4,026 research outputs found
Isovector Giant Dipole Resonance of Stable Nuclei in a Consistent Relativistic Random Phase Approximation
A fully consistent relativistic random phase approximation is applied to
study the systematic behavior of the isovector giant dipole resonance of nuclei
along the -stability line in order to test the effective Lagrangians
recently developed. The centroid energies of response functions of the
isovector giant dipole resonance for stable nuclei are compared with the
corresponding experimental data and the good agreement is obtained. It is found
that the effective Lagrangian with an appropriate nuclear symmetry energy,
which can well describe the ground state properties of nuclei, could also
reproduce the isovector giant dipole resonance of nuclei along the
-stability line.Comment: 4 pages, 1 Postscript figure, to be submitted to Chin.Phys.Let
Boundary Conditions for Interacting Membranes
We investigate supersymmetric boundary conditions in both the Bagger-Lambert
and the ABJM theories of interacting membranes. We find boundary conditions
associated to the fivebrane, the ninebrane and the M-theory wave. For the ABJM
theory we are able to understand the enhancement of supersymmetry to produce
the (4,4) supersymmetry of the self-dual string. We also include supersymmetric
boundary conditions on the gauge fields that cancel the classical gauge anomaly
of the Chern-Simons terms.Comment: 36 pages, latex, v2 minor typos correcte
Collective multipole excitations in a microscopic relativistic approach
A relativistic mean field description of collective excitations of atomic
nuclei is studied in the framework of a fully self-consistent relativistic
random phase approximation (RRPA). In particular, results of RRPA calculations
of multipole giant resonances and of low-lying collective states in spherical
nuclei are analyzed. By using effective Lagrangians which, in the mean-field
approximation, provide an accurate description of ground-state properties, an
excellent agreement with experimental data is also found for the excitation
energies of low-lying collective states and of giant resonances. Two points are
essential for the successful application of the RRPA in the description of
dynamical properties of finite nuclei: (i) the use of effective Lagrangians
with non-linear terms in the meson sector, and (ii) the fully consistent
treatment of the Dirac sea of negative energy states.Comment: 10 figures, submitted to Nucl.Phys.
Tumor taxonomy for the developmental lineage classification of neoplasms
BACKGROUND: The new "Developmental lineage classification of neoplasms" was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man. METHODS: The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document. RESULTS: The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates "The developmental lineage classification of neoplasms," and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length. The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version. CONCLUSIONS: This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within the tumor hierarchy. The entire classification and taxonomy are available as open access files (in XML and flat-file formats) with this article
Boundary conditions of the RGE flow in the noncommutative geometry approach to particle physics and cosmology
We investigate the effect of varying boundary conditions on the
renormalization group flow in a recently developed noncommutative geometry
model of particle physics and cosmology. We first show that there is a
sensitive dependence on the initial conditions at unification, so that, varying
a parameter even slightly can be shown to have drastic effects on the running
of the model parameters. We compare the running in the case of the default and
the maximal mixing conditions at unification. We then exhibit explicitly a
particular choice of initial conditions at the unification scale, in the form
of modified maximal mixing conditions, which have the property that they
satisfy all the geometric constraints imposed by the noncommutative geometry of
the model at unification, and at the same time, after running them down to
lower energies with the renormalization group flow, they still agree in order
of magnitude with the predictions at the electroweak scale.Comment: 18 pages LaTeX, 13 png figure
Extinction and recurrence of multi-group SEIR epidemic
In this paper, we consider a class of multi-group SEIR epidemic models with stochastic perturbations. By the method of stochastic Lyapunov functions, we study their asymptotic behavior in terms of the intensity of the stochastic perturbations and the reproductive number R0R0. When the perturbations are sufficiently large, the exposed and infective components decay exponentially to zero whilst the susceptible components converge weakly to a class of explicit stationary distributions regardless of the magnitude of R0R0. An interesting result is that, if the perturbations are sufficiently small and R0ā¤1R0ā¤1, then the exposed, infective and susceptible components have similar behaviors, respectively, as in the case of large perturbations. When the perturbations are small and R0>1R0>1, we construct a new class of stochastic Lyapunov functions to show the ergodic property and the positive recurrence, and our results reveal some cycling phenomena of recurrent diseases. Computer simulations are carried out to illustrate our analytical results
CELLmicrocosmos 2.2: advancements and applications in modeling of three-dimensional PDB membranes
Sommer B, Dingersen T, Schneider S, Rubert S, Gamroth C. CELLmicrocosmos 2.2: advancements and applications in modeling of three-dimensional PDB membranes (Conference Abstract). In: Journal of Cheminformatics. Journal of Cheminformatics. Vol 2(Suppl 1):O21. Springer Science and Business Media LLC; 2010
Advanced magnetic resonance imaging of cartilage components in haemophilic joints reveals that cartilage hemosiderin correlates with joint deterioration.
IntroductionEvidence suggests that toxic iron is involved in haemophilic joint destruction.AimTo determine whether joint iron deposition is linked to clinical and imaging outcomes in order to optimize management of haemophilic joint disease.MethodsAdults with haemophilia A or haemophilia B (nĀ =Ā 23, ā„ age 21) of all severities were recruited prospectively to undergo assessment with Hemophilia Joint Health Scores (HJHS), pain scores (visual analogue scale [VAS]) and magnetic resonance imaging (MRI) at 3T using conventional MRI protocols and 4-echo 3D-UTE-Cones sequences for one affected arthropathic joint. MRI was scored blinded by two musculoskeletal radiologists using the International Prophylaxis Study Group (IPSG) MRI scale. Additionally, UTE-T2* values of cartilage were quantified. Correlations between parameters were performed using Spearman rank correlation. Two patients subsequently underwent knee arthroplasty, which permitted linking of histological findings (including Perl's reaction) with MRI results.ResultsMRI scores did not correlate with pain scores or HJHS. Sixteen joints had sufficient cartilage for UTE-T2* analysis. T2* values for cartilage correlated inversely with HJHS (rs Ā =Ā -0.81, PĀ <Ā 0.001) and MRI scores (rs Ā =Ā -0.52, PĀ =Ā 0.037). This was unexpected since UTE-T2* values decrease with better joint status in patients with osteoarthritis, suggesting that iron was present and responsible for the effects. Histological analysis of cartilage confirmed iron deposition within chondrocytes, associated with low UTE-T2* values.ConclusionsIron accumulation can occur in cartilage (not only in synovium) and shows a clear association with joint health. Cartilage iron is a novel biomarker which, if quantifiable with innovative joint-specific MRI T2* sequences, may guide treatment optimization
DeepSF: deep convolutional neural network for mapping protein sequences to folds
Motivation
Protein fold recognition is an important problem in structural
bioinformatics. Almost all traditional fold recognition methods use sequence
(homology) comparison to indirectly predict the fold of a tar get protein based
on the fold of a template protein with known structure, which cannot explain
the relationship between sequence and fold. Only a few methods had been
developed to classify protein sequences into a small number of folds due to
methodological limitations, which are not generally useful in practice.
Results
We develop a deep 1D-convolution neural network (DeepSF) to directly classify
any protein se quence into one of 1195 known folds, which is useful for both
fold recognition and the study of se quence-structure relationship. Different
from traditional sequence alignment (comparison) based methods, our method
automatically extracts fold-related features from a protein sequence of any
length and map it to the fold space. We train and test our method on the
datasets curated from SCOP1.75, yielding a classification accuracy of 80.4%. On
the independent testing dataset curated from SCOP2.06, the classification
accuracy is 77.0%. We compare our method with a top profile profile alignment
method - HHSearch on hard template-based and template-free modeling targets of
CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is
14.5%-29.1% higher than HHSearch on template-free modeling targets and
4.5%-16.7% higher on hard template-based modeling targets for top 1, 5, and 10
predicted folds. The hidden features extracted from sequence by our method is
robust against sequence mutation, insertion, deletion and truncation, and can
be used for other protein pattern recognition problems such as protein
clustering, comparison and ranking.Comment: 28 pages, 13 figure
- ā¦