Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks

Greenhouse gas emissions from U.S. crude oil pipeline accidents:1968 to 2020

Abstract Crude oil pipelines are considered as the lifelines of energy industry. However, accidents of the pipelines can lead to severe public health and environmental concerns, in which greenhouse gas (GHG) emissions, primarily methane, are frequently overlooked. While previous studies examined fugitive emissions in normal operation of crude oil pipelines, emissions resulting from accidents were typically managed separately and were therefore not included in the emission account of oil systems. To bridge this knowledge gap, we employed a bottom-up approach to conducted the first-ever inventory of GHG emissions resulting from crude oil pipeline accidents in the United States at the state level from 1968 to 2020, and leveraged Monte Carlo simulation to estimate the associated uncertainties. Our results reveal that GHG emissions from accidents in gathering pipelines (~720,000 tCO2e) exceed those from transmission pipelines (~290,000 tCO2e), although significantly more accidents have occurred in transmission pipelines (6883 cases) than gathering pipelines (773 cases). Texas accounted for over 40% of total accident-related GHG emissions nationwide. Our study contributes to enhanced accuracy of the GHG account associated with crude oil transport and implementing the data-driven climate mitigation strategies

Wideband hybrid metamaterial absorber via compound design of multiple mechanisms

Broadband and high efficiency are the two core indexes of absorption research, which usually requires a balance between them. Therefore, how to take into account both and achieve broadband and efficient absorption is a hot topic in current research. In this paper, by the compound design of multiple mechanisms, a kind of wideband hybrid metamaterial absorber (HMA) is proposed. The overall structure consists of a layer of patterned resistive film and a layer of magnetic absorbing material (MAM) separated by the air. The resistive layer is designed as square ring type to regulate the local magnetic field, which results in significant magnetic field enhancement within the MAM layer, and this mechanism provides a prerequisite for wideband and high-efficiency absorption in the low frequency band. Furthermore, due to the electrical losses of the resistive film, another absorption band is additionally excited in the high frequency band. Thanks to the multiple mechanisms, the absorption efficiency above 90% in the 3.2–22.0 GHz frequency band can be realized, and the thickness of the overall structure is 7.0 mm that is 0.07 of the wavelengths at the lowest frequency point. To demonstrate this method, a prototype is designed, fabricated and measured. Both the simulation and experiment results verify the effectiveness of the proposed method. This work provides a new method to design wideband and high-efficiency electromagnetic absorption structures and may find potential applications in multi-functional planar or conformal structures

Low-Dimensional Dynamics of Brain Activity Associated with Manual Acupuncture in Healthy Subjects

Acupuncture is one of the oldest traditional medical treatments in Asian countries. However, the scientific explanation regarding the therapeutic effect of acupuncture is still unknown. The much-discussed hypothesis it that acupuncture’s effects are mediated via autonomic neural networks; nevertheless, dynamic brain activity involved in the acupuncture response has still not been elicited. In this work, we hypothesized that there exists a lower-dimensional subspace of dynamic brain activity across subjects, underpinning the brain’s response to manual acupuncture stimulation. To this end, we employed a variational auto-encoder to probe the latent variables from multichannel EEG signals associated with acupuncture stimulation at the ST36 acupoint. The experimental results demonstrate that manual acupuncture stimuli can reduce the dimensionality of brain activity, which results from the enhancement of oscillatory activity in the delta and alpha frequency bands induced by acupuncture. Moreover, it was found that large-scale brain activity could be constrained within a low-dimensional neural subspace, which is spanned by the “acupuncture mode”. In each neural subspace, the steady dynamics of the brain in response to acupuncture stimuli converge to topologically similar elliptic-shaped attractors across different subjects. The attractor morphology is closely related to the frequency of the acupuncture stimulation. These results shed light on probing the large-scale brain response to manual acupuncture stimuli

Online public concern about allergic rhinitis and its association with COVID-19 and air quality in China: an informative epidemiological study using Baidu index

Abstract Background Allergic rhinitis is a common health concern that affects quality of life. This study aims to examine the online search trends of allergic rhinitis in China before and after the COVID-19 epidemic and to explore the association between the daily air quality and online search volumes of allergic rhinitis in Beijing. Methods We extracted the online search data of allergic rhinitis-related keywords from the Baidu index database from January 23, 2017 to June 23, 2022. We analyzed and compared the temporal distribution of online search behaviors across different themes of allergic rhinitis before and after the COVID-19 pandemic in mainland China, using the Baidu search index (BSI). We also obtained the air quality index (AQI) data in Beijing and assessed its correlation with daily BSIs of allergic rhinitis. Results The online search for allergic rhinitis in China showed significant seasonal variations, with two peaks each year in spring from March to May and autumn from August and October. The BSI of total allergic rhinitis-related searches increased gradually from 2017 to 2019, reaching a peak in April 2019, and declined after the COVID-19 pandemic, especially in the first half of 2020. The BSI for all allergic rhinitis themes was significantly lower after the COVID-19 pandemic than before (all p values < 0.05). The results also revealed that, in Beijing, there was a significant negative association between daily BSI and AQI for each allergic rhinitis theme during the original variant strain epidemic period and a significant positive correlation during the Omicron variant period. Conclusion Both air quality and the interventions used for COVID-19 pandemic, including national and local quarantines and mask wearing behaviors, may have affected the incidence and public concern about allergic rhinitis in China. The online search trends can serve as a valuable tool for tracking real-time public concerns about allergic rhinitis. By complementing traditional disease monitoring systems of health departments, these search trends can also offer insights into the patterns of disease outbreaks. Additionally, they can provide references and suggestions regarding the public’s knowledge demands related to allergic rhinitis, which can further be instrumental in developing targeted strategies to enhance population-based disease education on allergic diseases

An inventory of greenhouse gas emissions due to natural gas pipeline incidents in the United States and Canada from 1980s to 2021

Abstract Natural gas is believed to be a critical transitional energy source. However, natural gas pipelines, once failed, will contribute to a large amount of greenhouse gas (GHG) emissions, including methane from uncontrolled natural gas venting and carbon dioxide from flared natural gas. However, the GHG emissions caused by pipeline incidents are not included in the regular inventories, making the counted GHG amount deviate from the reality. This study, for the first time, establishes an inventory framework for GHG emissions including all natural gas pipeline incidents in the two of the largest gas producers and consumers in North America (United States and Canada) from 1980s to 2021. The inventory comprises GHG emissions resulting from gathering and transmission pipeline incidents in a total of 24 states or regions in the United States between 1970 and 2021, local distribution pipeline incidents in 22 states or regions between 1970 and 2021, as well as natural gas pipeline incidents in a total of 7 provinces or regions in Canada between 1979 and 2021. These datasets can improve the accuracy of regular emission inventories by covering more emission sources in the United States and Canada and provide essential information for climate-oriented pipeline integrity management

In Vitro and In Vivo Anti-AChE and Antioxidative Effects of Schisandra chinensis Extract: A Potential Candidate for Alzheimer’s Disease

Acetylcholinesterase (AChE) inhibition and antioxidants are two common strategies for the treatment in the early stage of Alzheimer’s Disease (AD). In this study, extracts from nine traditional Chinese medical (TCM) herbs were tested for anti-AChE activity by Ellman’s microplate assay and cytotoxicity by CCK-8. Based on its excellent AChE inhibition effect and its lowest cytotoxicity, Schisandra chinensis (SC) extract was selected to do the mechanism research. SC extract protected pheochromocytoma (PC12) cells against H2O2-induced toxicity by improving the cell survival rate in a dose-dependent manner. And it also showed significant free radical (DPPH) scavenging activities, ferric reducing antioxidant power (FRAP), and 2,2′-Azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging. To confirm these results, the scopolamine-induced mice models were utilized in this study. Compared with the positive drug (piracetam), SC could also exhibit similar effects to alleviate the mice’s cognitive deficits. Moreover, in the mice brain samples, the AChE activity and malondialdehyde (MDA) levels of SC-treatment group both showed a reverse as compared to model group. Taken together, these results all suggested that SC extract may be a potential therapeutic candidate for AD