Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.

Introduction: Dynamic functional connectivity (dFC), which can capture the abnormality of brain activity over time in resting-state functional magnetic resonance imaging (rs-fMRI) data, has a natural advantage in revealing the abnormal mechanism of brain activity in patients with Attention Deficit/Hyperactivity Disorder (ADHD). Several deep learning methods have been proposed to learn dynamic changes from rs-fMRI for FC analysis, and achieved superior performance than those using static FC. However, most existing methods only consider dependencies of two adjacent timestamps, which is limited when the change is related to the course of many timestamps. Methods: In this paper, we propose a novel Temporal Dependence neural Network (TDNet) for FC representation learning and temporal-dependence relationship tracking from rs-fMRI time series for automated ADHD identification. Specifically, we first partition rs-fMRI time series into a sequence of consecutive and non-overlapping segments. For each segment, we design an FC generation module to learn more discriminative representations to construct dynamic FCs. Then, we employ the Temporal Convolutional Network (TCN) to efficiently capture long-range temporal patterns with dilated convolutions, followed by three fully connected layers for disease prediction. Results: As the results, we found that considering the dynamic characteristics of rs-fMRI time series data is beneficial to obtain better diagnostic performance. In addition, dynamic FC networks generated in a data-driven manner are more informative than those constructed by Pearson correlation coefficients. Discussion: We validate the effectiveness of the proposed approach through extensive experiments on the public ADHD-200 database, and the results demonstrate the superiority of the proposed model over state-of-the-art methods in ADHD identification.

Nitrogen addition may promote soil organic carbon storage and CO2 emission but reduce dissolved organic carbon in Zoige peatland.

With the increase of nitrogen (N) deposition, N input can affect soil C cycling since microbes may trigger a series of activities to balance the supply and demand of nutrients. However, as one of the largest C sinks on earth, the role of extra N addition in affecting peatland soil C and its potential mechanism remains unclear and debated. Therefore, this study chose the largest peatland in China (i.e., Zoige, mostly N-limited) to systematically explore the potential changes of soil C, microbes, and ecoenzymes caused by extra N input at the lab scale incubation. Three different types of soils were collected and incubated with different levels of NH4NO3 solution for 45 days. After incubation, N input generally increased soil organic C (SOC) but decreased dissolved organic carbon (DOC) in Zoige peatland soils. Moreover, CO2 and CH4 emissions were significantly increased after high N input (equal to 5 mg NH4NO3 g-1 dry soils). Through a series of analyses, it was observed that microbial communities and ecoenzyme activities mainly influenced the changes of different C components. Collectively, this study implied that the increasing N deposition might help C sequestration in N-limited peatland soils; simultaneously, the risk of increased CO2 and CH4 by N input in global warming should not be ignored.

The role of proactive behavior on COVID-19 infordemic in the Chinese Sina-Microblog: a modeling study.

In order to avoid forming an information cocoon, the information propagation of COVID-19 is usually created through the action of "proactive search", an important behavior other than "reactive follow". This behavior has been largely ignored in modeling information dynamics. Here, we propose to fill in this gap by proposing a proactive-reactive susceptible-discussing-immune (PR-SFI) model to describe the patterns of co-propagation on social networks. This model is based on the forwarding quantity and takes into account both proactive search and reactive follow behaviors. The PR-SFI model is parameterized by data fitting using real data of COVID-19 related topics in the Chinese Sina-Microblog, and the model is calibrated and validated using the prediction accuracy of the accumulated forwarding users. Our sensitivity analysis and numerical experiments provide insights about optimal strategies for public health emergency information dissemination.

Quantify the role of superspreaders -opinion leaders- on COVID-19 information propagation in the Chinese Sina-microblog.

BACKGROUD: Effective communication of accurate information through social media constitutes an important component of public health interventions in modern time, when traditional public health approaches such as contact tracing, quarantine and isolation are among the few options for the containing the disease spread in the population. The success of control of COVID-19 outbreak started from Wuhan, the capital city of Hubei Province of China relies heavily on the resilience of residents to follow public health interventions which induce substantial interruption of social-economic activities, and evidence shows that opinion leaders have been playing significant roles in the propagation of epidemic information and public health policy and implementations. METHODS: We design a mathematical model to quantify the roles of information superspreaders in single specific information which outbreaks rapidly and usually has a short duration period, and to examine the information propagation dynamics in the Chinese Sina-microblog. Our opinion-leader susceptible-forwarding-immune (OL-SFI) model is formulated to track the temporal evolution of forwarding quantities generated by opinion leaders and normal users. RESULTS: Data fitting from the real data of COVID-19 obtained from Chinese Sina-microblog can identify the different contact rates and forwarding probabilities (and hence calculate the basic information forwarding reproduction number of superspreaders), and can be used to evaluate the roles of opinion leaders in different stages of the information propagation and the outbreak unfolding. CONCLUSIONS: The parameterized model can be used to nearcast the information propagation trend, and the model-based sensitivity analysis can help to explore important factors for the roles of opinion leaders.