Single-walled carbon nanotube bundle under hydrostatic pressure studied by the first-principles calculations

The structural, electronic, optical and vibrational properties of the collapsed (10,10) single-walled carbon nanotube bundle under hydrostatic pressure have been studied by the first-principles calculations. Some features are observed in the present study: First, a collapsed structure is found, which is distinct from both of the herringbone and parallel structures obtained previously. Secondly, a pseudo-gap induced by the collapse appears along the symmetry axis \textit{$\Gamma$X}. Thirdly, the relative orientation between the collapsed tubes has an important effect on their electronic, optical and vibrational properties, which provides an efficient experimental method to distinguish unambiguously three different collapsed structures.Comment: 14 pages, 6 figure

First principles investigation of transition-metal doped group-IV semiconductors: Rx{_x}Y1−x_{1-x} (R=Cr, Mn, Fe; Y=Si, Ge)

A number of transition-metal (TM) doped group-IV semiconductors, R$_{x}$Y$_{1-x}$ (R=Cr, Mn and Fe; Y=Si, Ge), have been studied by the first principles calculations. The obtained results show that antiferromagnetic (AFM) order is energetically more favored than ferromagnetic (FM) order in Cr-doped Ge and Si with $x$=0.03125 and 0.0625. In 6.25% Fe-doped Ge, FM interaction dominates in all range of the R-R distances while for Fe-doped Ge at 3.125% and Fe-doped Si at both concentrations of 3.125% and 6.25%, only in a short R-R range can the FM states exist. In the Mn-doped case, the RKKY-like mechanism seems to be suitable for the Ge host matrix, while for the Mn-doped Si, the short-range AFM interaction competes with the long-range FM interaction. The different origin of the magnetic orders in these diluted magnetic semiconductors (DMSs) makes the microscopic mechanism of the ferromagnetism in the DMSs more complex and attractive.Comment: 14 pages, 2 figures, 6 table

Geometrical and electronic structures of the (5, 3) single-walled gold nanotube from first-principles calculations

The geometrical and electronic structures of the 4 {\AA} diameter perfect and deformed (5, 3) single-walled gold nanotube (SWGT) have been studied based upon the density-functional theory in the local-density approximation (LDA). The calculated relaxed geometries show clearly significant deviations from those of the ideally rolled triangular gold sheet. It is found that the different strains have different effects on the electronic structures and density of states of the SWGTs. And the small shear strain can reduce the binding energy per gold atom of the deformed SWGT, which is consistent with the experimentally observed result. Finally, we found the finite SWGT can show the metal-semiconductor transition.Comment: 11 pages, 4 figure

The electronic structures and magnetic properties of perovskite ruthenates from constrained orbital hybridization calculations

We introduce a method to analyze the effect of hybridization by shifting corresponding atomic levels using external potentials. Based on this approach, we study perovskite ruthenates,\ and unambiguously identify that the covalency between the \textit{A}-site cation and O ion will modify the Ru-O hybridization and change the density of state at Fermi level, consequently affect the magnetic properties significantly. We also study the effect of pressure and reveal that hydrostatic pressure has a small effect on the Ru-O-Ru bond angle of SrRuO$_{3}$, while it will decrease the Ru-O length and increase the band width significantly. Therefore, the magnetic ordering temperature will decrease monotonically with pressure

Statistical Theory of Protein Combinatorial Libraries

Combinatorial experiments provide new ways to probe the determinants of protein folding and to identify novel folding amino acid sequences. These types of experiments, however, are complicated both by enormous conformational complexity and by large numbers of possible sequences. Therefore, a quantitative computational theory would be helpful in designing and interpreting these types of experiment. Here, we present and apply a statistically based, computational approach for identifying the properties of sequences compatible with a given main-chain structure. Protein side-chain conformations are included in an atom-based fashion. Calculations are performed for a variety of similar backbone structures to identify sequence properties that are robust with respect to minor changes in main-chain structure. Rather than specific sequences, the method yields the likelihood of each of the amino acids at preselected positions in a given protein structure. The theory may be used to quantify the characteristics of sequence space for a chosen structure without explicitly tabulating sequences. To account for hydrophobic effects, we introduce an environmental energy that it is consistent with other simple hydrophobicity scales and show that it is effective for side-chain modeling. We apply the method to calculate the identity probabilities of selected positions of the immunoglobulin light chain-binding domain of protein L, for which many variant folding sequences are available. The calculations compare favorably with the experimentally observed identity probabilities

Structural and electronic properties of the metal-metal intramolecular junctions of single-walled carbon nanotubes

Several intramolecular junctions (IMJs) connecting two metallic (11, 8) and (9, 6) carbon nanotubes along their common axis have been realized by using a layer-divided technique to the nanotubes and introducing the topological defects. Atomic structure of each IMJ configuration is optimized with a combination of density-functional theory (DFT) and the universal force field (UFF) method, based upon which a four-orbital tight-binding calculation is made on its electronic properties. Different topological defect structures and their distributions on the IMJ interfaces have been found, showing decisive effects on the localized density of states, while the sigma-pi coupling effect is negligible near Fermi energy (EF). Finally, a new IMJ model has been proposed, which probably reflects a real atomic structure of the M-M IMJ observed in the experiment [Science 291, 97 (2001)].Comment: 11 pages and 3 figure

Raman modes of the deformed single-wall carbon nanotubes

With the empirical bond polarizability model, the nonresonant Raman spectra of the chiral and achiral single-wall carbon nanotubes (SWCNTs) under uniaxial and torsional strains have been systematically studied by \textit{ab initio} method. It is found that both the frequencies and the intensities of the low-frequency Raman active modes almost do not change in the deformed nanotubes, while their high-frequency part shifts obviously. Especially, the high-frequency part shifts linearly with the uniaxial tensile strain, and two kinds of different shift slopes are found for any kind of SWCNTs. More interestingly, new Raman peaks are found in the nonresonant Raman spectra under torsional strain, which are explained by a) the symmetry breaking and b) the effect of bond rotation and the anisotropy of the polarizability induced by bond stretching

Ultrastructural and immunohistochemical analysis of the 8-20 week human fetal pancreas

Development of the human pancreas is well-known to involve tightly controlled differentiation of pancreatic precursors to mature cells that express endocrine- or exocrine-specific protein products. However, details of human pancreatic development at the ultrastructural level are limited. The present study analyzed 8–20 week fetal age human pancreata using scanning and transmission electron microscopy (TEM), TEM immunogold and double or triple immunofluorescence staining. Primary organization of islets and acini occurred during the developmental period examined. Differentiating endocrine and exocrine cells developed from the ductal tubules and subsequently formed isolated small clusters. Extracellular matrix fibers and proteins accumulated around newly differentiated cells during their migration and cluster formation. Glycogen expression was robust in ductal cells of the pancreas from 8–15 weeks of fetal age; however, this became markedly reduced at 20 weeks, with a concomitant increase in acinar cell glycogen content. Insulin secretory granules transformed from being dense and round at 8 weeks to distinct geometric (multilobular, crystalline) structures by 14–20 weeks. Initially many of the differentiating endocrine cells were multihormonal and contained polyhormonal granules; by 20 weeks, monohormonal cells were in the majority. Interestingly, certain secretory granules in the early human fetal pancreatic cells showed positivity for both exocrine (amylase) and endocrine proteins. This combined ultrastructural and immunohistochemical study showed that, during early developmental stages, the human pancreas contains differentiating epithelial cells that associate closely with the extracellular matrix, have dynamic glycogen expression patterns and contain polyhormonal as well as mixed endocrine/ exocrine granules

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival