DECON) is developed to visually summarize the MS² results and thus allow for the assembly of the glycan skeleton, making the differentiation of isobaric glycan isomers unambiguous. For application to a real-world sample, we demonstrate the efficacy of the SS-FRAGS by analyzing glycan structures enzymatically cleaved from RNase-B

Mechanisms and energetics of free radical initiated disulfide bond cleavage in model peptides and insulin by mass spectrometry

We investigate the mechanism of disulfide bond cleavage in gaseous peptide and protein ions initiated by a covalently-attached regiospecific acetyl radical using mass spectrometry (MS). Highly selective S–S bond cleavages with some minor C–S bond cleavages are observed by a single step of collisional activation. We show that even multiple disulfide bonds in intact bovine insulin are fragmented in the MS2 stage, releasing the A- and B-chains with a high yield, which has been challenging to achieve by other ion activation methods. Yet, regardless of the previous reaction mechanism studies, it has remained unclear why (1) disulfide bond cleavage is preferred to peptide backbone fragmentation, and why (2) the S–S bond that requires the higher activation energy conjectured in previously suggested mechanisms is more prone to be cleaved than the C–S bond by hydrogen-deficient radicals. To probe the mechanism of these processes, model peptides possessing deuterated β-carbon(s) at the disulfide bond are employed. It is suggested that the favored pathway of S–S bond cleavage is triggered by direct acetyl radical attack at sulfur with concomitant cleavage of the S–S bond (S_H2). The activation energy for this process is substantially lower by ~9–10 kcal mol^(−1) than those of peptide backbone cleavage processes determined by density functional quantum chemical calculations. Minor reaction pathways are initiated by hydrogen abstraction from the α-carbon or the β-carbon of a disulfide, followed by β-cleavages yielding C–S or S–S bond scissions. The current mechanistic findings should be generally applicable to other radical-driven disulfide bond cleavages with different radical species such as the benzyl and methyl pyridyl radicals

Ferromagnetism of 3^3He Films in the Low Field Limit

We provide evidence for a finite temperature ferromagnetic transition in 2-dimensions as $H \to 0$ in thin films of $^3$He on graphite, a model system for the study of two-dimensional magnetism. We perform pulsed and CW NMR experiments at fields of 0.03 - 0.48 mT on $^3$He at areal densities of 20.5 - 24.2 atoms/nm$^2$. At these densities, the second layer of $^3$He has a strongly ferromagnetic tendency. With decreasing temperature, we find a rapid onset of magnetization that becomes independent of the applied field at temperatures in the vicinity of 1 mK. Both the dipolar field and the NMR linewidth grow rapidly as well, which is consistent with a large (order unity) polarization of the $^3$He spins.Comment: 4 figure

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial sements extraced from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlyingarterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial sements extraced from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlyingarterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs

Biomimetic Reagents for the Selective Free Radical and Acid–Base Chemistry of Glycans: Application to Glycan Structure Determination by Mass Spectrometry

Nature excels at breaking down glycans into their components, typically via enzymatic acid–base catalysis to achieve selective cleavage of the glycosidic bond. Noting the importance of proton transfer in the active site of many of these enzymes, we describe a sequestered proton reagent for acid-catalyzed glycan sequencing (PRAGS) that derivatizes the reducing terminus of glycans with a pyridine moiety possessing moderate proton affinity. Gas-phase collisional activation of PRAGS-derivatized glycans predominately generates C1–O glycosidic bond cleavages retaining the charge on the reducing terminus. The resulting systematic PRAGS-directed deconstruction of the glycan can be analyzed to extract glycan composition and sequence. Glycans are also highly susceptible to dissociation by free radicals, mainly reactive oxygen species, which inspired our development of a free radical activated glycan sequencing (FRAGS) reagent, which combines a free radical precursor with a pyridine moiety that can be coupled to the reducing terminus of target glycans. Collisional activation of FRAGS-derivatized glycans generates a free radical that reacts to yield abundant cross-ring cleavages, glycosidic bond cleavages, and combinations of these types of cleavages with retention of charge at the reducing terminus. Branched sites are identified with the FRAGS reagent by the specific fragmentation patterns that are observed only at these locations. Mechanisms of dissociation as well as application of the reagents for both linear and highly branched glycan structure analysis are investigated and discussed. The approach developed here for glycan structure analysis offers unique advantages compared to earlier studies employing mass spectrometry for this purpose