250 research outputs found
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{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: RY (R=Cr, Mn, Fe; Y=Si, Ge)
A number of transition-metal (TM) doped group-IV semiconductors,
RY (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 =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, 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
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