The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Pekka Martikainen - One of the best experts on this subject based on the ideXlab platform.
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using Age Difference and sex similarity to detect evidence of sibling influence on criminal offending
Psychological Medicine, 2020Co-Authors: Janne Mikkonen, Jukka Savolainen, Mikko Aaltonen, Pekka MartikainenAbstract:BACKGROUND Sibling resemblance in crime may be due to genetic relatedness, shared environment, and/or the interpersonal influence of siblings on each other. This latter process can be understood as a type of 'peer effect' in that it is based on social learning between individuals occupying the same status in the social system (family). Building on prior research, we hypothesized that sibling pairs that resemble peer relationships the most, i.e., same-sex siblings close in Age, exhibit the most sibling resemblance in crime. METHODS Drawing on administrative microdata covering Finnish children born in 1985-97, we examined 213 911 sibling pairs, observing the recorded criminality of each sibling between Ages 11 and 20. We estimated multivariate regression models controlling for individual and family characteristics, and employed fixed-effects models to analyze the temporal co-occurrence of sibling delinquency. RESULTS Among younger siblings with a criminal older sibling, the adjusted prevalence estimates of criminal offending decreased from 32 to 25% as the Age Differences increased from less than 13 months to 25-28 months. The prevalence leveled off at 23% when Age Difference reached 37-40 months or more. These effects were statistically significant only among same-sex sibling pairs (p < 0.001), with clear evidence of contemporaneous offending among siblings with minimal Age Difference. CONCLUSIONS Same-sex siblings very close in Age stand out as having the highest sibling resemblance in crime. This finding suggests that a meaningful share of sibling similarity in criminal offending is due to a process akin to peer influence, typically flowing from the older to the younger sibling.
Jiaqian Liu - One of the best experts on this subject based on the ideXlab platform.
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integrated analysis of ischemic stroke datasets revealed sex and Age Difference in anti stroke targets
PeerJ, 2016Co-Authors: Shaoxing Dai, Qian Wang, Yicheng Guo, Yi Hong, Junjuan Zheng, Jiaqian LiuAbstract:Ischemic stroke is a common neurological disorder and the burden in the world is growing. This study aims to explore the effect of sex and Age Difference on ischemic stroke using integrated microarray datasets. The results showed a dramatic Difference in whole gene expression profiles and influenced pathways between males and females, and also in the old and young individuals. Furthermore, compared with old males, old female patients showed more serious biological function damAge. However, females showed less affected pathways than males in young subjects. Functional interaction networks showed these differential expression genes were mostly related to immune and inflammation-related functions. In addition, we found ARG1 and MMP9 were up-regulated in total and all subgroups. Importantly, IL1A, ILAB, IL6 and TNF and other anti-stroke target genes were up-regulated in males. However, these anti-stroke target genes showed low expression in females. This study found huge sex and Age Differences in ischemic stroke especially the opposite expression of anti-stroke target genes. Future studies are needed to uncover these pathological mechanisms, and to take appropriate pre-prevention, treatment and rehabilitation measures.
Derya D. Emek-savaş - One of the best experts on this subject based on the ideXlab platform.
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Brain-predicted Age Difference score is related to specific cognitive functions: a multi-site replication analysis
Brain Imaging and Behavior, 2020Co-Authors: Rory Boyle, Laura M. Rueda-delgado, Rossella Rizzo, Jason P. Mcmorrow, Silvin P. Knight, Lee Jollans, Görsev G. Yener, Ian H Robertson, Daniel Carey, Derya D. Emek-savaşAbstract:Brain-predicted Age Difference scores are calculated by subtracting chronological Age from ‘brain’ Age, which is estimated using neuroimaging data. Positive scores reflect accelerated Ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted Age Difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological Age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological Age in each dataset: Dokuz Eylül University ( n = 175), the Cognitive Reserve/Reference Ability Neural Network study ( n = 380), and The Irish Longitudinal Study on Ageing ( n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted Age Difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted Age Differences and reduced cognitive function in some domains that are implicated in cognitive Ageing.
Shaoxing Dai - One of the best experts on this subject based on the ideXlab platform.
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integrated analysis of ischemic stroke datasets revealed sex and Age Difference in anti stroke targets
PeerJ, 2016Co-Authors: Shaoxing Dai, Qian Wang, Yicheng Guo, Yi Hong, Junjuan Zheng, Jiaqian LiuAbstract:Ischemic stroke is a common neurological disorder and the burden in the world is growing. This study aims to explore the effect of sex and Age Difference on ischemic stroke using integrated microarray datasets. The results showed a dramatic Difference in whole gene expression profiles and influenced pathways between males and females, and also in the old and young individuals. Furthermore, compared with old males, old female patients showed more serious biological function damAge. However, females showed less affected pathways than males in young subjects. Functional interaction networks showed these differential expression genes were mostly related to immune and inflammation-related functions. In addition, we found ARG1 and MMP9 were up-regulated in total and all subgroups. Importantly, IL1A, ILAB, IL6 and TNF and other anti-stroke target genes were up-regulated in males. However, these anti-stroke target genes showed low expression in females. This study found huge sex and Age Differences in ischemic stroke especially the opposite expression of anti-stroke target genes. Future studies are needed to uncover these pathological mechanisms, and to take appropriate pre-prevention, treatment and rehabilitation measures.
Rory Boyle - One of the best experts on this subject based on the ideXlab platform.
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Brain-predicted Age Difference score is related to specific cognitive functions: a multi-site replication analysis
Brain Imaging and Behavior, 2020Co-Authors: Rory Boyle, Laura M. Rueda-delgado, Rossella Rizzo, Jason P. Mcmorrow, Silvin P. Knight, Lee Jollans, Görsev G. Yener, Ian H Robertson, Daniel Carey, Derya D. Emek-savaşAbstract:Brain-predicted Age Difference scores are calculated by subtracting chronological Age from ‘brain’ Age, which is estimated using neuroimaging data. Positive scores reflect accelerated Ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted Age Difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological Age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological Age in each dataset: Dokuz Eylül University ( n = 175), the Cognitive Reserve/Reference Ability Neural Network study ( n = 380), and The Irish Longitudinal Study on Ageing ( n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted Age Difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted Age Differences and reduced cognitive function in some domains that are implicated in cognitive Ageing.
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brain predicted Age Difference score is related to specific cognitive functions a multi site replication analysis
bioRxiv, 2019Co-Authors: Rory Boyle, Rossella Rizzo, Jason P. Mcmorrow, Silvin P. Knight, Laura M Ruedadelgado, Lee Jollans, Görsev G. Yener, Ian H Robertson, Daniel Carey, Derya Durusu EmeksavasAbstract:Abstract Brain-predicted Age Difference scores are calculated by subtracting chronological Age from ‘brain’ Age. Positive scores reflect accelerated Ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted Age Difference scores and specific cognitive functions has not been systematically examined. First, applying machine learning to 1,359 T1-weighted MRI scans, we predicted the relationship between chronological Age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological Age: Dokuz Eylul University (n=175), the Cognitive Reserve/Reference Ability Neural Network study (n=380), and The Irish Longitudinal Study on Ageing (n=487). Each independent dataset had rich neuropsychological data. Brain-predicted Age Difference scores were significantly negatively correlated with general cognitive status (two datasets); processing speed, visual attention, cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). They were not significantly correlated with processing speed, cognitive flexibility, response inhibition and selective attention, sustained attention, verbal episodic memory or working memory in any dataset. As such, there is firm evidence of correlations between increased brain-predicted Age Differences and reduced cognitive function only in some domains that are implicated in cognitive Ageing.
