9,334 research outputs found
On Distance Magic Harary Graphs
This paper establishes two techniques to construct larger distance magic and
(a, d)-distance antimagic graphs using Harary graphs and provides a solution to
the existence of distance magicness of legicographic product and direct product
of G with C4, for every non-regular distance magic graph G with maximum degree
|V(G)|-1.Comment: 12 pages, 1 figur
Differential Dynamics at Glycosidic Linkages of an Oligosaccharide as Revealed by 13C NMR Spin Relaxation and Stochastic Modeling
Among biomolecules, carbohydrates are unique in that not only can linkages be formed through different positions but the structures may also be branched. The trisaccharide \uf062-D-Glcp-(1\uf0ae3)[\uf062-D-Glcp-(1\uf0ae2)]-\uf061-D-Manp-OMe represents a model of a branched vicinally disubstituted structure. A 13C site-specific isotopologue with labeling in each of the two terminal glucosyl residues enabled acquisition of high-quality 13C NMR relaxation parameters T1, T2 and heteronuclear NOE, with standard deviations of \uf0a3 0.5%. For interpretation of the experimental NMR data a diffusive chain model was used in which the dynamics of the glycosidic linkages is coupled to the global reorientation motion of the trisaccharide. Brownian dynamics simulations relying on the potential of mean force at the glycosidic linkages were employed to evaluate spectral densities of the spin probes. Calculated NMR relaxation parameters showed very good agreement with experimental data, deviating < 3%. The resulting dynamics is described by correlation times of 196 ps and 174 ps for the \uf062-(1\uf0ae2)- and \uf062-(1\uf0ae3)-linked glucosyl residues, respectively, i.e., different and linkage dependent. Notably, the devised computational protocol was performed without any fitting of parameters
Self-tuning of threshold for a two-state system
A two-state system (TSS) under time-periodic perturbations (to be regarded as
input signals) is studied in connection with self-tuning (ST) of threshold and
stochastic resonance (SR). By ST, we observe the improvement of signal-to-noise
ratio (SNR) in a weak noise region. Analytic approach to a tuning equation
reveals that SNR improvement is possible also for a large noise region and this
is demonstrated by Monte Carlo simulations of hopping processes in a TSS. ST
and SR are discussed from a little more physical point of energy transfer
(dissipation) rate, which behaves in a similar way as SNR. Finally ST is
considered briefly for a double-well potential system (DWPS), which is closely
related to the TSS
Giant coherence in driven systems
We study the noise-induced currents and reliability or coherence of transport
in two different classes of rocking ratchets. For this, we consider the motion
of Brownian particles in the over damped limit in both adiabatic and
non-adiabatic regimes subjected to unbiased temporally symmetric and asymmetric
periodic driving force. In the case of a time symmetric driving, we find that
even in the presence of a spatially symmetric simple sinusoidal potential,
highly coherent transport occurs. These ratchet systems exhibit giant coherence
of transport in the regime of parameter space where unidirectional currents in
the deterministic case are observed. Outside this parameter range, i.e., when
current vanishes in the deterministic regime, coherence in transport is very
low. The transport coherence decreases as a function of temperature and is a
non-monotonic function of the amplitude of driving. The transport becomes
unreliable as we go from the adiabatic to the non-adiabatic domain of
operation.Comment: 15 pages, 9 figures, replaced by the version to appear in JSTA
Quantitative Imaging of Protein-Protein Interactions by Multiphoton Fluorescence Lifetime Imaging Microscopy using a Streak camera
Fluorescence Lifetime Imaging Microscopy (FLIM) using multiphoton excitation
techniques is now finding an important place in quantitative imaging of
protein-protein interactions and intracellular physiology. We review here the
recent developments in multiphoton FLIM methods and also present a description
of a novel multiphoton FLIM system using a streak camera that was developed in
our laboratory. We provide an example of a typical application of the system in
which we measure the fluorescence resonance energy transfer between a
donor/acceptor pair of fluorescent proteins within a cellular specimen.Comment: Overview of FLIM techniques, StreakFLIM instrument, FRET application
Management of Gouty Arthritis with special reference to Vatarakta - A Case Report
Gouty Arthritis has now become a common disease condition which we deal in Ayurveda, but a proper treatment protocol is not followed in many cases. The case reported here was as a result of improper diet and lack of exercise which resulted in an increase serum uric acid level and joint inflammation. The treatment was given at IPD level diagnosing it as Gambhira Vatarakta with valid Chikitsa Siddhanta. This case report provides us guidelines that even a chronic gouty arthritis with a very high serum uric acid can be treated as per Vataraktha Chikitsa Siddhanta in Ayurveda
Direct Error Driven Learning for Deep Neural Networks with Applications to Bigdata
In this paper, generalization error for traditional learning regimes-based classification is demonstrated to increase in the presence of bigdata challenges such as noise and heterogeneity. To reduce this error while mitigating vanishing gradients, a deep neural network (NN)-based framework with a direct error-driven learning scheme is proposed. To reduce the impact of heterogeneity, an overall cost comprised of the learning error and approximate generalization error is defined where two NNs are utilized to estimate the costs respectively. To mitigate the issue of vanishing gradients, a direct error-driven learning regime is proposed where the error is directly utilized for learning. It is demonstrated that the proposed approach improves accuracy by 7 % over traditional learning regimes. The proposed approach mitigated the vanishing gradient problem and improved generalization by 6%
A Multi-Step Nonlinear Dimension-Reduction Approach with Applications to Bigdata
In this paper, a multi-step dimension-reduction approach is proposed for addressing nonlinear relationships within attributes. In this work, the attributes in the data are first organized into groups. In each group, the dimensions are reduced via a parametric mapping that takes into account nonlinear relationships. Mapping parameters are estimated using a low rank singular value decomposition (SVD) of distance covariance. Subsequently, the attributes are reorganized into groups based on the magnitude of their respective singular values. The group-wise organization and the subsequent reduction process is performed for multiple steps until a singular value-based user-defined criterion is satisfied. Simulation analysis is utilized to investigate the performance with five big data-sets
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