106 research outputs found
Design Principles of Pancreatic Islets: Glucose-dependent Coordination of Hormone Pulses
Pancreatic islets are functional units involved in glucose homeostasis. The
multicellular system comprises three main cell types; and
cells reciprocally decrease and increase blood glucose by producing insulin and
glucagon pulses, while the role of cells is less clear. Although their
spatial organization and the paracrine/autocrine interactions between them have
been extensively studied, the functional implications of the design principles
are still lacking. In this study, we formulated a mathematical model that
integrates the pulsatility of hormone secretion and the interactions and
organization of islet cells and examined the effects of different cellular
compositions and organizations in mouse and human islets. A common feature of
both species was that islet cells produced synchronous hormone pulses under
low- and high- glucose conditions, while they produced asynchronous hormone
pulses under normal glucose conditions. However, the synchronous coordination
of insulin and glucagon pulses at low glucose was more pronounced in human
islets that had more cells. When cells were selectively
removed to mimic diabetic conditions, the anti-synchronicity of insulin and
glucagon pulses was deteriorated at high glucose, but it could be partially
recovered when the re-aggregation of remaining cells was considered. Finally,
the third cell type, cells, which introduced additional complexity in
the multicellular system, prevented the excessive synchronization of hormone
pulses. Our computational study suggests that controllable synchronization is a
design principle of pancreatic islets.Comment: 24 pages, 7 figure
Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles
Machine learning shows remarkable success for recognizing patterns in data.
Here we apply the machine learning (ML) for the diagnosis of early stage
diabetes, which is known as a challenging task in medicine. Blood glucose
levels are tightly regulated by two counter-regulatory hormones, insulin and
glucagon, and the failure of the glucose homeostasis leads to the common
metabolic disease, diabetes mellitus. It is a chronic disease that has a long
latent period the complicates detection of the disease at an early stage. The
vast majority of diabetics result from that diminished effectiveness of insulin
action. The insulin resistance must modify the temporal profile of blood
glucose. Thus we propose to use ML to detect the subtle change in the temporal
pattern of glucose concentration. Time series data of blood glucose with
sufficient resolution is currently unavailable, so we confirm the proposal
using synthetic data of glucose profiles produced by a biophysical model that
considers the glucose regulation and hormone action. Multi-layered perceptrons,
convolutional neural networks, and recurrent neural networks all identified the
degree of insulin resistance with high accuracy above .Comment: 4 pages, 2 figur
Information flows of diverse autoencoders
The outstanding performance of deep learning in various fields has been a
fundamental query, which can be potentially examined using information theory
that interprets the learning process as the transmission and compression of
information. Information plane analyses of the mutual information between the
input-hidden-output layers demonstrated two distinct learning phases of fitting
and compression. It is debatable if the compression phase is necessary to
generalize the input-output relations extracted from training data. In this
study, we investigated this through experiments with various species of
autoencoders and evaluated their information processing phase with an accurate
kernel-based estimator of mutual information. Given sufficient training data,
vanilla autoencoders demonstrated the compression phase, which was amplified
after imposing sparsity regularization for hidden activities. However, we found
that the compression phase is not universally observed in different species of
autoencoders, including variational autoencoders, that have special constraints
on network weights or manifold of hidden space. These types of autoencoders
exhibited perfect generalization ability for test data without requiring the
compression phase. Thus, we conclude that the compression phase is not
necessary for generalization in representation learning
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