Towards 1H-MRSI of the human brain at 7T with slice-selective adiabatic refocusing pulses

Contains fulltext : 70576.pdf (publisher's version ) (Closed access)OBJECTIVE: To explore the possibilities of proton spectroscopic imaging (1H-MRSI) of the human brain at 7 Tesla with adiabatic refocusing pulses. MATERIALS AND METHODS: A combination of conventional slice selective excitation and two pairs of slice selective adiabatic refocusing pulses (semi-LASER) results in the formation of an echo from a localized volume. Depending on the used radio frequency (rf) coil efficiency and available rf power, the duration of the adiabatic full passage pulses (AFPs) is adapted to enable echo times down to 50 ms (head coil) or 30 ms (local surface coil). RESULTS: An AFP duration of 5 ms with a corresponding bandwidth of 5.1 kHz resulted in a chemical shift displacement error of 23% over 3.8 ppm at 7T. Using a local surface coil and an echo time down to 30 ms, we detected not only the three main metabolites (NAA, Cr and Cho), but also coupled signals from myo-inositol and glutamate/glutamine in spectra from 0.14 cc voxels with linewidths down to 10 Hz in 10 min measurement time. CONCLUSIONS: The semi-LASER pulse sequence enables 1H-MRSI of the human brain at 7T for larger parts of the brain as well as small localized areas with both a high spectral and spatial resolution

Psychological determinants of whole-body endurance performance

Background: No literature reviews have systematically identified and evaluated research on the psychological determinants of endurance performance, and sport psychology performance-enhancement guidelines for endurance sports are not founded on a systematic appraisal of endurance-specific research. Objective: A systematic literature review was conducted to identify practical psychological interventions that improve endurance performance and to identify additional psychological factors that affect endurance performance. Additional objectives were to evaluate the research practices of included studies, to suggest theoretical and applied implications, and to guide future research. Methods: Electronic databases, forward-citation searches, and manual searches of reference lists were used to locate relevant studies. Peer-reviewed studies were included when they chose an experimental or quasi-experimental research design, a psychological manipulation, endurance performance as the dependent variable, and athletes or physically-active, healthy adults as participants. Results: Consistent support was found for using imagery, self-talk, and goal setting to improve endurance performance, but it is unclear whether learning multiple psychological skills is more beneficial than learning one psychological skill. The results also demonstrated that mental fatigue undermines endurance performance, and verbal encouragement and head-to-head competition can have a beneficial effect. Interventions that influenced perception of effort consistently affected endurance performance. Conclusions: Psychological skills training could benefit an endurance athlete. Researchers are encouraged to compare different practical psychological interventions, to examine the effects of these interventions for athletes in competition, and to include a placebo control condition or an alternative control treatment. Researchers are also encouraged to explore additional psychological factors that could have a negative effect on endurance performance. Future research should include psychological mediating variables and moderating variables. Implications for theoretical explanations of endurance performance and evidence-based practice are described

EMT-Induced Stemness and Tumorigenicity Are Fueled by the EGFR/Ras Pathway

10.1371/journal.pone.0070427PLoS ONE88-POLN

CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation

Quantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC >0.97 on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer)

A Bayesian adaptive design for biomarker trials with linked treatments.

BACKGROUND: Response to treatments is highly heterogeneous in cancer. Increased availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups. METHODS: We propose a novel Bayesian adaptive randomisation (BAR) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment's effect. The design is motivated in part by two phase II trials that are currently in development. The design starts by randomising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active. At interim analyses, data from treated patients are used to update the allocation probabilities. If the linked treatments are effective, the allocation remains high; if ineffective, the allocation changes over the course of the trial to unlinked treatments that are more effective. RESULTS: Our proposed design has high power to detect treatment effects if the pairings of treatment with biomarker are correct, but also performs well when alternative pairings are true. The design is consistently more powerful than parallel-groups stratified trials. CONCLUSIONS: This BAR design is a powerful approach to use when there are pairings of biomarkers with treatments available for testing simultaneously.This work was supported by the Medical Research Council (grant number G0800860) and the NIHR Cambridge Biomedical Research Centre.This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/bjc.2015.27

Macroeconomic impact of stranded fossil-fuel assets

Several major economies rely heavily on fossil-fuel production and exports, yet current low-carbon technology diffusion, energy efficiency and climate policy may be substantially reducing global demand for fossil fuels.1-4 This trend is inconsistent with observed investment in new fossil-fuel ventures1,2, which could become stranded as a result. Here we use an integrated global economy environment simulation model to study the macroeconomic impact of stranded fossil-fuel assets (SFFA). Our analysis suggests that part of the SFFA would occur as a result of an already ongoing technological trajectory, irrespective of whether new climate policies are adopted or not; the loss would be amplified if new climate policies to reach the 2°C target are adopted and/or if low-cost producers (some OPEC countries) maintain their level of production (‘sell-out’) despite declining demand; the magnitude of the loss from SFFA may amount to a discounted global wealth loss of $1-4tn; and there are clear distributional impacts, with winners (e.g. net importers such as China or the EU) and losers (e.g. Russia, the US or Canada, which could see their fossil-fuel industries nearly shut down), although the two effects would largely offset each other at the level of aggregate global GDP.The authors acknowledge C-EERNG and Cambridge Econometrics for support, and funding from EPSRC (JFM, fellowship no. EP/ K007254/1); the Newton Fund (JFM, PS, JV, EPSRC grant no EP/N002504/1 and ESRC grant no ES/N013174/1), NERC (NRE, PH, HP, grant no NE/P015093/1), CONICYT (PS), the Philomathia Foundation (JV), the Cambridge Humanities Research Grants Scheme (JV), and Horizon 2020 (HP, JFM; Sim4Nexus project)

Search for supersymmetric particles in scenarios with a gravitino LSP and stau NLSP

Sleptons, neutralinos and charginos were searched for in the context of scenarios where the lightest supersymmetric particle is the gravitino. It was assumed that the stau is the next-to-lightest supersymmetric particle. Data collected with the DELPHI detector at a centre-of-mass energy near 189 GeV were analysed combining the methods developed in previous searches at lower energies. No evidence for the production of these supersymmetric particles was found. Hence, limits were derived at 95% confidence level.Comment: 31 pages, 14 figure