Targeted molecular therapeutics for Parkinson's Disease: A role for antisense oligonucleotides?

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by marked heterogeneity in clinical symptoms and a complex genetic background..

Surface structure and solidification morphology of aluminum nanoclusters

Classical molecular dynamics simulation with embedded atom method potential had been performed to investigate the surface structure and solidification morphology of aluminum nanoclusters Aln (n = 256, 604, 1220 and 2048). It is found that Al cluster surfaces are comprised of (111) and (001) crystal planes. (110) crystal plane is not found on Al cluster surfaces in our simulation. On the surfaces of smaller Al clusters (n = 256 and 604), (111) crystal planes are dominant. On larger Al clusters (n = 1220 and 2048), (111) planes are still dominant but (001) planes can not be neglected. Atomic density on cluster (111)/(001) surface is smaller/larger than the corresponding value on bulk surface. Computational analysis on total surface area and surface energies indicates that the total surface energy of an ideal Al nanocluster has the minimum value when (001) planes occupy 25% of the total surface area. We predict that a melted Al cluster will be a truncated octahedron after equilibrium solidification.Comment: 22 pages, 6 figures, 34 reference

Catch me if you can: a participant-level rumor detection framework via fine-grained user representation learning

Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and are difficult to generalize. Deep learning solutions come to help. However, they usually fail to capture the underlying structure of the rumor propagation and the influence of all participants involved in the spreading chain. In this study, we propose a novel participant level rumor detection framework. It explicitly models and integrates various fine-grained user representations (i.e., user influence, susceptibility, and temporal information) of all participants from the propagation threads via deep representation learning. Experiments conducted on real world datasets demonstrate a significant accuracy improvement of our approach. Theoretically, we contribute to the effective usage of data science and analytics for social information diffusion design, particularly rumor detection. Practically, our results can be used to improve the quality of rumor detection services for social platforms.Computer Science

Multi-scale graph capsule with influence attention for information cascades prediction

Information cascade size prediction is one of the primary challenges for understanding the diffusion of information. Traditional feature-based methods heavily rely on the quality of handcrafted features, requiring extensive domain knowledge and hard to generalize to new domains. Recently, inspired by the success of deep learning in computer vision and natural language processing, researchers have developed neural network-based approaches for tackling this problem. However, existing deep learning-based methods either focused on modeling the temporal characteristics of cascades but ignored the structural information or failed to take the order-scale and position-scale into consideration in modeling structures of information propagation. This paper proposed a novel graph neural network-based model, called MUCas, to learn the latent representations of cascade graphs from a multi-scale perspective, which can make full use of the direction-scale, high-order-scale, position-scale, and dynamic-scale of cascades via a newly designed MUlti-scale Graph Capsule Network (MUG-Caps) and the influence-attention mechanism. Extensive experiments conducted on two real-world data sets demonstrate that our MUCas significantly outperforms the state-of-the-art approaches.Computer Science

Modeling microscopic and macroscopic information diffusion for rumor detection

Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering, which requires extensive manual efforts and are difficult to generalize to different domains. Recently, deep learning solutions have emerged as the de facto methods which detect online rumors in an end-to-end manner. However, they still fail to fully capture the dissemination patterns of rumors. In this study, we propose a novel diffusion-based rumor detection model, called Macroscopic and Microscopic-aware Rumor Detection, to explore the full-scale diffusion patterns of information. It leverages graph neural networks to learn the macroscopic diffusion of rumor propagation and capture microscopic diffusion patterns using bidirectional recurrent neural networks while taking into account the user-time series. Moreover, it leverages knowledge distillation technique to create a more informative student model and further improve the model performance. Experiments conducted on two real-world data sets demonstrate that our method achieves significant accuracy improvements over the state-of-the-art baseline models on rumor detection.Computer Science

Sensitivity to measurement perturbation of single atom dynamics in cavity QED

We consider continuous observation of the nonlinear dynamics of single atom trapped in an optical cavity by a standing wave with intensity modulation. The motion of the atom changes the phase of the field which is then monitored by homodyne detection of the output field. We show that the conditional Hilbert space dynamics of this system, subject to measurement induced perturbations, depends strongly on whether the corresponding classical dynamics is regular or chaotic. If the classical dynamics is chaotic the distribution of conditional Hilbert space vectors corresponding to different observation records tends to be orthogonal. This is a characteristic feature of hypersensitivity to perturbation for quantum chaotic systems.Comment: 11 pages, 6 figure

Exact solution of Schrodinger equation for Pseudoharmonic potential

Exact solution of Schrodinger equation for the pseudoharmonic potential is obtained for an arbitrary angular momentum. The energy eigenvalues and corresponding eigenfunctions are calculated by Nikiforov-Uvarov method. Wavefunctions are expressed in terms of Jacobi polynomials. The energy eigenvalues are calculated numerically for some values of l and n with n<5 for some diatomic molecules.Comment: 10 page

Crystal structure and two-stage hydrolysis of dimethoxo(meso-tetra(4-methoxyphenylporphyrinato))tin(IV), Sn(tmpp)(OMe)(2)

In this work, we determine the crystal structure of dimethoxo(meso-tetra(4-methoxyphenylporphyrinato))tin(IV), Sn(tmpp)(OMe)(2) (1). Experimental results indicate that the tin atom has an octahedral geometry. The geometry around the tin center has Sn(1)-O(5) = 2.020(6), Sn(1)-O(6) = 2.003(7) Angstrom and an average Sn(1)-N = 2.10(1) Angstrom. The two methoxo groups are unidentately coordinated to the tin(IV) atom. Two-stage hydrolysis of Sn(tmpp)(OMe)(2) in CDCl3 was observed by H-1 and C-13 NMR spectroscopy. Compound (1) crystallizes in the space group P2(1)/n with a = 14.7492(1), b = 19.2022(3), c = 16.0806(2) Angstrom, beta = 94.104(1)degrees and Z = 4

Entanglement in Weisskopf-Wigner theory of atomic decay in free space

In this paper, we use the Weisskopf-Wigner theory to study the entanglement in the state of the free-space radiation field produced from vacuum due to atomic decay. We show how bipartite entanglement is shared between different partitions of the radiation modes. We investigate the role played by the size of the partitions and their detuning with the decaying atom. The dynamics of the atom-field entanglement during the atomic decay is also briefly discussed. From this dynamics, we assert that such entanglement is the physical quantity that fix the statistical atomic decay time.Comment: 7 pages, 4 figures, changed from purity to entropy of entanglement calculations in the replaced versio

Thyroid control over biomembranes: VI. Lipids in liver mitochondria and microsomes of hypothyroid rats

The lipids of liver mitochondria prepared from normal rats and from rats made hypothyroid by thyroidectomy and injection with131INa contained similar amounts, per mg protein, of total lipids, phospholipids, neutral lipids and lipid phosphorus. Hypothyroidism caused a doubling of the relative amounts of mitochondrial cardiolipins (CL; to 20.5% of the phospholipid P) and an accompanying trend (although statistically not significant) toward decreased amounts of both phosphatidylcholines (PC) and phosphatidylserines (PS), with phosphatidylethanolamines (PE) remaining unchanged. The pattern of elevated 18∶2 fatty acyl content and depleted 20∶4 acyl groups of the mitochondrial phospholipids of hypothyroid preparations was reflected to varying degrees in the resolved phospholipids, with PC showing greater degrees of abnormality than PE, and CL showing none. Hypothyroidism produced the same abnormal pattern of fatty acyl distributions in liver microsomal total lipids as was found in the mitochondria. Hypothyroid rats, when killed 6 hr after injection of [1‐14C] labeled linoleate, showed the following abnormalities: the liver incorporated less label into lipids, and converted 18∶2 not exclusively to 20∶4 (as normals do) but instead incorporated the label mainly into saturated fatty acids. These data, together with the known decrease in β‐oxidation, suggest that hypothyroidism involves possible defective step(s) in the conversion of 18∶2 to 20∶4.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142296/1/lipd0328.pd