Ecosystem complexity on the Kerguelen Axis: the need for integrated ecosystem studies and sustained coordinated monitoring

第6回極域科学シンポジウム[OB] 極域生物圏11月16日（月）　統計数理研究所 セミナー室1（D305

Exploring Southern Ocean ecosystem change through the use of a statistical sea ice emulator

第3回極域科学シンポジウム/第34回極域生物シンポジウム 11月26日（月） 統計数理研究所 ３階セミナー

Correction to: A note on diffusion limits of chaotic skew-product flows

Abstract This fixes a gap in the averaging argument in our paper: A note on diffusion limits of chaotic skew product flows. Nonlinearity (2011) 1361-1367, and moreover shows that the large deviation estimate assumed there is redundant. , but the proof is incorrect. Specifically, the proof introduces a random variable J n (see below) that depends on x ( ) (n 3/2 ) and y (1) (s), and derives an estimate for E|J n |. This estimate takes into account the randomness of y (1) (s) but overlooks the randomness of x ( ) (n 3/2 ). In this note, we correct the argument in Proof Following the calculation in [2, Section 3] with δ = 3/2 , we obtain ma

Brain volume estimation from post-mortem newborn and fetal MRI

AbstractObjectiveMinimally invasive autopsy using post-mortem magnetic resonance imaging (MRI) is a valid alternative to conventional autopsy in fetuses and infants. Estimation of brain weight is an integral part of autopsy, but manual segmentation of organ volumes on MRI is labor intensive and prone to errors, therefore unsuitable for routine clinical practice. In this paper we aim to show that volumetric measurements of the post-mortem fetal and neonatal brain can be accurately estimated using semi-automatic techniques and a high correlation can be found with the weights measured from conventional autopsy results.MethodsThe brains of 17 newborn subjects, part of Magnetic Resonance Imaging Autopsy Study (MaRIAS), were segmented from post-mortem MR images into cerebrum, cerebellum and brainstem using a publicly available neonate brain atlas and semi-automatic segmentation algorithm. The results of the segmentation were averaged to create a new atlas, which was then used for the automated atlas-based segmentation of 17 MaRIAS fetus subjects. As validation, we manually segmented the MR images from 8 subjects of each cohort and compared them with the automatic ones. The semi-automatic estimation of cerebrum weight was compared with the results of the conventional autopsy.ResultsThe Dice overlaps between the manual and automatic segmentations are 0.991 and 0.992 for cerebrum, 0.873 and 0.888 for cerebellum and 0.819 and 0.815 for brainstem, for newborns and fetuses, respectively. Excellent agreement was obtained between the estimated MR weights and autopsy gold standard ones: mean absolute difference of 5 g and 2% maximum error for the fetus cohort and mean absolute difference of 20 g and 11% maximum error for the newborn one.ConclusionsThe high correlation between the obtained segmentation and autopsy weights strengthens the idea of using post-mortem MRI as an alternative for conventional autopsy of the brain

Optimisation of arterial spin labelling using bayesian experimental design

Large-scale neuroimaging studies often use multiple individual imaging contrasts. Due to the finite time available for imaging,there is intense competition for the time allocated to the individual modalities; thus it is crucial to maximise the utility of each method given the resources available. Arterial Spin Labelled (ASL) MRI often forms part of such studies. Measuring perfusion of oxygenated blood in the brain is valuable for several diseases,but quantification using multiple inversion time ASL is time-consuming due to poor SNR and consequently slow acquisitions. Here,we apply Bayesian principles of experimental design to clinical-length ASL acquisitions,resulting in significant improvements to perfusion estimation. Using simulations and experimental data,we validate this approach for a five-minute ASL scan. Our design procedure can be constrained to any chosen scan duration,making it well-suited to improve a variety of ASL implementations. The potential for adaptation to other modalities makes this an attractive method for optimising acquisition in the time-pressured environment of neuroimaging studies

A review of feto-placental vasculature flow modelling

The placenta provides the vital nutrients and removal of waste products required for fetal growth and development. Understanding and quantifying the differences in structure and function between a normally functioning placenta compared to an abnormal placenta is vital to provide insights into the aetiology and treatment options for fetal growth restriction and other placental disorders. Computational modelling of blood flow in the placenta allows a new understanding of the placental circulation to be obtained. This structured review discusses multiple recent methods for placental vascular model development including analysis of the appearance of the placental vasculature and how placental haemodynamics may be simulated at multiple length scales

ASL-incorporated pharmacokinetic modelling of PET data with reduced acquisition time: Application to amyloid imaging

Pharmacokinetic analysis of Positron Emission Tomography (PET) data typically requires at least one hour of image acquisition, which poses a great disadvantage in clinical practice. In this work, we propose a novel approach for pharmacokinetic modelling with significantly reduced PET acquisition time, by incorporating the blood flow information from simultaneously acquired arterial spin labelling (ASL) magnetic resonance imaging (MRI). A relationship is established between blood flow, measured by ASL, and the transfer rate constant from plasma to tissue of the PET tracer, leading to modified PET kinetic models with ASL-derived flow information. Evaluation on clinical amyloid imaging data from an Alzheimer’s disease (AD) study shows that the proposed approach with the simplified reference tissue model can achieve amyloid burden estimation from 30 min [18F]florbetapir PET data and 5 min simultaneous ASL MR data, which is comparable with the estimation from 60 min PET data (mean error=−0.03). Conversely, standardised uptake value ratio (SUVR), the alternative measure from the data showed a positive bias in areas of higher amyloid burden (mean error=0.07)