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 $\times5$ to $\times27$ 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