scholarly journals Separating Clinical and Subclinical Depression by Big Data Informed Structural Vulnerability Index and Its impact on Cognition: ENIGMA Dot Product

2021 ◽  
Author(s):  
Peter Kochunov ◽  
Yizhou Ma ◽  
Kathryn S. Hatch ◽  
Lianne Schmaal ◽  
Neda Jahanshad ◽  
...  
Keyword(s):  
Big Data ◽  
Vulnerability Index ◽  
Structural Vulnerability ◽  
Dot Product ◽  
Data Informed ◽  
Subclinical Depression ◽  
Structural Vulnerability Index

scholarly journals Separating Clinical and Subclinical Depression by Big Data Informed Structural Vulnerability Index and Its impact on Cognition: ENIGMA Dot Product

2021 ◽  
Author(s):  
Peter Kochunov ◽  
Yizhou Ma ◽  
Kathryn S Hatch ◽  
Lianne Schmaal ◽  
Neda Jahanshad ◽  
...  
Keyword(s):  
Big Data ◽  
Depressive Symptoms ◽  
Brain Imaging ◽  
Cognitive Performance ◽  
Meta Analysis ◽  
Vulnerability Index ◽  
Imaging Genetics ◽  
Cognitive Tests ◽  
Correlation Pattern ◽  
Dot Product

Big Data neuroimaging collaborations including Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) integrated worldwide data to identify regional brain deficits in major depressive disorder (MDD). We evaluated the sensitivity of translating ENIGMA-defined MDD deficit patterns to the individual level. We treated ENIGMA MDD deficit patterns as a vector to gauge the similarity between individual and MDD patterns by calculating ENIGMA dot product (EDP). We analyzed the sensitivity and specificity of EDP in separating subjects with (1) subclinical depressive symptoms without a diagnosis of MDD, (2) single episode MDD, (3) recurrent MDD, and (4) controls free of neuropsychiatric disorders. We compared EDP to the Quantile Regression Index (QRI; a linear alternative to the brain age metric) and the global gray matter thickness and subcortical volumes and fractional anisotropy (FA) of water diffusion. We performed this analysis in a large epidemiological sample of UK Biobank (UKBB) participants (N=17,053/19,265 M/F). Group-average increases in depressive symptoms from controls to recurrent MDD was mirrored by EDP (r2=0.85), followed by FA (r2=0.81) and QRI (r2=0.56). Subjects with MDD showed worse performance on cognitive tests than controls with deficits observed for 3 out of 9 cognitive tests administered by the UKBB. We calculated correlations of EDP and other brain indices with measures of cognitive performance in controls. The correlation pattern between EDP and cognition in controls was similar (r2=0.75) to the pattern of cognitive differences in MDD. This suggests that the elevation in EDP, even in controls, is associated with cognitive performance - specifically in the MDD-affected domains. That specificity was missing for QRI, FA or other brain imaging indices. In summary, translating anatomically informed meta-analytic indices of similarity using a linear vector approach led to better sensitivity to depressive symptoms and cognitive patterns than whole-brain imaging measurements or an index of accelerated aging.

Analysis on spatial-temporal features of taxis' emissions from big data informed travel patterns: a case of Shanghai, China

2017 ◽  
Vol 142 ◽  
pp. 926-935 ◽  
Author(s):  
Xiao Luo ◽  
Liang Dong ◽  
Yi Dou ◽  
Ning Zhang ◽  
Jingzheng Ren ◽  
...  
Keyword(s):  
Big Data ◽  
Travel Patterns ◽  
Temporal Features ◽  
Data Informed

scholarly journals Risk assessment of sea ice disasters on fixed jacket platforms in the Liaodong Bay

2018 ◽  
Author(s):  
Ning Xu ◽  
Shuai Yuan ◽  
Xueqin Liu ◽  
Yuxian Ma ◽  
Wenqi Shi ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Sea Ice ◽  
Bohai Sea ◽  
Vulnerability Index ◽  
Structural Vulnerability ◽  
Liaodong Bay ◽  
The Bohai Sea ◽  
Oil Platforms ◽  
Force Mode ◽  
Ice Force

Abstract. Sea ice disasters seriously threaten the safety of oil platforms in the Bohai Sea. Therefore, it is necessary to carry out the risk assessment of sea ice disasters on oil platforms in the Bohai Sea. In the study, the risk assessment of sea ice disasters on fixed jacket platforms in the Liaodong Bay was performed. Firstly, the formation mechanisms of sea ice disasters were analyzed and the sources and modes of sea ice risks were clarified. Secondly, according to the calculation formulas of extreme ice force, dynamic ice force and accumulated force, several ice indices such as thickness, motion, strength, period, and concentration were proposed as the hazard indices and corresponding values were assigned to the proposed indices based on ice conditions in the Bohai Sea. Thirdly, based on four structural failure modes (structures overturned by the extreme ice force (Mode 1), structural fracture failure caused by dynamic ice force (Mode 2), facility damage caused by the dynamic ice force (Mode 3), and structural function failure caused by accumulated ice (Mode 4)), the structural vulnerability index, overturning index, dynamic index, ice-induced vibration index, and function index were proposed and corresponding values were assigned to the structural vulnerability index of fixed jacket platforms in the Liaodong Bay. Fourthly, the weight of each risk index was determined according to previous sea ice disasters and accidents and the sea ice risk was calculated with the weighted synthetic index method. Finally, with the above index system and risk assessment methods, the risk assessment of sea ice disasters on 10 jacket platforms in three sea areas in the Liaodong Bay was carried out. The analysis results showed that efficient sea ice prevention strategies could largely mitigate the sea ice-induced vibration-related risks of jacket platforms in the Liaodong Bay. If steady-state vibration occurred (usually in front of the vertical legged structure) or the structural fundamental frequency was high, the structural vulnerability was significantly increased and the calculated risk levels were high. The sea ice risk assessment method can be applied in the design, operation, and management of other engineering structures in sea ice areas.

Research challenges for the use of big data in policy-making

2020 ◽  
Vol 14 (4) ◽  
pp. 593-604
Author(s):  
Francesco Mureddu ◽  
Juliane Schmeling ◽  
Eleni Kanellou
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Policy Making ◽  
Quantitative Research ◽  
Future Research ◽  
The European Union ◽  
Public Governance ◽  
Content Type ◽  
Research Challenges ◽  
Data Informed

Purpose This paper aims to present pertinent research challenges in the field of (big) data-informed policy-making based on the research, undertaken within the course of the European Union-funded project Big Policy Canvas. Technological advancements, especially in the past decade, have revolutionised the way that both every day and complex activities are conducted. It is, thus, expected that a particularly important actor such as the public sector, should constitute a successful disruption paradigm through the adoption of novel approaches and state-of-the-art information and communication technologies. Design The research challenges stem from a need, trend and asset assessment based on qualitative and quantitative research, as well as from the identification of gaps and external framework factors that hinder the rapid and effective uptake of data-driven policy-making approaches. Findings The current paper presents a set of research challenges categorised in six main clusters, namely, public governance framework, privacy, transparency, trust, data acquisition, cleaning and representativeness, data clustering, integration and fusion, modelling and analysis with big data and data visualisation. Originality/value The paper provides a holistic overview of the interdisciplinary research challenges in the field of data-informed policy-making at a glance and shall serve as a foundation for the discussion of future research directions in a broader scientific community. It, furthermore, underlines the necessity to overcome isolated scientific views and treatments because of a high complex multi-layered environment.

Structural vulnerability of New Zealand soils

Soil Research ◽  
1997 ◽  
Vol 35 (3) ◽  
pp. 461 ◽  
Author(s):  
A. E. Hewitt ◽  
T. G. Shepherd
Keyword(s):  
New Zealand ◽  
Management Practices ◽  
Clay Content ◽  
Vulnerability Index ◽  
Organic Carbon Content ◽  
Structural Vulnerability ◽  
Phosphate Retention ◽  
Mineral Soils ◽  
Physical Degradation ◽  
The Impact

Some New Zealand soils withstand intensive cultivation and support continuing high production and yet maintain essential soil physical qualities of infiltration, aggregation, and aeration. In other soils, essential soil qualities deteriorate rapidly under the impact of even moderately intensive management practices. Our objective was to estimate the inherent susceptibility of New Zealand soils to physical degradation by focusing on structural vulnerability. We took a deductive approach by reviewing the available information on the structural stability and physical degradation of New Zealand soils. We identified 4 soil attributes that are well represented in the national soils database and are most likely to control structural vulnerability: (i) stabilising short-range-order oxy-hydroxides of aluminium and iron as estimated by phosphate retention, (ii) total organic carbon content, (iii) clay content, and (iv) wetness. The 4 attributes were standardised and transformed and a simple structural vulnerability index (SV) was devised. We determined SV for all mineral soils in the national soils database. The results provide a ranking of soil groups according to their structural vulnerability. We concluded that the index may be used as a first approximation rating of the structural vulnerability of New Zealand soils to aid resource management.

scholarly journals Measuring Structural Economic Vulnerability in Africa

2014 ◽  
pp. 406-426
Author(s):  
Patrick Guillaumont
Keyword(s):  
Climate Change ◽  
Developing Economies ◽  
Vulnerability Index ◽  
Fragile States ◽  
Structural Vulnerability ◽  
Methodological Issues ◽  
Physical Vulnerability ◽  
State Fragility ◽  
Economic Vulnerability ◽  
Vulnerability To Climate Change

This chapter examines the structural vulnerability of Africa’s economy and the methodological issues involved in measuring it. It begins by proposing a conceptual framework for measuring economic structural vulnerability that distinguishes it from general vulnerability, from physical vulnerability to climate change, and from state fragility. It then considers the main features and evolution of structural economic vulnerability in Africa using an economic vulnerability index. It suggests that structural economic vulnerability is higher in the continent than in other developing economies, reinforced by physical vulnerability to climate change. In addition, Africa has the highest proportion of fragile states among all continents. Finally, the chapter indicates that structural vulnerability, if adequately measured, may be useful as a criterion for the international allocation of official development assistance and of concessional resources.

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