A new hammer to crack an old nut : interspecific competitive resource capture by plants is regulated by nutrient supply, not climate

Peer reviewedPublisher PD

Psychological rumination and recovery from work in Intensive Care Professionals : associations with stress, burnout, depression, and health

Background The work demands of critical care can be a major cause of stress in intensive care unit (ICU) professionals and lead to poor health outcomes. In the process of recovery from work, psychological rumination is considered to be an important mediating variable in the relationship between work demands and health outcomes. This study aimed to extend our knowledge of the process by which ICU stressors and differing rumination styles are associated with burnout, depression and risk of psychiatric morbidity among ICU professionals. Methods Ninety-six healthcare professionals (58 doctors and 38 nurses) who work in ICUs in the UK completed a questionnaire on ICU-related stressors, burnout, work-related rumination, depression and risk of psychiatric morbidity. Results Significant associations between ICU stressors, affective rumination, burnout, depression and risk of psychiatric morbidity were found. Longer working hours were also related to increased ICU stressors. Affective rumination (but not problem-solving pondering or distraction detachment) mediated the relationship between ICU stressors, burnout, depression and risk of psychiatric morbidity, such that increased ICU stressors, and greater affective rumination, were associated with greater burnout, depression and risk of psychiatric morbidity. No moderating effects were observed. Conclusions Longer working hours were associated with increased ICU stressors, and increased ICU stressors conferred greater burnout, depression and risk of psychiatric morbidity via increased affective rumination. The importance of screening healthcare practitioners within intensive care for depression, burnout and psychiatric morbidity has been highlighted. Future research should evaluate psychological interventions which target rumination style and could be made available to those at highest risk. The efficacy and cost effectiveness of delivering these interventions should also be considered

Correction: Exome Sequencing in an Admixed Isolated Population IndicatesNFXL1 Variants Confer a Risk for Specific Language Impairment

Children affected by Specific Language Impairment (SLI) fail to acquire age appropriate language skills despite adequate intelligence and opportunity. SLI is highly heritable, but the understanding of underlying genetic mechanisms has proved challenging. In this study, we use molecular genetic techniques to investigate an admixed isolated founder population from the Robinson Crusoe Island (Chile), who are affected by a high incidence of SLI, increasing the power to discover contributory genetic factors. We utilize exome sequencing in selected individuals from this population to identify eight coding variants that are of putative significance. We then apply association analyses across the wider population to highlight a single rare coding variant (rs144169475, Minor Allele Frequency of 4.1% in admixed South American populations) in the NFXL1 gene that confers a nonsynonymous change (N150K) and is significantly associated with language impairment in the Robinson Crusoe population (p = 2.04 × 10–4, 8 variants tested). Subsequent sequencing of NFXL1 in 117 UK SLI cases identified four individuals with heterozygous variants predicted to be of functional consequence. We conclude that coding variants within NFXL1 confer an increased risk of SLI within a complex genetic model

Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation

Patient movement in emission tomography deteriorates reconstruction quality because of motion blur. Gating the data improves the situation somewhat: each gate contains a movement phase which is approximately stationary. A standard method is to use only the data from a few gates, with little movement between them. However, the corresponding loss of data entails an increase of noise. Motion correction algorithms have been implemented to take into account all the gated data, but they do not scale well, especially not in 3D. We propose a novel motion correction algorithm which addresses the scalability issue. Our approach is to combine an enhanced ML-EM algorithm with deep learning based movement registration. The training is unsupervised, and with artificial data. We expect this approach to scale very well to higher resolutions and to 3D, as the overall cost of our algorithm is only marginally greater than that of a standard ML-EM algorithm. We show that we can significantly decrease the noise corresponding to a limited number of gates

An Automated and High Precision Quantitative Analysis of the ACR Phantom

A novel phantom-imaging platform for automated and high precision imaging of the American College of Radiology (ACR) PET phantom is proposed. The platform facilitates the generation of an accurate μ-map for PET/MR systems with a robust alignment based on two-stage image registration using specifically designed PET templates. The automated analysis of PET images uses a set of granular composite volume of interest (VOI) templates in a 0.5 mm resolution grid for sampling of the system response to the insert step functions. The impact of the activity outside the field of view (FOV) was evaluated using two acquisitions of 30 minutes each, with and without the activity outside the FOV. Iterative image reconstruction was employed with and without modelled shift-invariant point spread function (PSF) and varying ordered subsets expectation maximisation (OSEM) iterations. Uncertainty analysis of all image-derived statistics was performed using bootstrap resampling of the list-mode data. We found that the activity outside the FOV can adversely affect the imaging planes close to the edge of the axial FOV, reducing the contrast, background uniformity and overall quantitative accuracy. The PSF had a positive impact on contrast recovery (although it slows convergence). The proposed platform may be helpful in a more informative evaluation of PET systems and image reconstruction methods

Partial volume correction strategies for quantitative FDG PET in oncology

Purpose: Quantitative accuracy of positron emission tomography (PET) is affected by partial volume effects resulting in increased underestimation of the standardized uptake value (SUV) with decreasing tumour volume. The purpose of the present study was to assess accuracy and precision of different partial volume correction (PVC) methods. Methods: Three methods for PVC were evaluated: (1) inclusion of the point spread function (PSF) within the reconstruction, (2) iterative deconvolution of PET images and (3) calculation of spill-in and spill-out factors based on tumour masks. Simulations were based on a mathematical phantom with tumours of different sizes and shapes. Phantom experiments were performed in 2-D mode using the National Electrical Manufacturers Association (NEMA) NU2 image quality phantom containing six differently sized spheres. Clinical studies (2-D mode) included a test-retest study consisting of 10 patients with stage IIIB and IV non-small cell lung cancer and a response monitoring study consisting of 15 female breast cancer patients. In all studies tumour or sphere volumes of interest (VOI) were generated using VOI based on adaptive relative thresholds. Results: Simulations and experiments provided similar results. All methods were able to accurately recover true SUV within 10% for spheres equal to and larger than 1 ml. Reconstruction-based recovery, however, provided up to twofold better precision than image-based methods. Cl