Reconstruction of HMBC Correlation Networks: A Novel NMR-Based Contribution to Metabolite Mixture Analysis

AbstractThe COVID-19 lockdown has drastically limited social interactions and brought about a climate of fear and uncertainty. These circumstances not only increased affective symptoms and social isolation among community dwelling older adults but also alter the dynamics between them. Using network analyses, we study the changes in these dynamics before and during the lockdown. Community-dwelling older adults (N = 419) completed questionnaires assessing depression, anxiety, and social isolation, before the COVID-19 pandemic, as part of a cohort study, and during the lockdown period. The total scores of these questionnaires were compared across time. For the network analyses, partial correlation networks were constructed using items in the questionnaires as nodes, separately at both timepoints. Changes in edges, as well as nodal and bridge centrality were examined across time. Depression and anxiety symptoms, and social isolation had significantly increased during the lockdown. Significant changes were observed across time on several edges. Greater connectivity between the affective and social isolation nodes at lockdown was observed. Depression symptoms have become more tightly coupled across individuals, and so were the anxiety symptoms. Depression symptoms have also become slightly decoupled from those of anxiety. These changing network dynamics reflect the greater influence of social isolation on affective symptoms across individuals and an increased vulnerability to affective disorders. These findings provide novel perspectives and translational implications on the changing mental health context amidst a COVID-19 pandemic situation.

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A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

Remote Sensing ◽

10.3390/rs13122333 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2333

Author(s):

Lilu Zhu ◽

Xiaolu Su ◽

Yanfeng Hu ◽

Xianqing Tai ◽

Kun Fu

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Temporal Correlation ◽

Local Association ◽

Sensing Data ◽

Multi Scale ◽

Correlation Networks ◽

Efficient Integration ◽

Spatio Temporal ◽

Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

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Critical Assessment: Use of Supersonic Jet Spectrometry for Complex Mixture Analysis (IUPAC Technical Report)

Chemistry International ◽

10.1515/ci.2003.25.5.22 ◽

2003 ◽

Vol 25 (5) ◽

Keyword(s):

Supersonic Jet ◽

Complex Mixture ◽

Critical Assessment ◽

Mixture Analysis ◽

Technical Report ◽

Complex Mixture Analysis

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