121 research outputs found
Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals
Heart failure is a debilitating condition that affects millions of people
worldwide and has a significant impact on their quality of life and mortality
rates. An objective assessment of cardiac pressures remains an important method
for the diagnosis and treatment prognostication for patients with heart
failure. Although cardiac catheterization is the gold standard for estimating
central hemodynamic pressures, it is an invasive procedure that carries
inherent risks, making it a potentially dangerous procedure for some patients.
Approaches that leverage non-invasive signals - such as electrocardiogram (ECG)
- have the promise to make the routine estimation of cardiac pressures feasible
in both inpatient and outpatient settings. Prior models trained to estimate
intracardiac pressures (e.g., mean pulmonary capillary wedge pressure (mPCWP))
in a supervised fashion have shown good discriminatory ability but have been
limited to the labeled dataset from the heart failure cohort. To address this
issue and build a robust representation, we apply deep metric learning (DML)
and propose a novel self-supervised DML with distance-based mining that
improves the performance of a model with limited labels. We use a dataset that
contains over 5.4 million ECGs without concomitant central pressure labels to
pre-train a self-supervised DML model which showed improved classification of
elevated mPCWP compared to self-supervised contrastive baselines. Additionally,
the supervised DML model that uses ECGs with access to 8,172 mPCWP labels
demonstrated significantly better performance on the mPCWP regression task
compared to the supervised baseline. Moreover, our data suggest that DML yields
models that are performant across patient subgroups, even when some patient
subgroups are under-represented in the dataset. Our code is available at
https://github.com/mandiehyewon/ssldm
Impact of environmental changes on the dynamics of temporal networks
Dynamics of complex social systems has often been described in the framework
of temporal networks, where links are considered to exist only at the moment of
interaction between nodes. Such interaction patterns are not only driven by
internal interaction mechanisms, but also affected by environmental changes. To
investigate the impact of the environmental changes on the dynamics of temporal
networks, we analyze several face-to-face interaction datasets using the
multiscale entropy (MSE) method to find that the observed temporal correlations
can be categorized according to the environmental similarity of datasets such
as classes and break times in schools. By devising and studying a temporal
network model considering a periodically changing environment as well as a
preferential activation mechanism, we numerically show that our model could
successfully reproduce various empirical results by the MSE method in terms of
multiscale temporal correlations. Our results demonstrate that the
environmental changes can play an important role in shaping the dynamics of
temporal networks when the interactions between nodes are influenced by the
environment of the systems.Comment: 10 pages, 8 figure
End-to-End Learnable Multi-Scale Feature Compression for VCM
The proliferation of deep learning-based machine vision applications has
given rise to a new type of compression, so called video coding for machine
(VCM). VCM differs from traditional video coding in that it is optimized for
machine vision performance instead of human visual quality. In the feature
compression track of MPEG-VCM, multi-scale features extracted from images are
subject to compression. Recent feature compression works have demonstrated that
the versatile video coding (VVC) standard-based approach can achieve a BD-rate
reduction of up to 96% against MPEG-VCM feature anchor. However, it is still
sub-optimal as VVC was not designed for extracted features but for natural
images. Moreover, the high encoding complexity of VVC makes it difficult to
design a lightweight encoder without sacrificing performance. To address these
challenges, we propose a novel multi-scale feature compression method that
enables both the end-to-end optimization on the extracted features and the
design of lightweight encoders. The proposed model combines a learnable
compressor with a multi-scale feature fusion network so that the redundancy in
the multi-scale features is effectively removed. Instead of simply cascading
the fusion network and the compression network, we integrate the fusion and
encoding processes in an interleaved way. Our model first encodes a
larger-scale feature to obtain a latent representation and then fuses the
latent with a smaller-scale feature. This process is successively performed
until the smallest-scale feature is fused and then the encoded latent at the
final stage is entropy-coded for transmission. The results show that our model
outperforms previous approaches by at least 52% BD-rate reduction and has
to times less encoding time for object detection. It is
noteworthy that our model can attain near-lossless task performance with only
0.002-0.003% of the uncompressed feature data size.Comment: Under peer review for IEEE TCSV
The variation of relative magnetic helicity around major flares
We have investigated the variation of magnetic helicity over a span of
several days around the times of 11 X-class flares which occurred in seven
active regions (NOAA 9672, 10030, 10314, 10486, 10564, 10696, and 10720) using
the magnetograms taken by the Michelson Doppler Imager (MDI) on board the Solar
and Heliospheric Observatory (SOHO). As a major result we found that each of
these major flares was preceded by a significant helicity accumulation over a
long period (0.5 to a few days). Another finding is that the helicity
accumulates at a nearly constant rate and then becomes nearly constant before
the flares. This led us to distinguish the helicity variation into two phases:
a phase of monotonically increasing helicity and the following phase of
relatively constant helicity. As expected, the amount of helicity accumulated
shows a modest correlation with time-integrated soft X-ray flux during flares.
However, the average helicity change rate in the first phase shows even
stronger correlation with the time-integrated soft X-ray flux. We discuss the
physical implications of this result and the possibility that this
characteristic helicity variation pattern can be used as an early warning sign
for solar eruptions
Decursinol chloroacrylates useful as fungicides
Natural products decursin and decursinol angelate were recently reported as benign fungicides for controlling rice blast. Inspired by the structural similarity of the cumarin compounds and gained hint from the skeletal motifs, we designed and prepared synthetic compounds to increase the natural product efficacy and evaluated their antifungal activities against various plant disease pathogens in vitro. Synthetically prepared compound 4 and 5 indeed suppressed the mycelial growth of B. cinerea, F. oxysporum, P. italicum, and R. quercus-mongolicae. Additionally, compound 5 effectively prevents the growth of C. coccodes and C. parasitica. Furthermore, both 4 and 5 possess better inhibitory activities on spore germination of F. oxysporum and M. oryzae than the natural product decursin and commercial pesticide Iprodione. These results suggest that the effect of the lead compound for plant disease protection can be improved by tuning the structure of the original natural product and decursinol chloroacrylates 4 and 5 are candidates for the control of F. oxysporum and M. oryzae.This work was supported by Research Resettlement Fund for the new faculty of Seoul National University (to Y. Kwon). Also, Rural Development Administration Republic of Korea supported this research (Project No. PJ016243022021 to H. Son)
Demography of SDSS early-type galaxies from the perspective of radial color gradients
We have investigated the radial g-r color gradients of early-type galaxies in
the Sloan Digital Sky Survey (SDSS) DR6 in the redshift range 0.00<z<0.06. The
majority of massive early-type galaxies show a negative color gradient
(red-cored) as generally expected for early-type galaxies. On the other hand,
roughly 30 per cent of the galaxies in this sample show a positive color
gradient (blue-cored). These "blue-cored" galaxies often show strong H beta
absorption line strengths and/or emission line ratios that are indicative of
the presence of young stellar populations. Combining the optical data with
Galaxy Evolution Explorer (GALEX) UV photometry, we find that all blue-cored
galaxies show UV-optical colors that can only be explained by young stellar
populations. This implies that most of the residual star formation in
early-type galaxies is centrally concentrated. Blue-cored galaxies are
predominantly low velocity dispersion systems. A simple model shows that the
observed positive color gradients (blue-cored) are visible only for a billion
years after a star formation episode for the typical strength of recent star
formation. The observed effective radius decreases and the mean surface
brightness increases due to this centrally-concentrated star formation episode.
As a result, the majority of blue-cored galaxies may lie on different regions
in the Fundamental Plane from red-cored ellipticals. However, the position of
the blue-cored galaxies on the Fundamental Plane cannot be solely attributed to
recent star formation but require substantially lower velocity dispersion. We
conclude that a low-level of residual star formation persists at the centers of
most of low-mass early-type galaxies, whereas massive ones are mostly quiescent
systems with metallicity-driven red cores.Comment: 15 pages, 19 figures, accepted for publication in ApJ
Mannosylated-serum albumin nanoparticle imaging to monitor tumor-associated macrophages under anti-PD1 treatment
Background
Immune checkpoint inhibitors such as anti-programmed cell death protein 1 (PD1) block tumor growth
by reinvigorating the immune system; however, determining their efcacy only by the changes in tumor size may
prove inaccurate. As the immune cells including macrophages in the tumor microenvironment (TME) are associ‑
ated with the response to anti-PD1 therapy, tumor-associated macrophages (TAMs) imaging using nanoparticles can
noninvasively provide the immune enrichment status of TME. Herein, the mannosylated-serum albumin (MSA) nano‑
particle was labeled with radioactive isotope 68Ga to target the mannose receptors on macrophages for noninvasive
monitoring of the TME according to anti-PD1 therapy.
Results
B16F10-Luc and MC38-Luc tumor-bearing mice were treated with anti-PD1, and the response to anti-PD1
was determined by the tumor volume. According to the fow cytometry, the responders to anti-PD1 showed an
increased proportion of TAMs, as well as lymphocytes, and the most enriched immune cell population in the TME
was also TAMs. For noninvasive imaging of TAMs as a surrogate of immune cell augmentation in the TME via anti-PD1,
we acquired [
68Ga] Ga-MSA positron emission tomography. According to the imaging study, an increased number of
TAMs in responders at the early phase of anti-PD1 treatment was observed in both B16F10-Luc and MC38-Luc tumorbearing mice models.
Conclusion
As representative immune cells in the TME, non-invasive imaging of TAMs using MSA nanoparticles can
refect the immune cell enrichment status in the TME closely associated with the response to anti-PD1. As non-inva‑
sive imaging using MSA nanoparticles, this approach shows a potential to monitor and evaluate anti-tumor response
to immune checkpoint inhibitors.This research was supported by the Basic Science Research Program through
the National Research Foundation of Korea (NRF), funded by the Ministry of
Education (NRF-2020R1A6A3A13069409), the Korean government the Ministry
of Science, ICT and Future Planning (2020R1A2C2010202, 2020R1A4A2002903,
2020M3A9B6038086, 2021M2E7A2079570, 2021R1A2C3009427,
2022M3E5F2018261), and the Ministry of Health and Welfare (HI19C0339,
HN22C0644). This study also was carried out by the research fund supported
by the fund project of Park Yang Sook - Chung Yung Ho in Seoul National
University
Brain structural correlates of subjective sleepiness and insomnia symptoms in shift workers
BackgroundStudies on the brain structures of shift workers are limited; thus, this cross-sectional study aimed to compare the brain structures and the brain structural correlates of subjective sleepiness and insomnia symptoms between shift workers and non-shift workers.MethodsShift workers (n = 63) and non-shift workers (n = 58) completed questionnaires assessing subjective sleepiness and insomnia symptoms. Cortical thickness, cortical surface area, and subcortical volumes were measured by magnetic resonance imaging. The brain morphometric measures were compared between the groups, and interaction analyses using the brain morphometric measures as the dependent variable were performed to test the interactions between the study group and measures of sleep disturbance (i.e., subjective sleepiness and insomnia symptoms).ResultsNo differences in cortical thickness, cortical surface area, or subcortical volumes were detected between shift workers and non-shift workers. A single cluster in the left motor cortex showed a significant interaction between the study group and subjective sleepiness in the cortical surface area. The correlation between the left motor cortex surface area and the subjective sleepiness level was negative in shift workers and positive in non-shift workers. Significant interaction between the study group and insomnia symptoms was present for the left/right putamen volumes. The correlation between the left/right putamen volumes and insomnia symptom levels was positive in shift workers and negative in non-shift workers.ConclusionLeft motor cortex surface area and bilateral putamen volumes were unique structural correlates of subjective sleepiness and insomnia symptoms in shift workers, respectively
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