Serial Correlations in Single-Subject fMRI with Sub-Second TR

When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences

Soluble Fermentable Dietary Fibre (Pectin) Decreases Caloric Intake, Adiposity and Lipidaemia in High-Fat Diet-Induced Obese Rats

Funding: This work was funded by the Scottish Government Rural and Environment Science and Analytical Services Division. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Mixed Quantum/Classical Calculations of Total and Differential Elastic and Rotationally Inelastic Scattering Cross Sections for Light and Heavy Reduced Masses in a Broad Range of Collision Energies

The mixed quantum/classical theory (MQCT) for rotationally inelastic scattering developed recently [A. Semenov and D. Babikov, J. Chem. Phys.139, 174108 (2013)] is benchmarked against the full quantum calculations for two molecular systems: He + H2 and Na + N2. This allows testing new method in the cases of light and reasonably heavy reduced masses, for small and large rotational quanta, in a broad range of collision energies and rotational excitations. The resultant collision cross sections vary through ten-orders of magnitude range of values. Both inelastic and elastic channels are considered, as well as differential (over scattering angle) cross sections. In many cases results of the mixed quantum/classical method are hard to distinguish from the full quantum results. In less favorable cases (light masses, larger quanta, and small collision energies) some deviations are observed but, even in the worst cases, they are within 25% or so. The method is computationally cheap and particularly accurate at higher energies, heavier masses, and larger densities of states. At these conditions MQCT represents a useful alternative to the standard full-quantum scattering theory

Dose-dependent effects of a soluble dietary fibre (pectin) on food intake, adiposity, gut hypertrophy and gut satiety hormone secretion in rats

Acknowledgments We thank Donna Wallace and Animal House staff at the Rowett Institute of Nutrition and Health for the daily care of experimental rats and for the body weight, food intake and MRI measurements.Peer reviewedPublisher PD

Evaluating The Effect Of Alternative Carbon Allocation Schemes In A Land Surface Model (Clm4.5) On Carbon Fluxes, Pools And Turnover In Temperate Forests

How carbon (C) is allocated to different plant tissues (leaves, stem and roots) determines C residence time and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and Leaf Area Index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a Land Surface Model (LSM), the Community Land Model (CLM4.5). We ran CLM for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes: i) Dynamic C allocation scheme (named D-CLM ) with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual Net Primary Production (NPP). ii) An alternative dynamic C allocation scheme (named D-Litton ), where, similar to (i) C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem and coarse roots iii–iv) Two fixed C allocation schemes, one representative of observations in evergreen (named F-Evergreen ) and the other of observations in deciduous forests (named F-Deciduous ). D-CLM generally overestimated Gross Primary Production (GPP) and ecosystem respiration, and underestimated Net Ecosystem Exchange (NEE). In D-CLM, initial aboveground biomass in 1980 was largely overestimated (between 10527 and 12897 g Cm-2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g Cm-2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM overestimated LAI in both evergreen and deciduous sites because the leaf C-LAI relationship in the model did not match the observed leaf C-LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic Cstem/Cleaf ratios and strongly reduced the overestimation of initial aboveground biomass, and aboveground NPP for deciduous forests by D-CLM. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. That could be done by addressing some of the current model assumptions about C allocation and the associated parameter uncertainty. Our results highlight the importance of using aboveground biomass data to evaluate and constrain the C allocation scheme in the model, and in particular, the sensitivity to the stem turnover rate. Revising these will be critical to improving long-term C processes in LSMs, and improve their projections of biomass accumulation in forests

Different types of soluble fermentable dietary fibre decrease food intake, body weight gain and adiposity in young adult male rats

We thank Donna Wallace and the Rowett Animal House staff for the daily care of experimental rats, body weight and food intake measurements and MRI scanning, Vivien Buchan and Donna Henderson of the Rowett Analytical Department for proximate analyses and SCFA GC, and Andrew Chappell for conducting the beta-glucan analysis. This research was funded by the Scottish Government’s Rural and Environment Science and Analytical Services Division.Peer reviewedPublisher PD

LiDAR patch metrics for object-based clustering of forest types in a tropical rainforest

Tropical rainforests support a large proportion of the Earth’s plant and animal species within a restricted global distribution, and play an important role in regulating the Earth’s climate. However, the existing knowledge of forest types or habitats is relatively poor and there are large uncertainties in the quantification of carbon stock in these forests. Airborne Laser Scanning, using LiDAR, has advantages over other remote sensing techniques for describing the three-dimensional structure of forests. With respect to the habitat requirements of different species, forest structure can be defined by canopy height, canopy cover and vertical arrangement of biomass. In this study, forest patches were identified based on classification and hierarchical merging of a LiDAR-derived Canopy Height Model in a tropical rainforest in Sumatra, Indonesia. Attributes of the identified patches were used as inputs for k-medoids clustering. The clusters were then analysed by comparing them with identified forest types in the field. There was a significant association between the clusters and the forest types identified in the field, to which arang forests and mixed agro-forests contributed the most. The topographic attributes of the clusters were analysed to determine whether the structural classes, and potentially forest types, were related to topography. The tallest clusters occurred at significantly higher elevations (> 850 m) and steeper slopes (> 26°) than the other clusters. These are likely to be remnants of undisturbed primary forests and are important for conservation and habitat studies and for carbon stock estimation. This study showed that LiDAR data can be used to map tropical forest types based on structure, but that structural similarities between patches of different floristic composition or human use histories can limit habitat separability as determined in the field

Gland segmentation in gastric histology images: detection of intestinal metaplasia

Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric intestinal metaplasia (IM) of the mucosa of the stomach has been found to increase the risk of gastric cancer and is considered as one of the precancerous lesions. Therefore, early detection of IM may have a valuable role in histopathological risk assessment regarding the possibility of progression to cancer. Accurate segmentation and analysis of gastric glands from the histological images plays an important role in the diagnostic confirmation of IM. Thus, in this paper, we propose a framework for segmentation of gastric glands and detection of IM. More specifically, we propose the GAGL-Net for the segmentation of glands. Then, based on two features of the extracted glands we classify the tissues into normal and IM cases. The results showed that the proposed gland segmentation approach achieves an F1 score equal to 0.914. Furthermore, the proposed methodology shows great potential for the IM detection achieving an accuracy score equal to 96.6%. To evaluate the efficiency of the proposed methodology we used a publicly available dataset and we created the GAGL dataset consisting of 59 Whole Slide Images (WSI) including both IM and normal cases

Right ventricular function is a predictor for sustained ventricular tachycardia requiring anti-tachycardic pacing in arrhythmogenic ventricular cardiomyopathy: insight into transvenous vs. subcutaneous implantable cardioverter defibrillator insertion

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) patients develop ventricular arrhythmias (VAs) responsive to anti-tachycardia pacing (ATP). However, VA episodes have not been characterized in accordance with the device therapy, and with the emergence of the subcutaneous implantable cardioverter defibrillator (S-ICD), the appropriate device prescription in ARVC remains unclear. Study aim was to characterize VA events in ARVC patients during follow-up in accordance with device therapy and elicit if certain parameters are predictive of specific VA events. METHODS AND RESULTS: This was a retrospective single-centre study utilizing prospectively collated registry data of ARVC patients with ICDs. Forty-six patients were included [54.0 ± 12.1 years old and 20 (43.5%) secondary prevention devices]. During a follow-up of 12.1 ± 6.9 years, 31 (67.4%) patients had VA events [n = 2, 6.5% ventricular fibrillation (VF), n = 14], 45.2% VT falling in VF zone resulting in ICD shock(s), n = 10, 32.3% VT resulting in ATP, and n = 5, 16.1% patients had both VT resulting in ATP and ICD shock(s). Lead failure rates were high (11/46, 23.9%). ATP was successful in 34.5% of patients. Severely impaired right ventricular (RV) function was an independent predictor of VT resulting in ATP (hazard ratio 16.80, 95% confidence interval 3.74–75.2; P < 0.001) with a high predictive accuracy (area under the curve 0.88, 95%CI 0.76–1.00; P < 0.001). CONCLUSION: VA event rates are high in ARVC patients with a majority having VT falling in the VF zone resulting in ICD shock(s). S-ICDs could be of benefit in most patients with ARVC with the absence of severely impaired RV function which has the potential to avoid consequences of the high burden of lead failure