6,958 research outputs found
Nuclear enhanced power corrections to DIS structure functions
We calculate nuclear enhanced power corrections to structure functions
measured in deeply inelastic lepton-nucleus scattering in Quantum
Chromodynamics (QCD). We find that the nuclear medium enhanced power
corrections at order of enhance the longitudinal structure
function , and suppress the transverse structure function . We
demonstrate that strong nuclear effects in and ,
recently observed by HERMES Collaboration, can be explained in terms of the
nuclear enhanced power corrections.Comment: Latex, 10 pages including 3 figure
SVSBI: Sequence-based virtual screening of biomolecular interactions
Virtual screening (VS) is an essential technique for understanding
biomolecular interactions, particularly, drug design and discovery. The
best-performing VS models depend vitally on three-dimensional (3D) structures,
which are not available in general but can be obtained from molecular docking.
However, current docking accuracy is relatively low, rendering unreliable VS
models. We introduce sequence-based virtual screening (SVS) as a new generation
of VS models for modeling biomolecular interactions. The SVS model utilizes
advanced natural language processing (NLP) algorithms and optimizes deep
-embedding strategies to encode biomolecular interactions without invoking
3D structure-based docking. We demonstrate the state-of-art performance of SVS
for four regression datasets involving protein-ligand binding, protein-protein,
protein-nucleic acid binding, and ligand inhibition of protein-protein
interactions and five classification datasets for the protein-protein
interactions in five biological species. SVS has the potential to dramatically
change the current practice in drug discovery and protein engineering
Parton Energy Loss at Twist-Six in Deeply Inelastic e-A Scattering
Within the framework of the generalized factorization in pQCD, we investigate
the multiple parton scattering and induced parton energy loss at twist-6 in
deeply inelastic e-A scattering with the helicity amplitude approximation. It
is shown that twist-6 processes will give rise to additional nuclear size
dependence of the parton energy loss due to LPM interference effect while its
contribution is power suppressed.Comment: 6 pages, 2 figure
Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an
effective approach, especially by using nuclear medicine imaging techniques
such as Positron Emission Topography (PET). In various literature it has been
found that PET images can be better modeled as signals (e.g. uptake of
florbetapir) defined on a network (non-Euclidean) structure which is governed
by its underlying graph patterns of pathological progression and metabolic
connectivity. In order to effectively apply deep learning framework for PET
image analysis to overcome its limitation on Euclidean grid, we develop a
solution for 3D PET image representation and analysis under a generalized,
graph-based CNN architecture (PETNet), which analyzes PET signals defined on a
group-wise inferred graph structure. Computations in PETNet are defined in
non-Euclidean, graph (network) domain, as it performs feature extraction by
convolution operations on spectral-filtered signals on the graph and pooling
operations based on hierarchical graph clustering. Effectiveness of the PETNet
is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset,
which shows improved performance over both deep learning and other machine
learning-based methods.Comment: Jiaming Guo, Wei Qiu and Xiang Li contribute equally to this wor
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