170 research outputs found
The effect of computation and feedback delay on the capacity of multiuser MIMO systems in a small outdoor cell
Propagation characteristics, metrics, and statistics for virtual MIMO performance in a measured outdoor cell
Evaluation of the capacity of multiple-access MIMO schemes with feedback in a small outdoor cell
TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data
The TADPOLE Challenge compares the performance of algorithms at predicting
the future evolution of individuals at risk of Alzheimer's disease. TADPOLE
Challenge participants train their models and algorithms on historical data
from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants
are then required to make forecasts of three key outcomes for ADNI-3 rollover
participants: clinical diagnosis, ADAS-Cog 13, and total volume of the
ventricles -- which are then compared with future measurements. Strong points
of the challenge are that the test data did not exist at the time of
forecasting (it was acquired afterwards), and that it focuses on the
challenging problem of cohort selection for clinical trials by identifying fast
progressors. The submission phase of TADPOLE was open until 15 November 2017;
since then data has been acquired until April 2019 from 219 subjects with 223
clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used
for the evaluation of the participants' predictions. Thirty-three teams
participated with a total of 92 submissions. No single submission was best at
predicting all three outcomes. For diagnosis prediction, the best forecast
(team Frog), which was based on gradient boosting, obtained a multiclass area
under the receiver-operating curve (MAUC) of 0.931, while for ventricle
prediction the best forecast (team EMC1), which was based on disease
progression modelling and spline regression, obtained mean absolute error of
0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was
considerably better than the benchmark mixed effects model (BenchmarkME),
provided to participants before the submission deadline. Further analysis can
help understand which input features and algorithms are most suitable for
Alzheimer's disease prediction and for aiding patient stratification in
clinical trials.Comment: 10 pages, 1 figure, 4 tables. arXiv admin note: substantial text
overlap with arXiv:1805.0390
Serotonin tranporter methylation and response to cognitive behaviour therapy in children with anxiety disorders
Anxiety disorders that are the most commonly occurring psychiatric disorders in childhood, are associated with a range of social and educational impairments and often continue into adulthood. Cognitive behaviour therapy (CBT) is an effective treatment option for the majority of cases, although up to 35-45% of children do not achieve remission. Recent research suggests that some genetic variants may be associated with a more beneficial response to psychological therapy. Epigenetic mechanisms such as DNA methylation work at the interface between genetic and environmental influences. Furthermore, epigenetic alterations at the serotonin transporter (SERT) promoter region have been associated with environmental influences such as stressful life experiences. In this study, we measured DNA methylation upstream of SERT in 116 children with an anxiety disorder, before and after receiving CBT. Change during treatment in percentage DNA methylation was significantly different in treatment responders vs nonresponders. This effect was driven by one CpG site in particular, at which responders increased in methylation, whereas nonresponders showed a decrease in DNA methylation. This is the first study to demonstrate differences in SERT methylation change in association with response to a purely psychological therapy. These findings confirm that biological changes occur alongside changes in symptomatology following a psychological therapy such as CBT
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