271 research outputs found
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning
Self-supervised learning is an efficient pre-training method for medical
image analysis. However, current research is mostly confined to
specific-modality data pre-training, consuming considerable time and resources
without achieving universality across different modalities. A straightforward
solution is combining all modality data for joint self-supervised pre-training,
which poses practical challenges. Firstly, our experiments reveal conflicts in
representation learning as the number of modalities increases. Secondly,
multi-modal data collected in advance cannot cover all real-world scenarios. In
this paper, we reconsider versatile self-supervised learning from the
perspective of continual learning and propose MedCoSS, a continuous
self-supervised learning approach for multi-modal medical data. Unlike joint
self-supervised learning, MedCoSS assigns different modality data to different
training stages, forming a multi-stage pre-training process. To balance modal
conflicts and prevent catastrophic forgetting, we propose a rehearsal-based
continual learning method. We introduce the k-means sampling strategy to retain
data from previous modalities and rehearse it when learning new modalities.
Instead of executing the pretext task on buffer data, a feature distillation
strategy and an intra-modal mixup strategy are applied to these data for
knowledge retention. We conduct continuous self-supervised pre-training on a
large-scale multi-modal unlabeled dataset, including clinical reports, X-rays,
CT scans, MRI scans, and pathological images. Experimental results demonstrate
MedCoSS's exceptional generalization ability across nine downstream datasets
and its significant scalability in integrating new modality data. Code and
pre-trained weight are available at https://github.com/yeerwen/MedCoSS
Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction
Multi-modal learning focuses on training models by equally combining multiple
input data modalities during the prediction process. However, this equal
combination can be detrimental to the prediction accuracy because different
modalities are usually accompanied by varying levels of uncertainty. Using such
uncertainty to combine modalities has been studied by a couple of approaches,
but with limited success because these approaches are either designed to deal
with specific classification or segmentation problems and cannot be easily
translated into other tasks, or suffer from numerical instabilities. In this
paper, we propose a new Uncertainty-aware Multi-modal Learner that estimates
uncertainty by measuring feature density via Cross-modal Random Network
Prediction (CRNP). CRNP is designed to require little adaptation to translate
between different prediction tasks, while having a stable training process.
From a technical point of view, CRNP is the first approach to explore random
network prediction to estimate uncertainty and to combine multi-modal data.
Experiments on two 3D multi-modal medical image segmentation tasks and three 2D
multi-modal computer vision classification tasks show the effectiveness,
adaptability and robustness of CRNP. Also, we provide an extensive discussion
on different fusion functions and visualization to validate the proposed model
Polymer-stabilized blue phase liquid crystal with a negative Kerr constant
A polymer-stabilized blue-phase liquid crystal (BPLC) with a negative Kerr constant is reported. In a voltage-on state, the double-twist BPLC molecules within the lattice cylinders are reoriented perpendicular to the applied electric field because of their negative dielectric anisotropy. As a result, the induced birefringence has a negative value, which leads to a negative Kerr constant. The negative sign of Kerr constant is experimentally validated by using a quarter-wave plate and a vertical field switching cell. Such a BPLC shows a negligible (similar to 1%) hysteresis and fast response time (similar to 1ms) at the room temperature, although its Kerr constant is relatively small because the employed host has a small Delta epsilon
Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering
Recently there is a growing focus on graph data, and multi-view graph
clustering has become a popular area of research interest. Most of the existing
methods are only applicable to homophilous graphs, yet the extensive real-world
graph data can hardly fulfill the homophily assumption, where the connected
nodes tend to belong to the same class. Several studies have pointed out that
the poor performance on heterophilous graphs is actually due to the fact that
conventional graph neural networks (GNNs), which are essentially low-pass
filters, discard information other than the low-frequency information on the
graph. Nevertheless, on certain graphs, particularly heterophilous ones,
neglecting high-frequency information and focusing solely on low-frequency
information impedes the learning of node representations. To break this
limitation, our motivation is to perform graph filtering that is closely
related to the homophily degree of the given graph, with the aim of fully
leveraging both low-frequency and high-frequency signals to learn
distinguishable node embedding. In this work, we propose Adaptive Hybrid Graph
Filter for Multi-View Graph Clustering (AHGFC). Specifically, a graph joint
process and graph joint aggregation matrix are first designed by using the
intrinsic node features and adjacency relationship, which makes the low and
high-frequency signals on the graph more distinguishable. Then we design an
adaptive hybrid graph filter that is related to the homophily degree, which
learns the node embedding based on the graph joint aggregation matrix. After
that, the node embedding of each view is weighted and fused into a consensus
embedding for the downstream task. Experimental results show that our proposed
model performs well on six datasets containing homophilous and heterophilous
graphs.Comment: Accepted by AAAI202
Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists
Lung cancer is a leading cause of death worldwide and early screening is
critical for improving survival outcomes. In clinical practice, the contextual
structure of nodules and the accumulated experience of radiologists are the two
core elements related to the accuracy of identification of benign and malignant
nodules. Contextual information provides comprehensive information about
nodules such as location, shape, and peripheral vessels, and experienced
radiologists can search for clues from previous cases as a reference to enrich
the basis of decision-making. In this paper, we propose a radiologist-inspired
method to simulate the diagnostic process of radiologists, which is composed of
context parsing and prototype recalling modules. The context parsing module
first segments the context structure of nodules and then aggregates contextual
information for a more comprehensive understanding of the nodule. The prototype
recalling module utilizes prototype-based learning to condense previously
learned cases as prototypes for comparative analysis, which is updated online
in a momentum way during training. Building on the two modules, our method
leverages both the intrinsic characteristics of the nodules and the external
knowledge accumulated from other nodules to achieve a sound diagnosis. To meet
the needs of both low-dose and noncontrast screening, we collect a large-scale
dataset of 12,852 and 4,029 nodules from low-dose and noncontrast CTs
respectively, each with pathology- or follow-up-confirmed labels. Experiments
on several datasets demonstrate that our method achieves advanced screening
performance on both low-dose and noncontrast scenarios.Comment: MICCAI 202
OIP5-AS1/CD147/TRPM7 axis promotes gastric cancer metastasis by regulating apoptosis related PI3K-Akt signaling
BackgroundTo explore the mechanism of OIP5-AS1/CD147/TRPM7 axis to gastric cancer (GC) metastasis.MethodsBioinformatic analysis was performed to pick up the candidate genes associated with regulation GC metastasis. Using GC cell lines, AGS and MKN-45 as research objects, identify the effect of candidate genes on GC metastasis, judge cell proliferation status by MTT assay and cell clone number, and detect cell migration by Transwell and Wound-healing assay. The molecular mechanism of CD147/OIP5/TRPM7 axis regulating GC metastasis was further explored by RNA sequencing. The key signaling pathways were subsequently verified by flow cytometry and WB.ResultsBioinformatic analysis suggested OIP5-AS1/CD147/TRPM7 axis may be involving in GC metastasis. The RNA interference experiment proved that after gene interference, the proliferation ability of GC cells decreased significantly (P<0.05), which was manifested in the reduction of the number of cell clones. In addition, the migration ability of GC cells was also affected, which was based on the results of Wound Healing (P<0.05). CD147, OIP5-AS1 and TRPM7 all have harmful effects on GC cells. The relationship between OIP5-AS1 and CD147/TRPM7 was detected by RNA immunoprecipitation. Moreover, the RNA sequencing data indicated that CD147/OIP5-AS1/TRPM7 may coordinately regulate the PI3K-AKT pathway related to GC cell apoptosis, thereby affecting the proliferation and migration of GC cells. After RNA interference, the level of apoptosis increased both in AGS and MKN-45 cells. Meanwhile, the expression of pro-apoptotic proteins Caspase9 and BAX were up-regulated (P<0.05). In addition, the expression of PI3K and AKT proteins was reduced (P<0.05). The mouse tumorigenesis experiment corroborated the results of the in vitro study.ConclusionOIP5-AS1/CD147/TRPM7 axis reduces GC cell proliferation by regulating apoptosis associated with PI3K-AKT signaling, further affecting cancer metastasis
Free energy landscape for the binding process of Huperzine A to acetylcholinesterase
Drug-target residence time (t = 1/koff, where koff is the dissociation
rate constant) has become an important index in discovering betteror
best-in-class drugs. However, little effort has been dedicated to
developing computational methods that can accurately predict this
kinetic parameter or related parameters, koff and activation free
energy of dissociation (ΔGâ‰
off). In this paper, energy landscape theory
that has been developed to understand protein folding and function
is extended to develop a generally applicable computational framework
that is able to construct a complete ligand-target binding free
energy landscape. This enables both the binding affinity and the
binding kinetics to be accurately estimated.We applied this method
to simulate the binding event of the anti-Alzheimer’s disease drug
(−)−Huperzine A to its target acetylcholinesterase (AChE). The computational
results are in excellent agreement with our concurrent
experimental measurements. All of the predicted values of binding
free energy and activation free energies of association and dissociation
deviate from the experimental data only by less than 1 kcal/
mol. The method also provides atomic resolution information for the
(−)−Huperzine A binding pathway, which may be useful in designing
more potent AChE inhibitors. We expect thismethodology to be
widely applicable to drug discovery and development
Mechanism of intercalation and deintercalation of lithium ions in graphene nanosheets
Graphene nanosheets (GNSs) were synthesized by reducing exfoliated graphite oxides. Their structure, surface morphology and lithium storage mechanism were characterized and investigated systematically using X-ray diffraction, atomic force microscopy, scanning electron microscopy, charge-discharge tests, cyclic voltammetry and electrochemical impedance spectroscopy. It was found that the GNSs, which were obtained via chemical synthesis, were primarily less than 10 graphene layers. The GNS electrodes, which were fabricated from the reduced GNSs, exhibited an enhanced reversible lithium storage capacity and good cyclic stability when serving as anodes in lithium-ion batteries. Also, the first-cycle irreversible capacities of the system were relatively high, because of the formation of a solid electrolyte interphase film on the surface of the GNS electrode and the spontaneous stacking of GNSs during the first lithiation. The electrochemical impedance spectroscopy results suggest that the solid electrolyte interphase film on the GNS electrode during first lithiation were primarily formed at potentials between 0.95 and 0.7 V. Also, the symmetry factor of the charge transfer was measured to be 0.446.Fundamental Research Funds for the Central Universities[2010LKHX03, 2010QNB04, 2010QNB05]; China University of Mining Technology[ON090237
The Effects of Ambient Temperature on Lumbar Disc Herniation: A Retrospective Study
PurposeThis article was designed to provide critical evidence into the relationship between ambient temperature and intensity of back pain in people with lumbar disc herniation (LDH).MethodsData concerning patient's age, gender, diagnostic logout, admission time, discharge time, residence area, and work area (residence area and work area were used to ensure research area) from 2017 to 2019 were obtained from the Neck-Shoulder and Lumbocrural Pain Hospital in Jinan, China. A total of 1,450 hospitalization records were collected in total. The distributed lag non-linear model (DLNM) was used to evaluate the relationship between lag–response and exposure to ambient temperature. Stratification was based on age and gender. Days 1, 5, 20, and 28 prior to admission were denoted as lags 0, 5, 20, and 28, respectively.ResultsAn average daily temperature of 15–23°C reduced the risk of hospitalization the most in men. Conversely, temperatures <10°C drastically increased hospitalization in men, particularly in lags 0–5 and lags 20–28. Men aged between 40 and 50 years old showed less effect in pain sensation during ambient temperature.ConclusionHigh or low ambient temperature can increase the hospitalization risk of LDH, and sometimes, the temperature effect is delayed
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