27,449 research outputs found
Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications
Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic
resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of
Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity
underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the
use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to
cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers
have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic,
and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity
across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power
and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited.
Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral
reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains
in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163
On the effects of firing memory in the dynamics of conjunctive networks
Boolean networks are one of the most studied discrete models in the context
of the study of gene expression. In order to define the dynamics associated to
a Boolean network, there are several \emph{update schemes} that range from
parallel or \emph{synchronous} to \emph{asynchronous.} However, studying each
possible dynamics defined by different update schemes might not be efficient.
In this context, considering some type of temporal delay in the dynamics of
Boolean networks emerges as an alternative approach. In this paper, we focus in
studying the effect of a particular type of delay called \emph{firing memory}
in the dynamics of Boolean networks. Particularly, we focus in symmetric
(non-directed) conjunctive networks and we show that there exist examples that
exhibit attractors of non-polynomial period. In addition, we study the
prediction problem consisting in determinate if some vertex will eventually
change its state, given an initial condition. We prove that this problem is
{\bf PSPACE}-complete
PaperRobot: Incremental Draft Generation of Scientific Ideas
We present a PaperRobot who performs as an automatic research assistant by
(1) conducting deep understanding of a large collection of human-written papers
in a target domain and constructing comprehensive background knowledge graphs
(KGs); (2) creating new ideas by predicting links from the background KGs, by
combining graph attention and contextual text attention; (3) incrementally
writing some key elements of a new paper based on memory-attention networks:
from the input title along with predicted related entities to generate a paper
abstract, from the abstract to generate conclusion and future work, and finally
from future work to generate a title for a follow-on paper. Turing Tests, where
a biomedical domain expert is asked to compare a system output and a
human-authored string, show PaperRobot generated abstracts, conclusion and
future work sections, and new titles are chosen over human-written ones up to
30%, 24% and 12% of the time, respectively.Comment: 12 pages. Accepted by ACL 2019 Code and resource is available at
https://github.com/EagleW/PaperRobo
In silico generation of novel, drug-like chemical matter using the LSTM neural network
The exploration of novel chemical spaces is one of the most important tasks
of cheminformatics when supporting the drug discovery process. Properly
designed and trained deep neural networks can provide a viable alternative to
brute-force de novo approaches or various other machine-learning techniques for
generating novel drug-like molecules. In this article we present a method to
generate molecules using a long short-term memory (LSTM) neural network and
provide an analysis of the results, including a virtual screening test. Using
the network one million drug-like molecules were generated in 2 hours. The
molecules are novel, diverse (contain numerous novel chemotypes), have good
physicochemical properties and have good synthetic accessibility, even though
these qualities were not specific constraints. Although novel, their structural
features and functional groups remain closely within the drug-like space
defined by the bioactive molecules from ChEMBL. Virtual screening using the
profile QSAR approach confirms that the potential of these novel molecules to
show bioactivity is comparable to the ChEMBL set from which they were derived.
The molecule generator written in Python used in this study is available on
request.Comment: in this version fixed some reference number
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